CN118170816B - Digital library information retrieval method, system and medium based on business space granularity - Google Patents
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Abstract
The invention discloses a digital library information retrieval method, a system and a medium based on business space granularity; relates to the technical field of information retrieval; constructing an intention database combination according to the user retrieval history data, primarily screening the databases commonly used by the user, eliminating the databases which are not used at all, avoiding the need of traversing and searching all shared databases in the retrieval process, avoiding the invalid search process of other irrelevant databases except the intention database combination, and improving the retrieval efficiency; meanwhile, the scheme solves the user retrieval problem in a quotient space problem solving mode; the database in the target database combination is subjected to hierarchical search from coarse to fine and step-by-step refinement, so that the resource data association in the database is realized, and compared with the searching mode of the traditional searching platform total resource library, the searching efficiency and the searching accuracy of a user are greatly improved.
Description
Technical Field
The invention relates to the technical field of information retrieval, in particular to a digital library information retrieval method, system and medium based on business space granularity.
Background
The digital library is a product of time transformation, changes the traditional information acquisition, storage and service modes for adapting to the development and application characteristics of digital technologies, realizes the quality upgrading of the operation of the digital library, ensures that the information service function is more and more abundant, and meets the urgent requirements of various fields on the application of information resources; electronic information resources such as various knowledge databases, digital libraries, self-built databases and the like enrich the knowledge surface of people and widen the way for readers to find information; however, the richness of the information resources of the digital library can bring certain challenges besides relieving the contradiction between information supply and demand.
There is a great difference between the retrieval systems of digital resources, and in order to use different retrieval systems, users need to spend a certain time to adapt and become familiar; the libraries of scientific research institutions and institutions usually have tens or hundreds of databases which can be shared for selection, users need to select databases corresponding to topics when searching information, and also need to spend a great deal of time to know search definitions in each database and familiarize with interface rules in each database; the user search information often needs to be searched repeatedly in a plurality of databases, most of resource records in the databases are independent, the correlation is not high, the method for sharing the databases can realize unified search, but the feedback results after each database website completes search are required to uniformly display the search results, the search efficiency is slow, the information exchange cannot be realized among the databases, and the repeated contents exist in most of document databases, so that the search efficiency of the user is influenced.
Disclosure of Invention
Aiming at the problems of slow retrieval efficiency of the digital library and independence among various resource data, the scheme provides a digital library information retrieval method, a system and a medium based on business space granularity; the method is improved on the basis of the existing shared sub-database retrieval technology, unnecessary retrieval is not required to be developed for other irrelevant databases except the search database combination in the retrieval process, and the retrieval efficiency is improved; solving a user retrieval problem in a quotient space problem solving mode; the database in the target database combination is subjected to hierarchical search from coarse to fine and step-by-step refinement, so that the resource data association in the database is realized, and compared with the searching mode of the traditional searching platform total resource library, the searching efficiency and the searching accuracy of a user are greatly improved.
The invention is realized by the following technical scheme:
the scheme provides a digital library information retrieval method based on business space granularity, which comprises the following steps:
Step one: collecting user retrieval history data;
Step two: based on the user retrieval history data, screening the database weights to obtain a target database combination;
Step three: and acquiring user retrieval requirements, and performing quotient space granularity calculation in a target database combination based on the user retrieval requirements to obtain target resource data.
The working principle of the scheme is as follows: aiming at the problems of slow retrieval efficiency of the digital library and independence among databases, the scheme provides the digital library information retrieval method based on the business space granularity, which is an improvement on the retrieval method based on the existing shared sub-database retrieval technology:
On the one hand, an intention database combination is constructed according to the user retrieval history data, the databases which are commonly used by the user are primarily screened out, the databases which are not used at all are removed, and all shared databases are not required to be searched through traversal in the retrieval process, so that the invalid search process of other irrelevant databases except the intention database combination is avoided, and the retrieval efficiency is improved; as for the database which is rejected, whether individual searching is needed or not can be judged according to the rope result by the user, and searching is further expanded according to a custom mode.
On the other hand, the scheme also improves the searching method in the searching process level of the target resource data; solving a user retrieval problem in a problem solving mode of a quotient space; by hierarchical database in the target database combination, searching is performed from coarse to fine and step by step, and compared with the searching mode of the traditional searching platform total resource library, the searching efficiency of a user is greatly improved.
