RU2632134C2 - Method and system of processing of search requests - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: plenty of search requests entered into a search system are got in the method of processing search requests. Each search request is processed. The processing includes obtaining a set of lexemes and a set of synonyms, calculating a set of normalized requests based on search requests and lexemes, calculating a set of generalized requests based on normalized requests and synonyms, and compiling an index structure based on a set of logical trees. Each normalized request is associated with the corresponding generalized request. Compiling of the index structure includes computing a set of logical trees. Each logical tree is a general request which is associated with a generalized request.

EFFECT: reduction of calculating resources due to the fact that the problem of combinatorial explosion is resolved when processing long search requests.

22 cl, 9 dwg

Description

FIELD OF THE INVENTION

[01] The present technology relates to systems and methods for processing search queries entered into a search engine service. More specifically, systems and methods are aimed at calculating generalized queries based on search queries to provide content to a user.

BACKGROUND

[02] Keyword matching is a convenient way to deliver targeted content to users of search engine services. In some cases, such a match may provide more relevant search results to the user in response to an entered query in the search engine. In other cases, target content providers may provide information about the product and / or service that they advertise, search phrases that will be used to correlate the advertisement with the search query entered by the user, and prices offered by the search engine service provider for display your advertisements. For example, when a user enters a search query into a search engine, a matching procedure is performed between the search query and the search phrase provided by the advertiser and / or content provider, and the advertisement and / or search results relevant to the search query are selected by the search engine to provide to the user in question. However, it should be borne in mind that there may be semantic errors, grammatical errors, spelling errors, and so on, associated with the procedure for establishing compliance.

[03] For example, search queries entered by users may contain typos that users mistakenly enter at the time of entering search queries. In another example, a search query and a search phrase can be entered into a search engine in another language. In another embodiment, the search query may include insignificant words, for example, prepositions, which can complicate the process of establishing a correspondence between a given search query and a given search phrase.

[04] One of the problems encountered when matching search queries and search phrases relates to synonyms. Indeed, in some cases, if the search query includes a synonym for the word contained in the search phrase, this search query and this search phrase may not match. This discrepancy is undesirable for: (i) the user, as he may not receive an advertisement for the product of interest; (ii) the advertiser and / or content provider, as their product and / or service and / or content is not well advertised; and (iii) a search engine provider because it does not achieve the optimal monetization associated with advertisements / content that are advertised using the search results page (SERP).

[05] Some search engine providers may create synonymous phrases equivalent to search queries to try to match the synonymous phrases with the search phrases provided by the advertiser. The usual approach to creating synonymous phrases is as follows. A search query is considered a set of words for which a set of synonyms is selected, and then synonymous phrases are created as possible combinations of the synonyms in question. The number of such combinations is 2 ^N-1 , with N being the number of words in the search query. This means that longer searches lead to an exponential increase in combinations, also known as combinatorial bursts.

DISCLOSURE

[06] The present technical solution can reduce at least some of the disadvantages inherent in the current level of technology in relation to the processing of search queries.

[07] In some embodiments of the technical solution, there is provided a method for processing search queries that runs on a server and in which the server hosts a search engine. The method includes: receiving by the server a plurality of search queries entered into a search engine; and server processing of each search query in a plurality of search queries. Processing of each search request includes: obtaining a set of tokens and a set of synonyms by the server; calculation by the server of a set of normalized queries based on search queries and tokens from a set of tokens; computing a set of generalized queries by the server based on normalized queries from a set of normalized queries and synonyms from a set of synonyms, with each normalized query associated with a corresponding generalized query from a set of generalized queries; and compiling the index structure by the server based on the set of logical trees, wherein compiling the index structure includes computing the set of logical trees by the server. Each logical tree in a set of logical trees represents at least one generalized query associated with at least one corresponding normalized query.

[08] In some embodiments of the method, the method further includes: receiving, by the server, a search phrase entered into the search system; server processing of the search phrase. Processing of a search phrase includes: the server calculating a normalized search phrase based on a search phrase and tokens from a set of tokens; calculation by the server of a generalized search phrase based on a normalized search phrase and synonyms from a set of synonyms; and server linking at least one normalized query from the set of normalized queries to the search phrase based on the generalized search phrase and index structure, wherein at least one normalized query is different from the normalized search phrase.

[09] In some embodiments of the method, calculating a set of normalized queries includes, for each search query from a plurality of search queries: a server determining a corresponding token for each significant word in a given search query; creating the server of this normalized query associated with this search query, and this normalized query includes the corresponding token of each significant word in this search query.

[10] In some embodiments of the method, computing a set of generalized queries includes, for each normalized query from a set of normalized queries: determining, by the server, the corresponding basic synonym for each token in the given normalized query; creation by the server of this generalized request associated with this normalized request, and this generalized request includes the corresponding main synonym for each token in this normalized request.

[11] In some embodiments of the method, a given normalized query is associated with a normalized frequency counter that indicates a series of search queries from a plurality of search queries that are associated with this normalized query.

[12] In some embodiments of the method, this generalized query is associated with a generalized frequency counter that indicates a series of normalized queries from the set of normalized queries that are associated with this generalized query.

[13] In some embodiments of the method, each significant word is a modified paradigm of the corresponding token.

[14] In some embodiments of the method, the corresponding main synonym for each token is determined by the processing module based on the corresponding indications of the popularity of synonyms from the set of synonyms.

[15] In some embodiments of the method, at least one logical tree from a set of logical trees includes: a parent node; at least one child node of the first level and at least one child node of the second level.

[16] In some embodiments of the method, the at least one logical tree represents at least one corresponding generalized query associated with at least one corresponding normalized query, and at least one generalized query is associated with at least one of: parent a node, at least one child node of the first level and at least one child node of the second level.

[17] In some embodiments of the method, the method further includes: receiving, by the server, the current search query entered into the search engine; server processing the current search request. Processing the current search query includes: the server calculating a normalized current search query based on the current search query and tokens from the set of tokens; calculating by the server a generalized current search query based on the normalized search query and synonyms from the set of synonyms and the server linking the normalized current query with the target content associated with the search phrase based on at least one of the normalized search phrase and at least one normalized query related with a search phrase.

[18] In some embodiments of the technical solution, there is provided a server for processing search queries that hosts a search engine. The server is configured to: receive the server a plurality of search queries entered into the search system; and server processing of each search query in a plurality of search queries. To process each search request, the server is configured to: obtain a set of tokens and a set of synonyms; calculating a set of normalized queries based on search queries and tokens from a set of tokens; calculating a set of generalized queries based on normalized queries from a set of normalized queries and synonyms from a set of synonyms, wherein each normalized query is associated with a corresponding generalized query from a set of generalized queries; and compiling the index structure based on the set of logical trees, wherein compiling the index structure includes a server configured to calculate the set of logical trees, each logical tree from the set of logical trees representing at least one generalized query associated with at least one appropriate normalized query.

[19] In some embodiments of the server, the server is further configured to: retrieve a search phrase entered into the search engine; search phrase processing. To process the search phrase, the server is configured to: calculate a normalized search phrase based on the search phrase and tokens from the set of tokens; calculating a generalized search phrase based on a normalized search phrase and synonyms from a set of synonyms and linking at least one normalized query from a set of normalized queries to a search phrase based on a generalized search phrase and index structure, and at least one normalized query is different from a normalized search phrase .

[20] In some server embodiments, to compute a set of normalized queries, the server is further configured to, for each given search query from a plurality of search queries: determine a corresponding token for each significant word in a given search query; the creation of this normalized query associated with this search query, and this normalized query includes the corresponding token of each significant word in this search query.

[21] In some embodiments of the method, for calculating a set of generalized queries, the server is configured to, for each normalized query from a set of normalized queries: determine the corresponding main synonym for each token in this normalized query; the creation of this generalized query associated with this normalized query, and this generalized query includes the corresponding main synonym for each token in this normalized query.

[22] In some server embodiments, a given normalized request is associated with a normalized frequency counter that indicates a series of search queries from a plurality of search queries that are associated with this normalized request.

[23] In some server embodiments, a given generalized query is associated with a generalized frequency counter that indicates a series of normalized queries from the set of normalized queries that are associated with this generalized query.

[24] In some embodiments of the server, each significant word is a modified paradigm of the corresponding token.

[25] In some server embodiments, the corresponding primary synonym for each token is determined based on corresponding indications of the popularity of synonyms from the set of synonyms.

[26] In some embodiments of the server, the at least one logical tree from the set of logical trees includes: a parent node; at least one child node of the first level and at least one child node of the second level.

[27] In some server embodiments, the at least one logical tree represents at least one corresponding generalized request associated with at least one corresponding normalized request, and at least one generalized request is associated with at least one of: parent a node, at least one child node of the first level and at least one child node of the second level.

[28] In some embodiments of the server, the server is further configured to: receive a current search query entered into a search engine; Processing the current search request. To process the current search query, the server is configured to: calculate the normalized current search query based on the current search query and tokens from the set of tokens; calculating a generalized current search query based on a normalized search query and synonyms from a set of synonyms; and linking the normalized current query to the target content associated with the search phrase based on at least one of: the normalized search phrase and at least one normalized query associated with the search phrase.

[29] In the context of the present description, unless clearly indicated otherwise, "electronic device", "user device", "server", "remote server" and "computer system" mean hardware and / or system software suitable for the solution corresponding task. Thus, some non-limiting examples of hardware and / or software include computers (servers, desktop computers, laptops, netbooks, etc.), smartphones, tablets, network equipment (routers, switches, gateways, etc.) and / or their a combination.

[30] In the context of the present description, unless clearly indicated otherwise, “machine-readable medium” and “memory” means a medium of absolutely any type and character, non-limiting examples include RAM, ROM, disks (CDs, DVDs) , floppy disks, hard drives, etc.), USB keys, flash cards, solid state drives and tape drives.

