CN113190692B - Self-adaptive retrieval method, system and device for knowledge graph - Google Patents
Self-adaptive retrieval method, system and device for knowledge graph Download PDFInfo
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Abstract
The invention relates to the technical field of computers, in particular to a method, a system and a device for self-adaptive retrieval of a knowledge graph; the method comprises the steps of 1) inputting a first retrieval instruction, decoding the first retrieval instruction, and obtaining a feature code contained in the first retrieval instruction and a weight value corresponding to the feature code; 2) acquiring a link relation according to the weight value, and acquiring a three-dimensional map according to the link relation; 3) presenting the three-dimensional map to a display device, and marking the three-dimensional map with the weight values contained in the retrieval; 4) and inputting a second retrieval instruction, wherein the second retrieval instruction carries out clicking or touch operation on any position of the common mark of the three-dimensional graph, and outputs a plurality of groups of display results in parallel according to a display rule. Through searching, the corresponding three-dimensional map is firstly presented on the display device, and the user can perform secondary searching according to the searching result highlighted by the three-dimensional map so as to obtain the interested result and the associated result.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a knowledge graph retrieval technology, and specifically relates to a self-adaptive retrieval method, a self-adaptive retrieval system and a self-adaptive retrieval device for a knowledge graph.
Background
The knowledge map is a knowledge domain visualization or knowledge domain mapping map, and is a series of different graphs for displaying the relationship between the knowledge development process and the structure. With the development of the information age and the development of big data, learning knowledge points by using a knowledge graph has become a scientific learning mode, and in the prior art, for example, the publication numbers are as follows: "CN 109947952A" discloses a retrieval method, device, equipment and storage medium based on english knowledge graph, the method includes: inquiring corresponding inquiry suggestion information in an index file according to key word information extracted from English knowledge information to be retrieved, wherein the inquiry suggestion information comprises individual category information such as grammar, sentences, phrases, vocabularies, titles, common errors and multimedia files; inquiring reference retrieval English knowledge information with derivation relation, composite relation and inclusion relation in a file library based on the body webpage language according to the inquiry suggestion information, wherein the reference retrieval English knowledge information comprises wiki vocabulary information, fixed collocation information, example sentences and translation information thereof, resource information and knowledge point information; and extracting, grouping and sequencing according to the query result to obtain English knowledge map information.
The above disclosed technique actually utilizes an individual classification search, which includes: the English knowledge search system comprises a plurality of English subjects, wherein each English subject comprises a grammar, a sentence, a phrase, words, a question, common errors, multimedia files and the like, each English subject comprises a plurality of individual words, such as a word, and the individual words comprise a plurality of attributes, such as phonetic symbols, parts of speech, usage, example sentences, grades, related books, whether the English subject is four-six-grade words or not, the number of times of a tested place and the like, the preset index file is a corresponding relation between preset keyword information and individual category information, and the corresponding individual category information can be inquired according to English knowledge information to be searched, which is input by a user.
For example, the search can be performed in four ways:
1. and inquiring the operator, wherein the keywords are in a relation of AND and adopt a form of + word1+ word 2; 2. an or query operator, wherein the keywords are in an OR relationship, a word1 word2 form 3 and a not query operator are adopted, and a + word1-word2 form is adopted when a certain keyword is excluded; 4. a like query operator, fuzzy query, in the form of word-pairs.
The above-mentioned searching method is complicated, and the searching result is not more accurate under rich conditions.
The technical means also have the following publication numbers: technical literature of "CN 109977291A".
If the publication number is: "CN 112528046A" discloses a method and a device for constructing a new knowledge graph and a method and a device for retrieving information, and particularly discloses a method for constructing a new knowledge graph, which is characterized by comprising the following steps: acquiring an original knowledge graph, wherein the original knowledge graph at least comprises a first entity object and a second entity object; acquiring text information based on the first entity object; wherein the text information comprises comment information and/or description information of the first entity object; according to the text information, identifying a second entity object having an incidence relation with the text information; and establishing the incidence relation between the text information and the first entity object and the second entity object respectively in the original knowledge graph so as to construct a new knowledge graph.
In the above-mentioned technology, the obtained knowledge graph is only a simple set, and only the relation of the knowledge graph in the set can be reflected.
Disclosure of Invention
The invention aims to provide a method, a system and a device for self-adaptive retrieval of knowledge graph, which aim to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a self-adaptive retrieval method of knowledge graph comprises the following steps:
1) inputting a first retrieval instruction, decoding the first retrieval instruction, and acquiring a feature code contained in the first retrieval instruction and a weight value corresponding to the feature code;
2) acquiring a link relation according to the weight value, and acquiring a three-dimensional map according to the link relation;
3) presenting the three-dimensional map to a display device, and marking the three-dimensional map with the weight values contained in the retrieval;
4) and inputting a second retrieval instruction, wherein the second retrieval instruction carries out clicking or touch operation on any position of the common mark of the three-dimensional graph, and a plurality of groups of display results are output in parallel according to a display rule.