The further optimization scheme is that the method for screening the database weights based on the user retrieval history data to obtain the target database combination comprises the following steps:
t1, acquiring a user retrieval log, and extracting an effective retrieval record from the user retrieval log; the effective search includes a search process having one or more search result outputs;
t2, obtaining the corresponding use database of each effective search; the usage database includes a database having operation actions on search results, the operation actions including: clicking, copying, browsing or downloading;
t3, constructing action database combination of user u based on the corresponding usage databases of each effective search ：
；
Wherein,Representing an nth database; represented by vectors; A weight representing an nth database;
t4, combining action databases And deleting all elements with the weight less than the weight threshold T to obtain the target database combination of the user u.
Further optimizing scheme is that the weight of the nth databaseCalculated according to the following formula:
Wherein, The total browsing amount and total download amount representing the search result of the effective search i,Representing efficient retrieval i in a databaseThe maximum downloading amount of the retrieval result in the process is represented by t, wherein the maximum downloading amount coefficient is 1.1-0.98; Representing the total number of search results for an effective search i, Representation databaseThe total number of search results contained in the search result list.
The further optimization scheme is that the method for calculating the quotient space granularity in the target database combination based on the user retrieval requirement to obtain target resource data comprises the following steps:
s1, representing a target database combination by using a graph vector, and representing the graph vector by using a triplet;
S2, performing rapid granulation operation on the triples to obtain a search quotient space model:
acquiring quotient space granularity from a domain direction, an attribute function direction or a granularity direction of a quotient space structure; granulating the domain X and the topological structure T respectively under the condition of not changing the attribute function f of the domain;
and S3, obtaining target resource data according to the space model of the retriever.
In a further optimized scheme, the step S1 comprises the following substeps:
S11, each database in the target database combination is represented by a graph g= (V, E), where the set v= (V 1,v2,… ,vn) represents a set of resource data nodes in the database, and n represents the total number of resource data nodes; set e= (E 1,e2,… ,em) represents a set of resource data nodes associated with the retrieval demand, m is the total number of resource data nodes associated with the retrieval demand;
S12, representing the graph g= (V, E) with triples (X, f, T), where the argument X represents the set V; f represents the associated attribute information of the resource data node and the retrieval requirement; topology T represents set E.
The method for granulating the domain X comprises the following steps of:
initializing each resource data node into a particle, and numbering each particle according to the ascending order of the node degree;
Traversing each particle in sequence according to the steps a-b until nodes contained by all particles are unchanged:
Step a, deleting the current node from the particles to which the current node belongs, and calculating node degree increment delta Q after the current node is respectively added into each adjacent particle;
Step b, judging whether the value of the maximum node degree increment delta Qmax is larger than 0; if yes, adding the current node into the adjacent particles corresponding to the maximum node degree increment delta Qmax; otherwise, the current node is put back to the original home particle.
The further optimization scheme is that the method for granulating the topological structure T comprises the following steps:
Each particle is subjected to two-by-two edge connection: for the particles a and b, the topological structure T ab is that all nodes corresponding to the particles a are connected with all nodes corresponding to the particles b in pairs;
Calculating the sum of weights of node lines between a particles and b particles ; A sum of weights of all node wires inside particleB the sum of the weights of all node wires inside the particle；
Construction of particle association coefficients：
;
Wherein t represents the weight of the current database;
When (when) When the topology structure T ab is reserved as the topology structure after granulationOtherwise, the topology T ab is deleted.
The further optimization scheme is that a grain layer with the highest node degree value in the search quotient space model is obtained, and resource data corresponding to each particle contained in the grain layer is output.
The scheme also provides a digital library information retrieval system based on the business space granularity, which is used for realizing the digital library information retrieval method based on the business space granularity, and comprises the following steps:
the acquisition module is used for acquiring user retrieval history data;
The database combination construction module is used for screening the database weights based on the user retrieval history data to obtain a target database combination;
and the calculation module is used for acquiring the user retrieval requirement, and calculating the business space granularity in the target database combination based on the user retrieval requirement to obtain the target resource data.