[31] In the context of the present description, unless clearly indicated otherwise, the "indication" of an information element may be the information element or pointer itself, a reference, a link or other indirect method allowing the recipient of the instruction to find a network, memory, database or other computer-readable medium from which the information item can be extracted. For example, a file reference can include the file itself (i.e. its contents), or it can be a unique file descriptor that identifies the file with respect to a specific file system, or by some other means send the destination a network folder , a memory address, a table in the database, or another place where you can access the file. As will be understood by those skilled in the art, the degree of accuracy necessary for such an indication depends on the degree of primary understanding of how the information exchanged between the receiver and sender of the pointer. For example, if before establishing a connection between the sender and the recipient, it is clear that the sign of the information element takes the form of a database key for recording in a specific table a pre-installed database containing the information element, then transferring the database key is all that is necessary for the efficient transmission of information element to the recipient, despite the fact that the information element itself was not transmitted between the sender and the recipient of the instruction.

[32] In the context of the present description, unless specifically indicated otherwise, the words "first", "second", "third", etc. used in the form of adjectives solely to distinguish the nouns to which they relate from each other, and not for the purpose of describing any specific relationship between these nouns. So, for example, it should be borne in mind that the use of the terms “first server” and “third server” does not imply any order, assignment to a certain type, chronology, hierarchy or ranking (for example) of servers / between servers, as well as their use (in itself) does not imply that a certain "second server" must exist in a given situation. Further, as indicated here in other contexts, the mention of the “first” element and the “second” element does not exclude the possibility that it is one and the same actual real element. So, for example, in some cases, the “first” server and the “second” server can be the same software and / or hardware, and in other cases they can be different software and / or hardware.

[33] Each embodiment of the present technical solution pursues at least one of the above objectives and / or objects, but the presence of all is not required. It should be borne in mind that some objects of this technical solution, obtained as a result of attempts to achieve the aforementioned goal, may not satisfy this goal and / or may satisfy other goals not specifically indicated here.

[34] Additional and / or alternative characteristics, aspects and advantages of embodiments of the present technical solution will become apparent from the following description, the attached drawings and the attached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[35] For a better understanding of this technical solution, as well as its other aspects and characteristics, reference is made to the following description, which should be used in combination with the accompanying drawings, where:

[36] In FIG. 1 is a diagram of a computer system that is suitable for implementing the present technology and / or which is used in combination with embodiments of the present technology.

[37] In FIG. 2 is a schematic representation of the processing of search queries from a plurality of search queries by a processing module, in some embodiments of the present technical solution, for calculating, among other things, a set of normalized queries.

[38] In FIG. 3 presents a variety of search queries that are stored in a database of search queries, as described in some embodiments of the present technical solution.

[39] In FIG. 4 presents a set of normalized queries and links between normalized queries and related related search queries, as shown in some embodiments of the present technical solution.

[40] In FIG. 5 is a schematic representation of the processing of normalized queries from a set of normalized queries by a processing module, in some embodiments of the present technical solution, for calculating, among other things, a set of generalized queries.

[41] In FIG. 6 presents a set of generalized queries and relationships between generalized queries and the corresponding related normalized queries created by the processing module and stored in the processing database, as shown in some embodiments of the present technical solution.

[42] In FIG. 7 is a schematic representation of the processing of generalized queries and normalized queries by the processing module, in some embodiments of the present technical solution, for compiling the index structure.

[43] In FIG. 8 shows an index structure including a set of logical trees that is stored in an index database, as described in some embodiments of the present technical solution.

[44] In FIG. 9 is a flowchart of a search query processing method that is performed by a server in some embodiments of the present technical solution.

[45] It should also be noted that the drawings are not to scale, unless specifically indicated otherwise.

IMPLEMENTATION

[46] All examples and conditional constructions used here are intended primarily to help the reader understand the principles of this technology, and not to establish the boundaries of its scope. It should also be noted that specialists in this field of technology can develop various schemes that are not separately described and not shown here, but which, however, embody the principles of this technology and are within its scope.

[47] In addition, for clarity of understanding, the following description relates to fairly simplified embodiments of the present technology. As will be clear to a person skilled in the art, many embodiments of the present technical solution will have much greater complexity.

[48] Some useful examples of modifications to the present technology may also be covered by the following description. The purpose of this is also solely assistance in understanding, and not determining the scope and boundaries of this technical solution. These modifications are not an exhaustive list, and those skilled in the art can create other modifications that remain within the scope of this technology. In addition, those cases where examples of modifications were not presented should not be interpreted as the fact that no modifications are possible, and / or that what has been described is the only embodiment of this element of the present technology.

[49] Moreover, all the principles, aspects and options for the implementation of this technology, as well as their specific examples, are intended to indicate their structural and functional foundations, regardless of whether they are currently known or will be developed in the future. . Thus, for example, it will be apparent to those skilled in the art that the block diagrams presented here are conceptual illustrative diagrams that reflect the principles of the present technology. Similarly, any flowcharts, diagrams, pseudo codes, etc. are various processes that can be represented on a computer-readable medium and thus be used by a computer or processor, regardless of whether an explicitly similar computer or processor is shown or not.

[50] The functions of the various elements shown in the figures, including a function block designated as “processor” or “graphics processor”, can be provided using specialized hardware or hardware capable of using suitable software. When it comes to a processor, functions can be provided by one specialized processor, one common processor or many individual processors, some of which may be shared. In some embodiments of the present technology, the processor may be a general purpose processor, such as a central processing unit (CPU), or a processor specialized for a specific purpose, such as a graphics processing unit (GPU). Moreover, the use of the term “processor” or “controller” should not imply exclusively hardware capable of supporting the operation of the software, and may include, without limitation, a digital signal processor (DSP), a network processor, a special purpose integrated circuit ( ASIC), Field Programmable Gate Array (FPGA), read-only memory (ROM) for storing software, random access memory (RAM) and non-volatile memory minal device. Other hardware may also be included, conventional and / or special.

[51] Software modules or simple modules, which are software that can be used here in combination with flowchart elements or other elements that indicate the execution of process steps and / or text description. Similar models can be performed on hardware shown directly or indirectly.

[52] Based on these notes, some non-limiting embodiments of aspects of the present technology will be discussed below.

[53] In FIG. 1 illustrates a system 100 suitable for use with some embodiments of the present technology. System 100 may comprise, inter alia, client device 104, network 106, server 108, and target content resources 180.

[54] Embodiments of the client device 104 are not particularly limited, but, as an example, the client device 104 may communicate with the server 108 by transmitting / receiving data to the server 108 / from the server 108. The client device 104 may, for example, not A limiting example is a desktop computer, laptop, smartphone (e.g. Apple iPhone ™ or a Samsung Galaxy S5 ™), personal digital organizer (PDA), or other device that includes computing functionality and data transfer capabilities. Client device 104 may include, inter alia, internal hardware components, including one or more single or multi-core processors, collectively represented here as a processor (not shown), RAM (not shown), a network interface (not shown) for communication with server 108.

[55] The client device 104 is associated with a user 102 that can interact with the client device 104. In some embodiments of the present technical solution, the client device 104 may be configured to execute a browser application (not shown). In general, the task of a browser application is to provide a user 102 with access to one or more web resources. The implementation of the browser application is not specifically limited. For example, the browser application 104 may be implemented as a Yandex ™ browser. Although the client device 104 is associated with the user 102, this communication does not require authorization 102 or the like from the user 102.

[56] Client device 104 is connected to network 106 through data networks (not numbered). In some embodiments of the present technical solution, the network 106 may be implemented as the Internet. In other embodiments of the present technology, the network 106 may be implemented differently — as a global data network, a local data network, a private data network, and the like.

[57] The implementation of the data link is not limited and will depend on which client device 104 is used. By way of example (but not limitation) in these embodiments of the present technology, in cases where the client device 104 is a wireless communication device (eg, a smartphone), the data line is a wireless data network (for example, inter alia, a data line 3G, 4G data line, Wireless Fidelity or WiFi®, Bluetooth®, etc.). In those examples where the client client device 104 is a laptop computer, the data line can be either wireless (Wireless Internet Wireless Fidelity or WiFi®, Bluetooth®, etc.) or wired (Ethernet-based connection).

[58] It is important to keep in mind that the implementations of client device 104, data lines, and network 106 are for illustrative purposes only. Thus, those skilled in the art will be able to understand the details of other specific embodiments of the client device 104, data line and network 106. That is, the examples presented here do not limit the scope of this technical solution.

[59] Also connected to the network 106 is a server 108, on which a search engine service may be located to process search queries related to one or more users (eg, user 102). Server 108 may be a conventional computer server. In an example embodiment of the present technology, server 108 may be a Dell ™ PowerEdge ™ server that uses a Microsoft ™ Windows Server ™ operating system. Needless to say, the server 108 may be any other suitable hardware and / or application software and / or system software, or a combination thereof. In the presented embodiment of the present technical solution, not limiting its scope, server 108 is a single server. In other embodiments of the present technical solution, not limiting its scope, the functionality of the server 108 can be divided, and can be performed using multiple servers.

[60] Embodiments of the server 108 are well known to those skilled in the art. However, in short: the server 108 comprises a communication interface (not shown) that is configured and configured to connect to various elements (eg, client device 180 and other devices potentially connected to network 106) via network 106.

[61] As mentioned previously, the server 108 may implement a search engine service. In general, a search engine service is controlled and / or managed by a search engine provider (not shown), such as, for example, the Yandex ™ search engine operator. Thus, the search engine of the server 108 can be configured to perform one or more searches in response to search queries that are entered by the user 102 and / or potential users of the search engine service. The search engine service of the server 108 is also configured to transmit to the client device 104 a set of search results that will be displayed to the user 102 associated with the search queries that have been entered by the user 102.

[62] Target content resources 180 are also connected to the data network 106. In general, target content resources 180 are managed by a plurality of target content providers that promote products and / or services. In another example, target content resources 180 may provide the search engine, which is located on server 108, target content data, which includes: (i) data related to a variety of advertisements promoting products and / or services, for example, images, video, text or other data, (ii) search phrases associated with each advertisement in the set of advertisements, respectively, to establish the correspondence of advertisements with the search queries entered into the search engine service systems by the user 102 and / or other potential users of the search engine service, and (iii) prices offered by the respective target content provider from the plurality of target content providers to display their advertisements to the user 102 and / or other potential users of the search engine service.

[63] Like server 108, target content resources 180 can be implemented as ordinary computer servers. In an example embodiment of the present technical solution, the target content resources 180 may be a Dell ™ PowerEdge ™ server that uses the Microsoft ™ Windows Server ™ operating system. Needless to say, the target content resources 180 may be any other suitable hardware and / or application software and / or system software, or a combination thereof. In other embodiments of the technical solution, the target content resources 180 may be implemented as websites, respectively, associated with a plurality of target content providers and located on a corresponding conventional server system.