The present solution also provides a computer readable medium having stored thereon a computer program for execution by a processor to implement a method of digital library information retrieval based on a business space granularity as described above.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention provides a digital library information retrieval method, a system and a medium based on business space granularity; constructing an intention database combination according to the user retrieval history data, primarily screening the databases commonly used by the user, eliminating the databases which are not used at all, avoiding the need of traversing and searching all shared databases in the retrieval process, avoiding the invalid search process of other irrelevant databases except the intention database combination, and improving the retrieval efficiency; as for the removed database, whether individual searching is needed or not can be judged according to the request result by the user, and searching is further expanded according to a user-defined mode;
2. The invention provides a digital library information retrieval method, a system and a medium based on business space granularity; the searching method is improved on the searching process level of the target resource data; solving a user retrieval problem in a problem solving mode of a quotient space; the database in the target database combination is subjected to hierarchical search from coarse to fine and step-by-step refinement, so that the resource data association in the database is realized, and compared with the searching mode of the traditional searching platform total resource library, the searching efficiency and the searching accuracy of a user are greatly improved.
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In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a digital library information retrieval method based on business space granularity;
FIG. 2 is a schematic diagram of a digital library information retrieval system based on business space granularity;
FIG. 3 is a comparative diagram of two search speeds of example 3.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Aiming at the problems of slow retrieval efficiency of a digital library and independence among various resource data, the scheme provides the following embodiments for solving.
Example 1: the present embodiment provides a digital library information retrieval method based on business space granularity, as shown in fig. 1, including:
Step one: collecting user retrieval history data; in this step, the search history data mainly aims at the search data of the user, including the history search information in the databases commonly used by the users in the digital library, the public library and the like in each large search website, and the history search information needs to include detailed information such as search logs, search time, search results and the like.
Step two: based on the user retrieval history data, screening the database weights to obtain a target database combination; the method specifically comprises the following steps:
T1, acquiring a user retrieval log, and extracting an effective retrieval record from the user retrieval log; the effective search includes a search process having one or more search result outputs; the history search information contains a large amount of invalid data such as invalid data in a search process without results, a search process with errors, and the like, so that the history search information needs to be preprocessed to remove the invalid data.
T2, obtaining the corresponding use database of each effective search; the usage database includes a database having operation actions on search results, the operation actions including: clicking, copying, browsing or downloading;
t3, constructing action database combination of user u based on the corresponding usage databases of each effective search ：;
Representing an nth database; represented by vectors; A weight representing an nth database;
Weights of the nth database Calculated according to the following formula:
Wherein, The total browsing amount and total download amount representing the search result of the effective search i,Representing an effective search i in the database; t represents a maximum downloading amount coefficient; The maximum download amount of the search result, Representing the total number of search results for an effective search i,Representation databaseThe total number of search results contained in the search result list. In this step of the present embodiment, t is taken to be 1.06.
T4, combining action databasesAnd deleting all elements with the weight less than the weight threshold T to obtain the target database combination of the user u.
In this step, the weights of the databaseThe calculation method is obtained by carrying out multiple fitting calculation on a large amount of search result data, in order to ensure the accuracy of target database combination, the weight threshold T is set in advance according to experience to serve as a screening condition, elements meeting the screening condition are reserved, and elements not meeting the screening condition are deleted.
Step three: and acquiring user retrieval requirements, and performing quotient space granularity calculation in a target database combination based on the user retrieval requirements to obtain target resource data. The method specifically comprises the following steps:
S1, representing a target database combination by using a graph vector, and representing the graph vector by using a triplet; the method specifically comprises the following substeps:
S11, each database in the target database combination is represented by a graph g= (V, E), where the set v= (V 1,v2,… ,vn) represents a set of resource data nodes in the database, and n represents the total number of resource data nodes; set e= (E 1,e2,… ,em) represents a set of resource data nodes associated with the retrieval demand, m is the total number of resource data nodes associated with the retrieval demand;
S12, representing the graph g= (V, E) with triples (X, f, T), where the argument X represents the set V; f represents the associated attribute information of the resource data node and the retrieval requirement; topology T represents set E;
S2, performing rapid granulation operation on the triples to obtain a search quotient space model:
Acquiring quotient space granularity from a domain direction, an attribute function direction or a granularity direction of a quotient space structure; the domain X and the topology T are granulated separately without changing the attribute function f of the domain.
The method for granulating the discourse domain X comprises the following steps:
initializing each resource data node into a particle, and numbering each particle according to the ascending order of the node degree;
Traversing each particle in sequence according to the steps a-b until nodes contained by all particles are unchanged:
Step a, deleting the current node from the particles to which the current node belongs, and calculating node degree increment delta Q after the current node is respectively added into each adjacent particle;
Step b, judging whether the value of the maximum node degree increment delta Qmax is larger than 0; if yes, adding the current node into the adjacent particles corresponding to the maximum node degree increment delta Qmax; otherwise, the current node is put back to the original home particle.
The method for granulating the topological structure T comprises the following steps:
Each particle is subjected to two-by-two edge connection: for the particles a and b, the topological structure T ab is that all nodes corresponding to the particles a are connected with all nodes corresponding to the particles b in pairs;
Calculating the sum of weights of node lines between a particles and b particles ; A sum of weights of all node wires inside particleB the sum of the weights of all node wires inside the particle；
Construction of particle association coefficients：
Wherein t represents the weight of the current database;
When (when) When the topology structure T ab is reserved as the topology structure after granulationOtherwise, the topology T ab is deleted.
S3, obtaining target resource data according to the search quotient space model: and acquiring a grain layer with the highest node degree value in the search quotient space model, and outputting resource data corresponding to each particle contained in the grain layer.
According to the scheme, the intention database combination is constructed according to the user retrieval history data, the databases commonly used by the users are primarily screened out, the databases which are not used at all are removed, and all shared databases do not need to be searched through traversal in the retrieval process, so that the invalid search process of other irrelevant databases except the intention database combination is avoided, and the retrieval efficiency is improved; as for the removed database, whether individual searching is needed or not can be judged according to the request result by the user, and searching is further expanded according to a user-defined mode; the searching method is improved on the searching process level of the target resource data; solving a user retrieval problem in a problem solving mode of a quotient space; the database in the target database combination is subjected to hierarchical search from coarse to fine and step-by-step refinement, so that the resource data association in the database is realized, and compared with the searching mode of the traditional searching platform total resource library, the searching efficiency and the searching accuracy of a user are greatly improved.
Example 2: the present embodiment provides a digital library information retrieval system based on business space granularity, for implementing the digital library information retrieval method based on business space granularity of embodiment 1, as shown in fig. 2, the system includes:
the acquisition module is used for acquiring user retrieval history data;
The database combination construction module is used for screening the database weights based on the user retrieval history data to obtain a target database combination;
and the calculation module is used for acquiring the user retrieval requirement, and calculating the business space granularity in the target database combination based on the user retrieval requirement to obtain the target resource data.
Example 3: the present embodiment provides a computer readable medium having stored thereon a computer program that is executed by a processor to implement the method for retrieving digital library information based on business space granularity as in embodiment 1. The method specifically comprises the following steps:
Step one: collecting user retrieval history data;
Step two: based on the user retrieval history data, screening the database weights to obtain a target database combination;
Step three: and acquiring user retrieval requirements, and performing quotient space granularity calculation in a target database combination based on the user retrieval requirements to obtain target resource data.
In order to effectively verify the effect of the scheme, the embodiment utilizes part of library resources to construct a small-sized resource library experiment, and utilizes a quotient space granularity calculation algorithm to realize the retrieval of a resource library retrieval system engine. The whole resource total library is respectively set to be divided into three databases, and each database comprises 400, 500 and 600 nodes. The final search time results obtained by the conventional resource library search method and the two search methods of the embodiment are shown in fig. 3, wherein the final search time results obtained by the search method of the embodiment are about 20s, 25s and 36s, the final search time results are connected to obtain a straight line a, the final search time results obtained by the conventional resource library search method are 37s, 44s and 49s, and the final search time results are connected to obtain a straight line B.