[64] In other embodiments of the technical solution, target content resources 180 may provide target content data to server 108 to promote products and / or services. For example, target content resources 180 may transmit to server 108 via network 106 a first data packet 194. The first data packet 194 may contain target content data. In response to receiving the first data packet 194, the server 108 may be configured to store the target content data in the target content database 150.

[65] In some embodiments of the present technical solution, the server 108 may be connected and / or functionally connected to the token database 120, the synonym database 130, the search query database 140, the target content database 150, the processing database 160 and the index a database of 170 data. Although the token database 120, the synonym database 130, the search query database 140, the target content database 150, the processing database 160, and the index database 170 are schematically distributed for ease of illustration, it is believed that some or all of the token database 120, the synonym database 130, the search query database 140, the target content database 150, the processing database 160, and the index database 170 can be implemented as a single element. The functionality of each database of tokens database 120, synonym database 130, search query database 140, target content database 150, processing database 160 and index database 170, as well as the data and / or information that is stored on them, will be described Further.

[66] Token database 120 may be configured to store data associated with tokens and correspondingly related modified paradigms. In the general case, due to the grammatical rules of various languages, this token may be associated with a data set of inflected paradigms. For example, if a given token is a ride, then the set of modified paradigms may include these modified paradigms: riding, ridden, rode, and so on. In another example, if a given token is a "sing", then the set of modified paradigms may include these modified paradigms: "singing", "sang", "sung" and so on. The token database 120 may include data related to thousands of tokens and related associated paradigms. It should be noted that for example and for ease of understanding, these lexemes and correspondingly related inflected paradigms of English grammar were used, but tokens and accordingly related inflected paradigms of other grammars (for example, Russian, French, German, Spanish and so on) can also be represented in some options, without going beyond the boundaries of this technical solution.

[67] In some embodiments of the present technical solution, the synonym database 130 may be available to the server 108. The synonym database 130 may be configured to store data associated with words and correspondingly associated synonyms. In general, a synonym is a word with the same or similar meaning as another word. For example, in English, the words “happy” and “joyful” are synonyms for the word “elated”. The synonym database 130 may include data relating to a large number of synonyms associated with the corresponding words for which they are synonyms. In another embodiment, the synonym database 130 may include data associated with words and their corresponding synonyms in other languages, for example, Russian, French, German, Spanish, and so on.

[68] In some embodiments of the technical solution, each synonym in the synonym database 130 may be associated with an indication of their popularity in a given language. For example, the words "buy", "purchase", "acquire" and "procure" can be included in the synonym set in the synonym database 130, and an indication of the popularity of each synonym among the words "buy", "purchase", "acquire" and "procure" may indicate that the word "buy" is the most popular synonym and / or most commonly used synonym and / or main synonym in English, among "buy", "purchase", "acquire" and "procure".

[69] In some embodiments of the technical solution, server 108 may be configured to use history information collected from a large number of previous search queries that may be stored in the search query database 140. The search query database 140 shown in FIG. 1 may be accessible by the server 108. The search query database 140 may be configured to store data related to the search queries that have been entered by the user 102 and / or other potential users of the search engine service into a search engine located on the server 108. It should be note that the search queries entered into the search engine service implemented by the server 108 can be implemented in languages other than English.

[70] The method of storing data related to search queries, as well as the method of collecting and / or providing them by server 108 / server 108 are not specifically limited, and only as an example, data related to search queries stored in the database 140 search queries can be received by the server 108 through the network 106 from the client device 104 and / or other potential client devices associated with other potential users of the search engine service implemented by the server 108.

[71] As mentioned previously, the server 108 may store the target content data in the target content database 150. In some embodiments of the technical solution, the server 108 may be configured to request and receive target content data from the target content resources 180. In other embodiments of the technical solution, target content resources 180 may be configured to transmit target content data to server 108 without initiating a request from server 108. Server 108 may be configured to receive a first data packet 194, which includes target content data, and store target content data in a target content database 150.

[72] For example, target content data may relate to a variety of advertisements promoting products and / or services. A given advertisement from a plurality of advertisements may be associated with a given search phrase to establish the correspondence of this advertisement and this current search query entered into a search engine service, for example, by user 102. This advertisement may be associated with a given price offered by this provider target content related to this advertisement to display the advertisement to user 102 in response to a correspondence between the data search terms and this search phrase related to this ad.

[73] In some embodiments of the present technical solution, server 108 may have access to processing database 160. The processing database 160 may be configured to store various types of data. In general, the processing database 160 may store data related to the processing of search queries, search phrases, tokens and synonyms by the server 108. The processing of search queries, search phrases, tokens and synonyms by the server 108 will be described later and, therefore, the functionality of the database 160 treatments will be apparent to those skilled in the art.

[74] In some embodiments of the present technical solution, the server 108 may further have access to the index database 170. The index database 170 may include an index structure 706 (shown in FIG. 8), which is shown in some embodiments of the present technical solution. The index structure 706 can be described and stored by the server 108 in the index database 170 based on the processing of search queries, search phrases, tokens and synonyms. How server 108 can compile index structures will be described in more detail below.

[75] In some embodiments of the present technical solution, the server 108 may be configured to process search queries. To this end, server 108 may implement processing module 110 and index creation module 112. The processing module 110 and the index generating module 112 may be a device or a plurality of modules made using hardware, for example, using, for example, a special purpose integrated circuit (ASIC) or programmable logic integrated circuit (FPGA), or a combination of hardware and software, for example, using a microprocessor system and a set of instructions that implement the functionality of the module, which (when executed) transform the microprocessor system into a special device Nogo destination. The processing module 110 and the index generating module 112 can also be implemented as a combination of both, with some specific functions implemented through hardware and other functions implemented by a combination of hardware and software.

[76] In other embodiments of the technical solution, the processing module 110 and the index generating module 112 may also be implemented in software to execute various types of processors. Any of the processing module 110 and the index generating module 112 may, for example, include one or more physical or logical blocks of computer instructions, which may, for example, be organized as an object, procedure, or function. However, the executable files of this module do not have to be located together, but can consist of separate instructions stored in various places, which, if they are logically connected, represent this module and achieve the goals stated for this module. It should be noted that in other embodiments of the present technical solution, the processing module 110 and the index creating module 112 may be implemented by the server 108 as a single element.

[77] In further embodiments of the technical solution, the processing module 110 and the index generating module 112 need not be implemented in the same ways. For example, processing module 110 may be implemented as a software module, and index creation module 112 may be implemented as a combination of hardware and / or software, or vice versa. The functionality of the processing module 110 and the index generating module 112 will be described in more detail below, and at least some of their advantages will become apparent to those skilled in the art.

[78] In some embodiments of the present technical solution, the server 108 may be configured to receive a plurality of search queries 300 (shown in FIG. 3) entered into a search engine hosted on the server 108. The plurality of search queries 300 may include hundreds or thousands or even millions of search queries that have been entered into a search engine. For example, the processing module 110 of the server 108 may receive a plurality of search queries 300 from the search query database 140 via the data path 202 shown in FIG. 2.

[79] Processing module 110 may be configured to parse each search query from multiple search queries 300 to identify meaningful words contained in each search query from multiple search queries 300. In some embodiments of the technical solution, all words in this search query, with the exception of the prepositions, for example, "a", "the", "with", "for" and the like, can be significant words of this search query. How exactly the relevant words from each search query are identified will be clear to a person skilled in the art, taking into account the following examples.

[80] In FIG. 3 presents the first search query (SQ1), the second search query (SQ2), the third search query (SQ3), the fourth search query (SQ4), the fifth search query (SQ5), the sixth search query (SQ6), the seventh search query (SQ7) , the eighth search query (SQ8), the ninth search query (SQ9) and the tenth search query (SQ10) It is important to keep in mind that many other search queries can be parsed by the processing module 110 in other embodiments of the present technical solution, and that the search queries SQ1 , SQ2, SQ3, SQ4, SQ5, SQ6, SQ7, SQ8, SQ9 and SQ10 are represented by lko for ease of understanding.

[81] For example, processing module 110 may parse the search query SQ1 to identify meaningful words: "buying", "car" and "Moscow". Processing module 110 may parse the search query SQ2 to identify meaningful words: "bought", "car" and "Moscow". Processing module 110 may parse the SQ3 search query to identify meaningful words: "buying", "vehicles" and "Moscow". Processing module 110 may parse the search query SQ4 to identify meaningful words: "purchasing", "automobiles" and "Moscow". Processing module 110 may parse the SQ5 search query to identify meaningful words: "purchased", "cars" and "Montreal". Processing module 110 may parse the SQ6 search query to identify meaningful words: "buying", "car" and "Montreal". Processing module 110 may parse SQ7 search to identify meaningful words: "purchasing" and "fridge". Processing module 110 may parse the SQ8 search query to identify meaningful words: “buying,” “fridge,” and “Paris.” Processing module 110 may parse the search query SQ9 to identify meaningful words: "selling", "car" and "Berlin". Processing module 110 may parse the search query SQ10 to identify meaningful words: "sold" and "car".

[82] In other embodiments of the technical solution, the processing module 110 may be configured to obtain a set of tokens associated with meaningful words identified with a plurality of search queries 300. For example, a set of tokens may be obtained from a token database 120 through data path 204 shown in FIG. 2. In this case, the set of tokens may include tokens associated with significant words (for example, inflected paradigms): "buying", "car", "Moscow", "bought", "vehicles", "automobiles", " purchasing "," purchased "," cars "," Montreal "," fridge "," Paris "," selling "," Berlin "and" sold ".

[83] For example, a set of tokens may include a buy token, because it is associated with the meaningful words “buying” and “bought”, which are some of the word-wise paradigms of the token “buy”. The set of tokens may include the token "car", since it is associated with the meaningful words "car" and "cars". The set of tokens may include the token "Moscow", since it is associated with the meaningful word "Moscow". The token set may include the vehicle token, as it is associated with the meaningful word vehicles. The token set may include the word “automobile”, as it is associated with the meaningful word “automobiles”. A set of tokens may include the token "purchase", since it is associated with the meaningful words "purchasing" and "purchased". The token set may include the Montreal token, as it is associated with the meaningful word Montreal. A set of tokens may include the token “Paris” because it is associated with the meaningful word “Paris”. The token set may include the token “Berlin”, as it is associated with the meaningful word “Berlin”. The token set may include the fridge token, as it is associated with the meaningful word fridge. A set of tokens may include a sell token, as it is associated with the meaningful words selling and sold.