According to experimental results, the searching mode of gradually asking for the essence from the thick to the thin in the scheme has the advantages that the searching area in the resource library is reduced in height, the searching area of the required searching content is defined, and according to the straight line A and the straight line B, the resource searching efficiency is obviously improved compared with the searching time required by the traditional searching mode.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The digital library information retrieval method based on the business space granularity is characterized by comprising the following steps of:
Step one: collecting user retrieval history data;
step two: based on the user retrieval history data, screening the database weights to obtain a target database combination; the method specifically comprises the following steps:
t1, acquiring a user retrieval log, and extracting an effective retrieval record from the user retrieval log; the effective search includes a search process having one or more search result outputs;
t2, obtaining the corresponding use database of each effective search; the usage database includes a database having operation actions on search results, the operation actions including: clicking, copying, browsing or downloading;
t3, constructing action database combination of user u based on the corresponding usage databases of each effective search ：
;
Representing an nth database; represented by vectors; A weight representing an nth database;
t4, combining action databases Deleting all elements with the middle weight smaller than the weight threshold T to obtain a target database combination of the user u;
step three: acquiring a user retrieval demand, and performing quotient space granularity calculation in a target database combination based on the user retrieval demand to obtain target resource data;
The method specifically comprises the following steps:
s1, representing a target database combination by using a graph vector, and representing the graph vector by using a triplet;
S2, performing rapid granulation operation on the triples to obtain a search quotient space model:
acquiring quotient space granularity from a domain direction, an attribute function direction or a granularity direction of a quotient space structure; granulating the domain X and the topological structure T respectively under the condition of not changing the attribute function f of the domain;
and S3, obtaining target resource data according to the space model of the retriever.
2. The method for retrieving digital library information based on business space granularity as claimed in claim 1, wherein the weight of the nth databaseCalculated according to the following formula:
; wherein, The sum of the total browsing amount and the total download amount of the search result representing the effective search i,Representing efficient retrieval i in a databaseThe maximum downloading amount of the retrieval result, and t represents the maximum downloading amount coefficient; Representing the total number of search results for an effective search i, Representation databaseThe total number of search results contained in the search result list.
3. The method for retrieving digital library information based on business space granularity as claimed in claim 1, wherein the step S1 comprises the sub-steps of:
s11, each database in the target database combination is represented by a graph g= (V, E), where the set v= (V 1,v2,… ,vn) represents a set of resource data nodes in the database, and n represents the total number of resource data nodes; set e= (E 1,e2,… ,em) represents a set of resource data nodes associated with the retrieval demand, m is the total number of resource data nodes associated with the retrieval demand;
S12, representing the graph g= (V, E) with triples (X, f, T), where the argument X represents the set V; f represents the associated attribute information of the resource data node and the retrieval requirement; topology T represents set E.
4. A digital library information retrieval method based on a business space granularity as claimed in claim 3, wherein the method of granulating the domain X comprises:
initializing each resource data node into a particle, and numbering each particle according to the ascending order of the node degree;
Traversing each particle in sequence according to the steps a-b until nodes contained by all particles are unchanged:
Step a, deleting the current node from the particles to which the current node belongs, and calculating node degree increment delta Q after the current node is respectively added into each adjacent particle;
Step b, judging whether the value of the maximum node degree increment delta Qmax is larger than 0; if yes, adding the current node into the adjacent particles corresponding to the maximum node degree increment delta Qmax; otherwise, the current node is put back to the original home particle.
5. The method for retrieving digital library information based on business space granularity as claimed in claim 4, wherein the method for granulating the topology T comprises:
Each particle is subjected to two-by-two edge connection: for the particles a and b, the topological structure T ab is that all nodes corresponding to the particles a are connected with all nodes corresponding to the particles b in pairs;
Calculating the sum of weights of node lines between a particles and b particles ; A sum of weights of all node wires inside particleB the sum of the weights of all node wires inside the particle；
Construction of particle association coefficients：
Wherein t represents the weight of the current database;
When (when) When the topology structure T ab is reserved as the topology structure after granulationOtherwise, topology T ab is deleted.
6. The method for searching digital library information based on the business space granularity according to claim 4, wherein a grain layer with the highest node degree value in the search business space model is obtained, and resource data corresponding to each particle contained in the grain layer is output.
7. A digital library information retrieval system based on business space granularity for implementing the digital library information retrieval method based on business space granularity as claimed in any one of claims 1 to 6, the system comprising:
the acquisition module is used for acquiring user retrieval history data;
The database combination construction module is used for screening the database weights based on the user retrieval history data to obtain a target database combination;
and the calculation module is used for acquiring the user retrieval requirement, and calculating the business space granularity in the target database combination based on the user retrieval requirement to obtain the target resource data.
8. A computer readable medium having stored thereon a computer program, wherein the computer program is executable by a processor to implement a method of retrieving digital library information based on a business space granularity as claimed in any one of claims 1-6.
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