[84] In other embodiments of the present technical solution, processing module 110 may be configured to calculate normalized query data 206 based on search queries from a plurality of search queries 300 and tokens from a set of tokens. Further, the processing unit 110 may be configured to store normalized request data 206 in the processing data base 160. For example, processing module 110 may transmit normalized request data 206 to processing data base 160 via data path 208. Normalized request data 206 will now be described with reference to FIG. four.

[85] In FIG. 4 presents normalized query data 206, which includes: a plurality of search queries 300, normalization communications 404, normalized frequency counters 402, and a set of normalized queries 400. Processing module 110 may create a set of normalized queries 400 that includes a first normalized query (NQ1 ), the second normalized query (NQ2), the third normalized query (NQ4), the fourth normalized query (NQ5), the sixth normalized query (NQ6), the seventh normalized query (NQ7), the eighth normalized query (NQ8 ) and the ninth normalized query (NQ9).

[86] In general, computing a set of normalized queries 400 by processing module 110 may include, for each search query from a plurality of search queries, (i) determining an appropriate token for each meaningful word in a given search query and (ii) creating a given normalized query associated with this search query, and this normalized query includes the corresponding tokens of each significant word in this search query.

[87] For example, processing module 110 may generate NQ1 based on SQ1 and tokens in the token set. Since SQ1 is “buying a car in Moscow”, the processing module 110 identifies in SQ1 by parsing the meaningful words “buying”, “car” and “Moscow”. As a result, the processing module 110 can create NQ1 by amalgamating the corresponding meaningful tokens identified in SQL. Therefore, the processing module 110 can create NQ1 in the form: "buy" "car" "Moscow". In another example, processing module 110 may generate NQ1 based on SQ2 and tokens in the token set. Since SQ2 is “bought a car in Moscow”, the processing module 110 identifies in SQ2 by parsing the significant words “bought”, “car” and “Moscow”. As a result, processing module 110 can create NQ1 by amalgamating the corresponding meaningful tokens identified in SQ2. Therefore, the processing module 110 may create NQ1 in the form: "buy" "car" "Moscow".

[88] In some embodiments of the technical solution, a given normalized query may be associated with a given normalized frequency counter, which indicates a series of search queries from a plurality of search queries 300 that are associated with this normalized query. Therefore, the processing unit 110 may associate NQ1 with the normalized frequency counter “2” since NQ1 may be associated with two separate search queries, for example, SQ1 and SQ2. The relationship of NQ1 with SQ1 and SQ2, respectively, is shown in FIG. 4 among other normalized relationships 404.

[89] In another example, processing module 110 may generate NQ2 based on SQ3 and tokens in the token set. Since SQ3 is “buying vehicles in Moscow”, the processing module 110 identifies in SQ3 by parsing the significant words “buying”, “vehicles” and “Moscow”. As a result, processing module 110 can create NQ1 by amalgamating the corresponding meaningful tokens identified in SQ3. Therefore, the processing module 110 may create NQ2 in the form: "buy" "vehicle" "Moscow".

[90] Processing module 110 may associate NQ2 with a normalized frequency counter “1”, since NQ2 can be associated with only one search query from a plurality of search queries 300, for example, SQ3. The association of NQ2 with SQ2 is respectively shown in FIG. 4 among other normalized relationships 404.

[91] Further, processing module 110 may create NQ3, NQ4, NQ5, NQ6, NQ7, NQ8, and NQ9 in the same way that processing module 110 creates NQ2.

[92] In some embodiments of the present technical solution, with reference to FIG. 5, processing module 110 may be configured to calculate generalized request data 506. To this end, processing module 110 may receive normalized query data 206 from processing database 160 through data path 502 and a set of synonyms from synonyms database 130 through data path 504. As previously mentioned, normalized query data 206 may include, but is not limited to, data associated with a set of tokens and a set of normalized queries 400. As a result, processing module 110 may obtain a synonym set from the synonym database 130 including the corresponding synonyms each token from the set of tokens. Processing module 110 may be configured to store normalized query data 506 in processing database 160. For example, processing module 110 may transmit generalized request data 506 to processing database 160 through data path 508. Generic request data 506 will now be described with reference to FIG. 6.

[93] In FIG. 6, generalized query data 506 is presented, which includes: a set of normalized queries 400, normalized frequency counters 602, generalized relationships 604, generalized frequency counters 602, and a set of generalized queries 600. Processing module 110 may execute a set of generalized queries 600, including a first generalized query (GQ1), the second generalized query (GQ2), the third generalized query (GQ3), the fourth generalized query (GQ4), the fifth generalized query (GQ5) and the sixth generalized query (GQ6).

[94] In general, the calculation of the set of generalized queries 600 by the processing unit 110 may include, for each given normalized query from the set of normalized queries 400, (i) determining the corresponding basic synonym for each token in the given search query and (ii) creating this generalized query associated with this normalized query, and this generalized query includes the corresponding basic synonyms of each token in this normalized query.

[95] For example, processing module 110 may generate GQ1 based on NQ1 and synonyms from a set of synonyms. Since NQ1 is a "buy" "car" "Moscow", processing module 110 can create GQ1 by amalgamating the corresponding basic synonyms of each token in NQ1. For example, the main synonym for the token "buy" may be "buy", because among the synonyms the words "buy", "buy" may be the most commonly used synonym. In another example, the main synonym for the token "car" may be "car", because among the synonyms the words "car", "car" may be the most commonly used synonym. In another example, the main synonym for the token “Moscow” may be “Moscow”, because among the synonyms the words “Moscow”, “Moscow” may be the most frequently used synonym. Processing module 110 may determine a basic synonym for a given token based on an appropriate indication of the popularity of each synonym in the synonym database 130. Therefore, the processing module 110 may create GQ1 in the form: "buy" "car" "Moscow".

[96] Further, processing module 110 may create GQ1 based on NQ2 and synonyms from a set of synonyms. Since NQ2 is a "buy" "vehicle" "Moscow", processing module 110 can create GQ1 by amalgamating the corresponding basic synonyms of each token in NQ2. In one example, the main synonym for the vehicle token may be car, because among the synonyms for vehicle, car may be the most commonly used synonym. Therefore, the processing module 110 may create GQ1 in the form: "buy" "car" "Moscow".

[97] In addition, processing module 110 may generate GQ1 based on NQ3 and synonyms from a set of synonyms. Since NQ3 is a "purchase" "automobile" "Moscow", processing module 110 may create GQ1 by amalgamating the corresponding basic synonyms of each token in NQ3. For example, the main synonym for the token “purchase” may be “buy”, because among the synonyms the words “purchase”, “buy” may be the most commonly used synonym. In another example, the main synonym for the token "automobile" may be "car", because among the synonyms the words "automobile", "car" may be the most commonly used synonym. Therefore, the processing module 110 may create GQ1 in the form: "buy" "car" "Moscow".

[98] In some embodiments of the technical solution, a given generalized query may be associated with a given generalized frequency counter that indicates a series of normalized queries from the set of normalized queries 400 that are associated with this generalized query. Therefore, the processing unit 110 may associate GQ1 with a generalized frequency counter “3” since GQ1 may be associated with three separate normalized queries from the set of normalized queries 400, such as NQ1, NQ2, and NQ3. The links 610, 620 and 630 of the GQ1 request, respectively, with NQ1, NQ2 and NQ3 are shown in FIG. 6 among generalizing relationships 604.

[99] In a further example, processing module 110 may generate GQ2 based on NQ4 and synonyms from a set of synonyms. Since NQ4 is a "purchase" "car" of "Montreal", processing module 110 can create GQ2 by amalgamating the corresponding basic synonyms of each token in NQ4. For example, the main synonym for the token “purchase” may be “buy”, because among the synonyms the words “purchase”, “buy” may be the most commonly used synonym. In another example, the main synonym for the token "car" may be "car", because among the synonyms the words "car", "car" may be the most commonly used synonym. In another example, the main synonym for the Montreal token may be Montreal, because among the synonyms for Montreal, Montreal may be the most commonly used synonym. Therefore, processing module 110 may create GQ1 in the form: "buy" "car" "Montreal". Similarly, processing module 110 may generate GQ2 based on NQ5 and synonyms from a set of synonyms.

[100] Therefore, the processing unit 110 may associate GQ2 with a generalized frequency counter “2” since GQ2 may be associated with two separate normalized queries from a plurality of normalized queries 400, for example, NQ4 and NQ5. The links 610, 640 and 650 of the GQ2 request, respectively, with NQ4 and NQ5 are shown in FIG. 6 among generalizing relationships 604.

[101] Further, the processing unit 110 may create GQ3, GQ4, GQ5 and GQ6 in the same way as the processing unit 110 creates GQ1 and / or GQ2. A relationship of 660 GQ3 with NQ6, a relationship of 670 GQ4 with NQ7, a relationship of 680 GQ5 with NQ8 and a relationship of 690 GQ6 with NQ9 are shown in FIG. 6 among generalizing relationships 604.

[102] In some embodiments of the present technical solution, with reference to FIG. 7, server 108 may be configured to compile the index structure 706. To this end, index creation module 112 may receive generalized query data 506 from processing database 160 through data path 702. As mentioned earlier, generalized query data 506 may include a set of normalized queries 400, normalized frequency counters 402, generalized relationships 604, generalized frequency counters 602, and a set of generalized queries 600. Index creation module 112 may also be configured to maintain an index structure 706 in the index database 170 data. For example, index creation module 112 may transmit the index structure 706 to the index database 170 via the data path 708. The index structure 706 will now be described with reference to FIG. 8.

[103] In FIG. 8 illustrates an index structure 706 that includes, among other things, a set of logical trees 808. The index creation module 112 may include a first logical tree 802, a second logical tree 804, and another logical tree 806, each logical tree from a set of logical trees 808 may be at least one generalized query associated with at least one corresponding normalized query. It should be noted that in other embodiments of the technical solution, the index structure 706 may include other structures other than the logical trees shown in FIG. 8. In other words, the index structure 706 can be any other index structure without going beyond the boundaries of this technical solution.

[104] In general, the index creation module 112 compiles the index structure 706 and may include computing a set of logical trees 808 to index a set of generalized queries 600 associated with a set of normalized queries 400. As mentioned above, each logical tree in a set of logical trees 808 may be at least one generalized query associated with at least one corresponding normalized query.

[105] In some embodiments of the technical solution, index creation module 112 may compute a first logical tree 802 based on at least one generalized query (ie, in this case, at least one generalized query is GQ1, GQ2, GQ3 and GQ4) associated with at least one corresponding normalized query (i.e., in this case, at least one corresponding normalized query is NQ1, NQ2, NQ3, NQ4, NQ5, NQ6, and NQ7).

[106] The calculation of the first logical tree 802 by the index creating module 112 will be described in more detail below. Index creation module 112 may calculate the first logical tree 802 by determining the parent node 822 of the first logical tree 802, the first level child nodes 824 of the first logical tree 802, and the second level child nodes 826 of the first logical tree 802.

[107] For example, the index creation module 112 may identify the word “buy” as the parent node 822 of the first logical tree 802, because at least one generalized query in the set of generalized queries 600 (ie, in this case at least one generalized query represents GQ1, GQ2, GQ3 and GQ4) begins with the main synonym "buy".

[108] Further, index creation module 112 may identify “car” and “refrigerator” as first level child nodes 824 of the first logical tree 802, since GQ1, GQ2, GQ3 and GQ4 include either the primary synonym for “car” or “refrigerator” after the main synonym "buy". In other words, index creation module 112 can identify “car” as one of the first level child nodes 824, since GQ1 and GQ2 contain the main synonym “car” after the main synonym “buy”. Also, index creation module 112 may identify “refrigerator” as one of the first level child nodes 824, since GQ3 and GQ4 contain the main synonym “refrigerator” after the main synonym “buy”.

[109] In this case, the word “car,” which is one of the first level child nodes 824, may not be associated with any generalized query among the set of generalized queries 600, since the set of generalized queries 600 does not include this generalized query representing: "buy" "car". On the other hand, the word "refrigerator", which is one of the first level child nodes 824, may be associated with a GQ3 request, since GQ3 is: "buy" "refrigerator". Therefore, GQ3 can be associated with the word "refrigerator", which is one of the first level child nodes 824. Given the fact that GQ3 is linked to NQ6 via bond 660 among the generalizing bonds 604; index creation module 112 may associate NQ3 with the word "refrigerator", which is one of the first level child nodes 824 of the first logical tree 802. It should be noted that GQ3 can be associated with only one node in the index structure 706.

[110] Further, the index creation module 112 may identify "Moscow" and "Montreal" as two child nodes 826 of the second level of the first logical tree 802, since GQ1 and GQ2 include either the main synonym "Moscow" or "Montreal" after the main synonyms "buy" and "car". In other words, the index creation module 112 may identify “Moscow” as one of the second level child nodes 826, since GQ1 contains the main synonym “Moscow” after the main synonyms “buy” and “car”. Also, index creation module 112 may identify "Montreal" as one of the second-level child nodes 826, since GQ2 contains the main synonym "Montreal" after the basic synonyms "buy" and "car". In addition, the index creation module 112 may identify “Paris” as one of the second level child nodes 826 of the first logical tree 802, since GQ4 contains the main synonym “Paris” after the basic synonyms “buy” and “refrigerator”.

[111] In this case, the word "Moscow", which is one of the second level child nodes 826, may be associated with the query GQ1, since GQ1 is: "buy" "car" "Moscow". Therefore, GQ1 may be associated with the word "Moscow", which is one of the second level child nodes 826. Given that GQ1 is connected to NQ1, NQ2, and NQ3 via link 610, link 620 and link 630, respectively, among the generalizing bonds 604; index creation module 112 may associate NQ1, NQ2 and NQ3 with the word “Moscow”, which is one of the second level child nodes 826 of the first logical tree 802. It should be noted that GQ1 can be associated with only one node in the index structure 706.

[112] In this case, the word "Montreal", which is one of the second level child nodes 826, may be associated with the query GQ2, since GQ2 is: "buy" "car" "Montreal". Therefore, GQ2 can be associated with the word "Montreal", which is one of the child nodes 826 of the second level. Given the fact that GQ2 is associated with NQ4 and NQ5 via bond 640 and bond 650, respectively, among the generalizing bonds 604; index creation module 112 may associate NQ4 and NQ5 with the word "Montreal", which is one of the child nodes 826 of the second level of the first logical tree 802. It should be noted that GQ2 can be associated with only one node in the index structure 706.

[113] In this case, the word "Paris", which is one of the second level child nodes 826, may be associated with a GQ4 request, since GQ4 is: "buy" "refrigerator" "Paris". Therefore, GQ4 can be associated with the word "Paris", which is one of the second level child nodes 826. Given that GQ4 is linked to NQ7 via link 670 among the generalizing bonds 604; index creation module 112 may associate NQ7 with the word “Paris”, which is one of the second level child nodes 826 of the first logical tree 802. It should be noted that GQ4 can only be associated with one node in the index structure 706.

[114] In some embodiments of the technical solution, the index creation module 112 may calculate a second logical tree 804 based on at least one generalized query (ie, in this case, at least one generalized query is GQ5 and GQ6), associated with at least one corresponding normalized query (i.e., in this case, at least one corresponding normalized query is NQ8 and NQ9).

[115] The calculation of the second logical tree 804 by the index creation module 112 will be described in more detail below. Index creation module 112 may calculate the second logical tree 804 by determining the parent node 842 of the second logical tree 804, the first level child node 844 of the second logical tree 804, and the second level child node 846 of the second logical tree 804.

[116] For example, the index creation module 112 may identify the word “sell” as the parent node 842 of the second logical tree 804, because at least one generalized query is in the set of generalized queries 600 (ie, in this case, at least one generalized query represents GQ5 and GQ6) begins with the main synonym "sell".

[117] Further, the index creation module 112 may identify “car” as a first level child node 844 of the second logical tree 804, since GQ1 and GQ4 include the primary synonym “car” after the primary synonym “sell”. Index creation module 112 may identify “car” as a first level child node 844 of the second logical tree 804 as the only first level child node of the second logical tree 804, since, in this case, none of the generalized queries from the set of generalized queries 600 includes another main synonym (ie different from "car") after the main synonym "sell".

[118] In this case, the word "car", which is a first level child node 844, may be associated with a GQ6 request, since GQ6 is: "sell" "car". Therefore, GQ6 may be associated with the word "car", which is a first level child node 844. Given that GQ6 is linked to NQ9 via link 690 among the generalizing bonds 604; index creation module 112 may associate NQ9 with the word "car", which is a first level child node 844 of the second logical tree 804. It should be noted that GQ6 can only be associated with one node in the index structure 706.

[119] In addition, the index creation module 112 may identify “Berlin” as a second level child node 846 of the second logical tree 804, since GQ5 contains the main synonym “Berlin” after the main synonyms “sell” and “car”. Index creation module 112 may identify “Berlin” as a second-level child node 846 of the second logical tree 804 as the only second-level child node of the second logical tree 804, since, in this case, none of the generalized queries from the set of generalized queries 600 includes another main synonym (ie different from "Berlin") after the main synonyms "sell" and "car".

[120] In this case, the word "Berlin", which is a second level child node 846, may be associated with a GQ5 request since GQ5 is: "sell" "car" "Berlin". Therefore, GQ5 can be associated with the word "Berlin", which is a child node 846 of the second level. Given that GQ5 is linked to NQ8 via link 680 among the generalizing bonds 604; index creation module 112 may associate NQ8 with the word "Berlin", which is a child node 846 of the second level of the second logical tree 804. It should be noted that GQ5 can be associated with only one node in the index structure 706.

[121] In some embodiments of the technical solution, index creation module 112 may compute another logical tree 806 based on at least one generalized query associated with at least one corresponding normalized query. As mentioned previously, a plurality of search queries 300 may include many other search queries, as shown in FIG. 3, different from SQ1, SQ2, SQ3, SQ4, SQ5, SQ6, SQ7, SQ8, SQ9 and SQ10, which are simplified in some embodiments of the present technical solution. This means that the set of normalized queries 400 can include many other normalized queries, as shown in FIG. 4, different from NQ1, NQ2, NQ3, NQ4, NQ5, NQ6, NQ7, NQ8 and NQ9, which are simplified in some embodiments of the present technical solution. This also means that the set of generalized queries 600 can include many other generalized queries, as shown in FIG. 6, different from GQ1, GQ2, GQ3, GQ4, GQ5 and GQ6, which are simplified in some embodiments of the present technical solution. Therefore, index creation module 112 may compute another logical tree 806, which is at least one other logical tree, based on at least one generalized query associated with at least one corresponding normalized query.

[122] It should be noted that in some embodiments of the technical solution, the parent node 822 of the first logical tree 802 and the parent node 842 of the second logical tree 804 may be associated with at least one generic request. In general, a given parent node of a given logical tree from a set of logical trees 808 may be associated with this generalized query and at least one correspondingly associated data with a normalized query.

[123] For example, if the set of generalized requests 600 contains another generalized request, which is the word "buy", the parent node 822 of the first logical tree 802 may be associated with another generalized request. Since another generalized query will be associated with at least one other normalized query, at least one other normalized query may be associated with the parent node 822 of the first logical tree 802. Similarly, if the set of generalized queries 600 contains an additional generalized query, which is a word "sell", the parent node 842 of the second logical tree 804 may be associated with an additional generic request. Since an additional generalized query will be associated with at least one other normalized query, at least one additional normalized query may be associated with the parent node 842 of the first logical tree 804.

[124] In some embodiments of the present technical solution, the server 108 may be configured to process search phrases stored in the target content database 150. To process the search phrases entered into the search engine service hosted on the server 108, the server 108 may use the index structure 706 stored in the index database 170.

[125] For example, the server 108 may be configured to retrieve a search phrase from search phrases from the target content database 150. As mentioned earlier, the search phrase may be associated with an advertisement from a plurality of advertisements received by the server 108 using the first data packet 194 to match the advertisement with the current search queries entered into the search engine by the user 102 and / or other potential users of the service search engine.

[126] Server 108 may be configured to process search phrases obtained from target content database 150. In general, processing the search phrase by the server 108 may include: (i) calculating a normalized search phrase based on the search phrase and tokens from the set of tokens; (ii) calculating a generalized search phrase based on a normalized search phrase and synonyms from a set of synonyms; and (iii) linking at least one normalized query from a set of normalized queries 400 to a search phrase based on a generalized search phrase and index structure 706. Similar processing of a search phrase the server 108 can improve the establishment of correspondence between the search phrase and the current search queries. In other words, improved matching can reduce the response time of the server 108 for delivering this advertisement to the user 102 and / or other potential users in response to a match between the given search phrase associated with this advertisement and this current search query entered by the user 102 and / or other potential users. In other embodiments of the technical solution, such search phrase processing can be performed offline, which can reduce the necessary bandwidth load on the network 106. Further, processing the search phrase and search queries may require less computing resources than the methods existing in the prior art. , since the problem of a combinatorial explosion is resolved when processing long search queries and / or search phrases.

[127] As previously mentioned, the processing of the search phrase by the server 108 may include calculating a normalized search phrase based on the search phrase and the token from the set of tokens. For the purpose of facilitating understanding, it is assumed that the search phrase is “procuring a car in Moscow”. It should be noted that many possible search phrases can be considered in other embodiments of the technical solution, without going beyond the boundaries of this technology.

[128] Similar to the fact that the processing unit 110 calculates a given normalized query based on the given search query, the processing unit 110 of the server 108 can calculate the normalized search phrase based on the search phrase and tokens from the set of tokens.

[129] For example, processing module 110 may generate a normalized search phrase based on the search phrase and tokens in the token set. Processing module 110 may identify the relevant words "procuring", "car" and "Moscow" in a search phrase, using parsing. As a result, the processing module 110 can create a normalized search phrase by amalgamating the corresponding tokens of meaningful words (i.e., modified paradigms) identified in the search phrase. Therefore, the processing module 110 may create a normalized search phrase in the form: "procure" "car" "Moscow". In some embodiments of the technical solution, the server 108 may be configured to store a search phrase, a normalized search phrase, and a normalized relationship between the search phrase and the normalized search phrase in processing database 160.

[130] Similar to the fact that the processing unit 110 computes the given generalized query based on the given normalized query, the processing unit 110 of the server 108 can calculate the generalized search phrase based on the normalized search phrase and synonyms from the set of synonyms.

[131] For example, processing module 110 may create a generic search phrase based on a normalized search phrase and synonyms from a set of synonyms. Since the normalized search phrase is "procure" "car" "Moscow", the processing module 110 may create a generalized search phrase by amalgamating the corresponding basic synonyms of each token in the normalized search phrase. The main synonym for the procure token may be buy, since among the synonyms the words procure, buy may be the most commonly used synonym. In another example, the main synonym for the token "car" may be "car", because among the synonyms the words "car", "car" may be the most commonly used synonym. In another example, the main synonym for the token “Moscow” may be “Moscow”, because among the synonyms the words “Moscow”, “Moscow” may be the most frequently used synonym. Therefore, the processing module 110 may create a generalized search phrase in the form: "buy" "car" "Moscow". In some embodiments of the technical solution, the server 108 may further be configured to store a generalized search phrase and a generalized relationship between a normalized search phrase and a generalized search phrase in processing database 160.

[132] Further, the server 108 may be configured to associate at least one normalized query from a set of normalized queries in the index structure 706 with a search phrase based on a generalized search phrase and index structure 706.

[133] For example, server 108 may access the index structure 706 in the index database 170. Further, the server 108 can access the first logical tree 802, since the generalized search phrase begins with the word "buy", and the parent node 822 of the first logical tree 802 is also the word "buy". Further, the server 108 can access the first level child node 824, which is “car,” and then access the second level child node 826, which is “Moscow,” since the generic search phrase includes “car” and "Moscow" after the word "buy". In other words, the server 108 can access the second level child node 826, which is “Moscow,” since the generic search phrase matches GQ1. Therefore, the server 108 may associate at least one normalized query, which is NQ1, NQ2, and NQ3, with a search phrase and a normalized search phrase.

[134] In response to the association between NQ1, NQ2 and NQ3 with the search phrase, server 108 may associate NQ1, NQ2 and NQ3 and the normalized search phrase with an advertisement associated with the search phrase. In some embodiments of the technical solution, the server 108 may store NQ1, NQ2, and NQ3 and a normalized search phrase in the target content database 150, and their corresponding associations with the advertisement associated with the search phrase. In other words, the advertisement may be associated with the search phrase, NQ1, NQ2, NQ3, and the normalized search phrase, respectively.

[135] In further embodiments of the technical solution, the server 108 may store GQ1 in the target content data base 150, and their association with the advertisement associated with the search phrase. In other words, the advertisement may be associated with a search phrase, NQ1, NQ2, NQ3, a normalized search phrase, and GQ1 (i.e., in this case, GQ1 corresponds to a generalized search phrase).

[136] In other embodiments of the technical solution, since in this case at least one normalized query (NQ1, NQ2, and NQ3) is different from the normalized search phrase, server 108 may further associate the normalized search phrase "procure" "car" "Moscow" with GQ1 (which is associated with the second level child node 826 representing "Moscow") in the first logical tree 802 of the index structure 706.

[137] In some embodiments of the present technical solution, the user 102 may wish to enter the current search query “buying vehicle in Moscow”. To this end, the user 102 can interact with the client device 104 so that the client device 104 runs the browser application. In addition, user 102 can access the website through a browser application associated with a search engine service that is executed by server 108. User 102 can enter the current search term “buying vehicle in Moscow” into the search engine service. To this end, the client device 104 may create a second data packet 192 that contains data representing the current search request, and may transmit the second data packet 192 through the network 106 to the server 108.

[138] In response to entering the current search query (ie, receiving the second data packet 192 by the server 108), the server 108 may create a current search results page (SERP) based on data representing the current search query. The current search results page may include a current advertisement that is targeted to the user 102 based on the current search query. Further, the server 108 may create a third data packet 196 that contains data representing the current SERP, and may transmit the third data packet 196 to the client device 104. In response to receiving the third data packet 196, the client device 104 may display the current SERP to the user through a browser application 102. How exactly the current advertisement is selected by the server 108 to present to the client device 104 on the SERP will be described later.

[139] In some embodiments of the present technical solution, the processing unit 110 may generate a normalized search query based on the current search query and tokens from the set of tokens. Processing module 110 may identify the relevant words "buying", "vehicle" and "Moscow" in the current search query, using parsing. As a result, the processing unit 110 may generate a normalized current search query by amalgamating the relevant meaningful tokens identified in the current search query. Therefore, the processing module 110 may generate a normalized current search query in the form: "buy" "vehicle" "Moscow". In some embodiments of the technical solution, the server 108 may be configured to save the current search query, the normalized current search query, and the normalized relationship between the current search query and the normalized current search query in the processing database 160.

[140] In other embodiments of the technical solution, the server 108 may be configured to access the target content database 150. As a result, the server 108 may be configured to match a normalized current search query with at least one normalized search phrase and / or at least one normalized query. In this case, the server 108 may determine that the normalized current search query matches NQ2, which is associated with the advertisement associated with the search phrase "procuring a car in Moscow". Although the search phrase “procuring a car in Moscow” provided by the targeted content resources 180 in connection with the advertisement does not match the current search query, server 108 may select the advertisement as the current advertisement to be presented on the current page search results to user 102 because the normalized current search query matches NQ2, which is associated with the advertisement. Further, although the normalized search phrase "procure" "car" "Moscow" associated with the advertisement does not match the normalized current search query, server 108 may select the advertisement as the current advertisement that will be presented on the current results page the search to user 102 because the normalized current search query corresponds to NQ2, which is associated with the advertisement.

[141] In another example, user 102 may enter the current search query "acquiring a vehicle in Moscow". Processing module 110 may generate a normalized search query based on the current search query and tokens from the set of tokens. In this case, the processing module 110 may identify the relevant words "acquiring", "vehicle" and "Moscow" in the current search query, using parsing. As a result, the processing unit 110 may generate a normalized current search query by amalgamating the relevant meaningful tokens identified in the current search query. Therefore, in this case, the processing module 110 may create a normalized current search query in the form: "acquire" "vehicle" "Moscow".

[142] In other embodiments of the technical solution, the server 108 may be configured to access the target content database 150. Server 108 may be configured to match a normalized current search query with at least one normalized search phrase and / or at least one normalized query. In this case, the server 108 may determine that the normalized current search query does not match any normalized search phrase and / or any normalized query in the target content database 150. As a result, the server 108 may be further configured to calculate a generalized current search query based on a normalized current search query and synonyms from a set of synonyms.

[143] Since the normalized current search query is “acquire" "vehicle" "Moscow", the processing module 110 may generate a generalized current search query by amalgamating the corresponding basic synonyms of each token in the normalized current search query. The main synonym for the token “acquire” can be “buy”, because among the synonyms the words “acquire”, “buy” may be the most commonly used synonym. In another example, the main synonym for the vehicle token may be car, since among the synonyms for the word vehicle, car may be the most commonly used synonym. In another example, the main synonym for the token “Moscow” may be “Moscow”, because among the synonyms the words “Moscow”, “Moscow” may be the most frequently used synonym. Therefore, the processing module 110 may create a generalized current search query in the form: "buy" "car" "Moscow". In some embodiments of the technical solution, the server 108 may further be configured to store a generalized current search query and a generalized relationship between a normalized current search query and a generalized current search query in the processing database 160.

[144] Further, the server 108 can access the index structure 706 stored in the index database 170. More specifically, the server 108 can access the second level child node 826 of the first logical tree 802, which is “Moscow,” since the generic current search query matches GQ1. Server 108 may be configured to associate a generic current search query "acquire" "vehicle" "Moscow" with GQ1 and, therefore, with a child node 826 of the second level of the first logical tree 802, which is "Moscow".

[145] As another embodiment of the technical solution, the server 108 may be configured to match a generalized current search query with at least one normalized search phrase and / or at least one normalized query and / or at least one generalized query in the database 150 of the target content data. In this case, server 108 may determine that the generalized current search query "buy" "car" "Moscow" matches GQ1, which is associated with an advertisement associated with the search phrase "procuring a car in Moscow". Although the search phrase “procuring a car in Moscow” provided by the targeted content resources 180 in connection with the advertisement does not match the current search query, server 108 may select the advertisement as the current advertisement to be presented on the current page search results to user 102 because the generalized current search query corresponds to GQ1, which is associated with an advertisement.

[146] In some embodiments of the present technical solution, the server 108 may be configured to execute the method 900 of FIG. 9, search query processing. Method 900 will be described in more detail below.

[147] STEP 902: Obtaining a plurality of search queries entered into a search engine

[148] The method 900 begins at block 902, where the server 902 receives a plurality of search queries 300 (shown in FIG. 3) entered into a search engine located on the server 108. In some embodiments, a plurality of search queries 300 may be received. a server 108 from the search query database 140 via the data path 202 shown in FIG. 2. Search queries from a plurality of search queries 300 may be previously entered by a user 102 and / or other potential users into a search engine service implemented by the server 108.

[149] STEP 904: Processing each search query from multiple search queries

[150] Method 900 continues to block 904, where server 108 processes each request from a plurality of search queries 300.

[151] In some embodiments of the technical solution, the processing of each search request may include the server 108 receiving a set of tokens and a set of synonyms. For example, a set of tokens can be obtained from the token database 120 by the processing module 110 of the server 108 via the data path 204 shown in FIG. 2. A set of tokens can be obtained from the synonym database 130 by the processing unit 110 through the data path 504 shown in FIG. 5.

[152] In some embodiments of the technical solution, the processing of each search query may include the server 108 calculating a set of normalized queries 400 shown in FIG. 4, based on search queries from a plurality of search queries 300 and tokens from a set of tokens.

[153] In other embodiments of the technical solution, the processing of each search request may include the calculation by server 108 of a set of generalized queries 600 shown in FIG. 6, based on normalized queries from a set of normalized queries 400 and synonyms from a set of synonyms. Processing module 110 may calculate a set of generalized queries 600 so that each normalized query from the set of normalized queries 400 is associated with a corresponding generalized query from the set of generalized queries 600.

[154] In further embodiments of the technical solution, the processing of each search request may include compilation by the server 108 of the index structure 706 shown in FIG. 7 and 8, based on the set of logical trees 808. In some embodiments of the technical solution, compiling the index structure 706 includes calculating by the server 108 the set of logical trees 808 shown in FIG. 8, wherein each logical tree from the set of logical trees 808 represents at least one generalized query associated with at least one corresponding normalized query.

[155] STEP 906: Getting the search phrase entered into the search engine

[156] The method 900 continues to block 906, where the server 108 obtains a search phrase entered into a search engine located on the server 108. In some embodiments of the technical solution, the server 108 may obtain a search phrase from the target content database 150. The search phrase may be associated with the advertisement, which allows you to establish the correspondence of the advertisement with the current search queries, which were previously entered by the user 102 and / or other potential users in the search engine service implemented by the server 108.

[157] In other embodiments of the technical solution, the search phrase was previously received by the server 108 from resources 180 of the target content. For example, target content resources 180 may provide the search engine service, which is implemented by server 108, target content data that includes: (i) data associated with a variety of advertisements, for example, promoting products and / or services, (ii) search phrases associated with each advertisement in a plurality of advertisements to establish correspondence of advertisements with current search queries entered into the search engine by user 102 and / or other potential all users of the search engine service and (iii) prices offered by the respective target content provider from a plurality of target content providers to display their advertisements to the user 102 and / or other potential users of the search engine service.

[158] Target content resources 180 may transmit to server 108 via network 106 a first data packet 194. The first data packet 194 may include target content data, which may include, inter alia, data related to the advertisement, as well as to a related related search phrase. In response to receiving the first data packet 194, the server 108 may be configured to store the target content data in the target content database 150.

[159] STEP 908: Processing a search phrase

[160] Method 900 ends at step 908, where server 108 processes the search phrase. For example, search phrase processing may be performed by processing module 110 of server 108.

[161] In some embodiments of the technical solution, the processing of the search phrase may include calculating a normalized search phrase based on the search phrase and the tokens in the token set. For example, processing module 110 may identify, using parsing, relevant words in a search phrase. Further, for each significant word in the search phrase, the processing module 110 may determine the corresponding token from the set of tokens. To this end, processing module 110 may retrieve a set of tokens from the token database 120. As a result, the processing unit 110 may amalgamate the corresponding tokens of each significant word in the search phrase to calculate a normalized search phrase. In further embodiments of the technical solution, the processing module 110 may store the search phrase, the normalized search phrase, and the normalized relationship between the search phrase and the normalized search phrase in the processing database 160 for further processing.

[162] In other embodiments of the technical solution, processing the search phrase may include calculating a generalized search phrase based on the normalized search phrase and synonyms in the set of synonyms. For example, processing module 110 may determine, for each token from a normalized search phrase, a corresponding primary synonym from a set of synonyms. To this end, processing module 110 may retrieve a set of synonyms from the synonym database 130. As a result, the processing module 110 can amalgamate the corresponding basic synonyms of each token from the normalized search phrase to calculate a generalized search phrase. In further embodiments of the technical solution, the processing module 110 may store the generalized search phrase, the normalized search phrase, and the generalized relationship between the generalized search phrase and the normalized search phrase in the processing database 160 for further processing.

[163] In further embodiments of the technical solution, the processing of the search phrase may include linking at least one normalized query from the set of normalized queries 400 (shown in FIG. 4) with the search phrase based on the generalized search phrase and index structure 706 (shown in Fig. 7). In some embodiments of the technical solution, at least one normalized query may differ from the normalized search phrase.

[164] To facilitate understanding, it is assumed that the search phrase is “procuring a car in Moscow”. In one example, a normalized search phrase may be "procure" "car" "Moscow". As a result, the generalized search phrase may be "buy" "car" "Moscow".

[165] In some embodiments of the technical solution, the server 108 may access the index structure 706 (see FIG. 8) in the index database 170. Further, the server 108 can access the first logical tree 802, since the generalized search phrase begins with the word "buy", and the parent node 822 of the first logical tree 802 is also the word "buy". Further, the server 108 can access the first level child node 824, which is “car,” and then access the second level child node 826, which is “Moscow,” since the generic search phrase includes “car” and "Moscow" after the word "buy". In other words, the server 108 can access the second level child node 826, which is “Moscow,” since the generic search phrase matches GQ1. Therefore, the server 108 may associate at least one normalized query, which is NQ1, NQ2, and NQ3, with a search phrase and a normalized search phrase.

[166] In response to the association between NQ1, NQ2 and NQ3 with the search phrase, server 108 may associate NQ1, NQ2 and NQ3 and the normalized search phrase with an advertisement associated with the search phrase. In some embodiments of the technical solution, the server 108 may store NQ1, NQ2, and NQ3 and a normalized search phrase in the target content database 150, and their corresponding associations with the advertisement associated with the search phrase. In other words, the advertisement may be associated with the search phrase, NQ1, NQ2, NQ3, and the normalized search phrase, respectively.

[167] In further embodiments of the technical solution, the server 108 may store GQ1 in the target content data base 150, and their association with the advertisement associated with the search phrase. In other words, the advertisement may be associated with a search phrase, NQ1, NQ2, NQ3, a normalized search phrase, and a generalized search phrase (i.e., in this case, the generalized search phrase corresponds to GQ1).

[168] In other embodiments of the technical solution, since in this case at least one normalized query (NQ1, NQ2, and NQ3) is different from the normalized search phrase, server 108 may further associate the normalized search phrase "procure" "car" "Moscow" with GQ1 (which is associated with the second level child node 826 representing "Moscow") in the first logical tree 802 of the index structure 706.

[169] In some embodiments of method 900, processing search queries from a plurality of search queries 300 and search phrases by server 108 may improve the mapping between search phrases and current search queries. In other words, improved matching can reduce the response time of the server 108 for delivering this advertisement to the user 102 and / or other potential users in response to a match between the given search phrase associated with this advertisement and this current search query entered by the user 102 and / or other potential users. In other embodiments of the technical solution, similar processing of search phrases and search queries can be performed offline, which can reduce the necessary bandwidth load on the network 106 by providing the current page of search results (SERP) of the search system located on the server 108. Next, processing search phrases and search queries may require less computational resources than the methods existing in the prior art, since the problem of a combinatorial explosion is solved when processing long search queries and / or long search phrases.

[170] Some of these steps, as well as signal transmission-reception, are well known in the art and, therefore, have been omitted in specific parts of this description for simplicity. Signals can be transmitted-received using optical means (for example, fiber optic connection), electronic means (for example, wired or wireless connection) and mechanical means (for example, based on pressure, temperature or other suitable parameter).

[171] Given the above-described embodiments of the technology that have been described and shown with reference to specific steps performed in a specific order, it should be borne in mind that these steps can be combined, separated, have a different execution order - all this does not go beyond the boundaries of this technology. Accordingly, the order and grouping of stages is not a limitation for the present technology.

[172] Those skilled in the art will understand that in the present description, the expression “receiving data” from a user means receiving by an electronic device or other electronic device data from the user in the form of an electronic (or other) signal. In addition, those skilled in the art will understand that displaying data to a user through a graphical user interface (for example, a screen of a computer device and the like) may include transmitting a signal to the graphical user interface, this signal contains data that can be processed, and at least a portion of this data may be displayed to the user via a graphical user interface.

[173] It is important to keep in mind that not all the technical results mentioned here may appear in each of the embodiments of the present technical solution. For example, embodiments of the present technical solution can be performed without the manifestation of some technical results, others can be performed with the manifestation of other technical results or without them.

[174] Modifications and improvements to the above-described embodiments of the present technical solution will be apparent to those skilled in the art. The preceding description is provided as an example only and is not subject to any restrictions. Thus, the scope of the present technical solution is limited only by the scope of the attached claims.

[175] Embodiments of the present technology can be summarized as follows, in numbered paragraphs:

[176] ITEM 1 A method (900) for processing search queries, a method (900) is executed on a server (108), a server (108) hosts a search engine, a method (900) includes:

a. receiving (902) by the server (108) a plurality of search queries (300) entered into the search engine; and

b. processing (904) by the server (108) of each search query in a plurality of search queries (300), processing (904) of each search query includes:

i. receiving by the server (108) a set of tokens and a set of synonyms;

ii. calculation by the server (108) of a set of normalized queries (400) based on search queries and tokens from the set of tokens;

iii. calculation by the server (108) of a set of generalized queries based on normalized queries from a set of normalized queries (400) and synonyms from a set of synonyms, with each normalized query associated with a corresponding generalized query from a set of generalized queries (600);

iv. compilation by the server (108) of the index structure (706) based on the set of logical trees (808), moreover, compilation of the index structure (706) includes the calculation by the server (108) of a set of logical trees (808), each logical tree from a set of logical trees ( 808) represents at least one generalized query associated with at least one normalized query.

[177] ITEM 2 The method (900) according to claim 1, further including:

a) the receipt (906) of the server (108) of the search phrase entered into the search engine;

b) processing (908) by the server (108) of the search phrase, processing (908) of the search phrase includes:

i) the server (108) calculating a normalized search phrase based on the search phrase and tokens from the set of tokens;

ii) the server (108) calculating a generalized search phrase based on a normalized search phrase and synonyms from a set of synonyms; and

iii) the server (108) associating at least one normalized query from the set of normalized queries (400) with a search phrase based on a generalized search phrase and index structure (706), at least one normalized query is different from a normalized search phrase.

[178] ITEM 3 The method (900) according to claim 2, wherein calculating a set of normalized queries (400) includes for each given search query from a plurality of search queries (300):

a) server (108) identifies the corresponding token from each significant word from the given search query;

b) the creation by the server (108) of this normalized query associated with this search query, this normalized query includes the corresponding token of each significant word from the given search query.

[179] ITEM 4 The method (900) according to claim 3, in which the calculation of the set of generalized queries (600) includes for each given normalized query from the set of normalized queries (400):

a) the server (108) identifies the corresponding basic synonym for each token from the given normalized query;

b) the creation by the server (108) of this generalized query associated with this normalized query, this generalized query includes the corresponding main synonym for each token from this normalized query.

[180] ITEM 5 The method (900) of claim 3, wherein the given normalized query is associated with a normalized frequency counter that indicates a series of search queries from a plurality of search queries (300) that are associated with this normalized query.

[181] ITEM 6 The method (900) of claim 4, wherein the generalized query is associated with a generalized frequency counter that indicates a series of normalized queries from the set of normalized queries (400) that are associated with this generalized query.

[182] ITEM 7 The method (900) of claim 3, wherein each significant word is a modified paradigm of the corresponding token.

[183] ITEM 8 The method (900) of claim 4, wherein the corresponding main synonym for each token is determined by the processing module (112) based on the corresponding indications of the popularity of synonyms from the set of synonyms.

[184] ITEM 9 The method (900) according to claim 2, wherein at least one logical tree from the set of logical trees (808) includes:

a) parent node (822);

b) at least one first level child node (824); and

from. at least one second level child node (826).

[185] ITEM 10 The method (900) of claim 9, wherein the at least one logical tree is at least one corresponding generalized query associated with at least one corresponding normalized query, wherein at least one generalized query is associated with at least one of:

a) parent node (822);

b) at least one first level child node (824); and

c) at least one child node (826) of the second level.

[186] ITEM 11 The method (900) of claim 2, further comprising:

a) the server (108) receiving the current search query entered into the search engine;

b) processing by the server (108) of the current search request, processing of the current search request includes:

i) the server (108) calculating a normalized current search query based on the current search query and tokens from the set of tokens;

ii) calculation by the server (108) of a generalized current search query based on the normalized current search query and synonyms from the set of synonyms; and

iii) linking by the server (108) of the normalized current search query to the target content related to the search phrase based on at least one of:

1) a normalized search phrase; and

2) at least one normalized query related to the search phrase.

[187] ITEM 12 The server (108) for processing search queries, the server (108) places a search engine, the server (108) is configured to perform the method (900) according to claims. 1-11.

Claims (78)

1. The method of processing search queries, the method is performed on the server, the server hosts the search engine, the method includes: - receipt by the server of a multitude of search queries entered into the search engine; and - server processing of each search query in a plurality of search queries, processing of each search query includes: i) receipt by the server of a set of tokens and a set of synonyms; ii) the server calculates a set of normalized queries based on search queries and tokens from the set of tokens; iii) computing by the server a set of generalized queries based on normalized queries from a set of normalized queries and synonyms from a set of synonyms, wherein each normalized query is associated with a corresponding generalized query from a set of generalized queries; iv) compiling the index structure by the server based on the set of logical trees, wherein compiling the index structure includes computing the set of logical trees by the server, each logical tree from the set of logical trees representing at least one generalized query associated with at least one normalized query . 2. The method according to p. 1, further comprising: - receipt by the server of the search phrase entered into the search system; - processing by the server of the search phrase, the processing of the search phrase includes: i) the server calculating a normalized search phrase based on the search phrase and tokens from the set of tokens; ii) the server calculating a generalized search phrase based on a normalized search phrase and synonyms from a set of synonyms; and iii) server linking at least one normalized query from a set of normalized queries to a search phrase based on a generalized search phrase and index structure, at least one normalized query is different from a normalized search phrase. 3. The method according to p. 2, in which the calculation of the set of normalized queries includes for each given search query from the set of search queries: - server identification of the corresponding token from each significant word from a given search query; - creation by the server of this normalized query associated with this search query, this normalized query includes the corresponding token of each significant word from this search query. 4. The method according to p. 3, in which the calculation of the set of generalized queries includes for each given normalized query from the set of normalized queries: - identification by the server of the corresponding basic synonym for each token from this normalized request; - creation by the server of this generalized request associated with this normalized request, this generalized request includes the corresponding main synonym for each token from this normalized request. 5. The method of claim 3, wherein the normalized request is associated with a normalized frequency counter that indicates a series of search queries from a plurality of search queries that are associated with this normalized request. 6. The method according to claim 4, in which the generalized query is associated with a generalized frequency counter, which indicates a series of normalized queries from a set of normalized queries that are associated with this generalized query. 7. The method according to p. 3, in which each significant word is a modified paradigm of the corresponding token. 8. The method according to claim 4, in which the corresponding main synonym for each token is determined by the processing module based on the corresponding indications of the popularity of synonyms from the set of synonyms. 9. The method of claim 2, wherein the at least one logical tree from the set of logical trees includes: - parent node; - at least one first level child node; and - at least one child node of the second level. 10. The method of claim 9, wherein the at least one logical tree is at least one corresponding generalized query associated with at least one corresponding normalized query, wherein at least one generalized query is associated with at least one of : - parent node; - at least one first level child node; and - at least one child node of the second level. 11. The method according to p. 2, further comprising: - receipt by the server of the current search query entered into the search system; - server processing the current search query, processing the current search query includes: i) the server calculating the normalized current search query based on the current search query and tokens from the set of tokens; ii) the server computes a generalized current search query based on the normalized current search query and synonyms from the set of synonyms; and iii) the server associating a normalized current search query with targeted content related to the search phrase based on at least one of: - a normalized search phrase; and - at least one normalized query related to the search phrase. 12. A server for processing search queries, the server hosts a search engine, the server is configured to: - receiving multiple search queries entered into the search engine; and - processing each search query from a plurality of search queries, and for processing each search query, the server is configured to: i) obtaining a set of tokens and a set of synonyms; ii) calculating a set of normalized queries based on search queries and tokens from a set of tokens; iii) calculating a set of generalized queries based on normalized queries from a set of normalized queries and synonyms from a set of synonyms, with each normalized query associated with a corresponding generalized query from a set of generalized queries; and iv) compiling the index structure based on the set of logical trees, to compile the index structure, the server is configured to compute the set of logical trees by the server, each logical tree from the set of logical trees being at least a normalized query. 13. The server according to p. 12, the server is additionally configured to perform: - getting the search phrase entered into the search engine; - processing of the search phrase, for processing the search phrase, the server is configured to: i) calculating a normalized search phrase based on the search phrase and tokens from the set of tokens; ii) calculating a generalized search phrase based on a normalized search phrase and synonyms from a set of synonyms; and iii) linking at least one normalized query from the set of normalized queries to a search phrase based on a generalized search phrase and index structure, at least one normalized query is different from a normalized search phrase. 14. The server according to claim 13, in which for calculating a set of normalized queries, the server is configured to implement for each given search query from a plurality of search queries: - identification of the corresponding token from each significant word from a given search query; - the creation of this normalized query associated with this search query, this normalized query includes the corresponding token of each significant word from this search query. 15. The server according to claim 14, in which to calculate the set of generalized requests, the server is configured to implement for each given normalized request from the set of normalized requests: - identification of the corresponding basic synonym for each token from the given normalized query; - creation of this generalized query associated with this normalized query, this generalized query includes the corresponding main synonym for each token from this normalized query. 16. The server of claim 14, wherein the normalized request is associated with a normalized frequency counter that indicates a series of search queries from a plurality of search queries that are associated with this normalized request. 17. The server according to claim 15, wherein the generalized request is associated with a generalized frequency counter that indicates a series of normalized requests from a set of normalized requests that are associated with this generalized request. 18. The server of claim 14, wherein each significant word is a modified paradigm of the corresponding token. 19. The server of claim 15, wherein the corresponding primary synonym of each token is determined based on the corresponding indications of the popularity of synonyms from the set of synonyms. 20. The server according to claim 13, in which at least one logical tree from a set of logical trees includes: - parent node; - at least one first level child node; and - at least one child node of the second level. 21. The server according to claim 20, in which at least one logical tree is at least one corresponding generalized query associated with at least one corresponding normalized query, and at least one generalized query is associated with at least one of : - parent node; - at least one first level child node; and - at least one child node of the second level. 22. The server according to claim 13, in which the server is also configured to: - receiving the current search query entered into the search engine; - processing the current search request, to process the current search request, the server is configured to: i) calculating the normalized current search query based on the current search query and tokens from the set of tokens; ii) calculating a generalized current search query based on a normalized current search query and synonyms from a set of synonyms; and iii) associating a normalized current search query with targeted content related to the search phrase based on at least one of: - a normalized search phrase; and - at least one normalized query related to the search phrase.

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