Preferably, in step 3), marking the three-dimensional atlas with the weight values included in the search includes:
acquiring a corresponding weight value on the three-dimensional atlas,
extracting coordinates corresponding to the weighted values on the three-dimensional map by the weighted values,
and controlling the display buoy corresponding to the coordinates to perform highlighting.
Preferably, in step 4), outputting multiple groups of display results in parallel according to the display rule includes:
acquiring coordinates corresponding to the highlighted display buoy and a corresponding weight value;
acquiring a plane map corresponding to the coordinate on the three-dimensional map;
acquiring a corresponding knowledge set in each knowledge set according to the weight value;
acquiring association codes according to the weight values, and acquiring corresponding knowledge subsets on the knowledge set by the associated codes;
and sending the knowledge units corresponding to the weight values in the knowledge subsets to a display device for displaying.
The invention also provides a self-adaptive retrieval system of the knowledge graph, which comprises
A knowledge subset forming module for acquiring a plurality of knowledge units, preprocessing the knowledge units, acquiring at least one knowledge subset contained in the knowledge units, a feature code corresponding to each knowledge subset, an association code corresponding to each feature code, and a weight value of each feature code,
the first storage module is used for storing the feature codes corresponding to each knowledge subset, the association codes corresponding to each feature code and the weight value of each feature code;
the classification module is used for configuring classification attributes and classification rules corresponding to the classification attributes, writing the feature codes into the classification attributes, and setting and enabling the knowledge subsets to be classified to have the same classification attributes so that the knowledge subsets can be distributed to corresponding classification units by the classification rules;
the knowledge set forming module is used for arranging the knowledge subsets under the same classification unit by using the weight values of the feature codes and associating the arranged knowledge subsets under the same classification unit by using the association codes to form a knowledge set;
the second storage module is used for storing the knowledge set;
the plane map forming module is used for mapping the ownership weight value of each knowledge set to the plane map;
the three-dimensional map forming module is used for linking the plurality of plane maps according to the weight values to form a three-dimensional map;
the third storage module is used for storing the three-dimensional map and the corresponding link relation of the three-dimensional map;
the user server is used for receiving the retrieval command sent by the user, acquiring the retrieval command, carrying out corresponding response, and transmitting the execution command to the data server group after the response;
the data server group receives the execution command, starts the retrieval executor to determine the corresponding feature code and the weight value corresponding to the feature code in the executed command through at least one of the first execution command or the second execution command so as to search the corresponding knowledge unit in the storage server group,
and the display module is used for displaying the retrieved knowledge units.
Further, the data server group comprises a first storage module, a second storage module and a third storage module, and comprises a link path among the first storage module, the second storage module and the third storage module.
Further, still include: a retrieval module for obtaining a first retrieval instruction,
and the decoding module is used for decoding the input first retrieval instruction to obtain the feature codes contained in the first retrieval instruction and the weight values corresponding to the feature codes.
Further, the link path includes:
a classification path established by the feature code;
an arrangement path is established by the weight value corresponding to the feature code;
the associated path is established by the associated code corresponding to the characteristic code;
mapping the ownership weight value of the knowledge set into a mapping path of the planar atlas;
the planar atlas is linked by the weight value to form the link relation of the three-dimensional atlas,
and the first storage module, the second storage module and the third storage module form a physical path in the data server group.
The invention also provides a self-adaptive retrieval device of the knowledge graph, which comprises
Input means for inputting a first search command or a second search command;
the user server receives a first retrieval command or a second retrieval command input by the input device and responds to the first retrieval command or the second retrieval command;
the data server group receives the execution command, starts the retrieval executor to determine the feature code corresponding to the execution command and the weight value corresponding to the feature code through at least one of the first execution command or the second execution command so as to search the corresponding knowledge unit in the storage server group;
and the display device is used for displaying the searched knowledge units.
Further, the input device includes:
a user client, and an input component possessed by the user client.
Further, the data server group comprises a first storage module, a second storage module and a third storage module, and comprises a link path among the first storage module, the second storage module and the third storage module;
the link path includes:
a classification path established by the feature code;
an arrangement path is established by the weight value corresponding to the feature code;
the associated path is established by the associated code corresponding to the characteristic code;
mapping the ownership weight value of the knowledge set into a mapping path of the plane map;
the planar atlas is linked by the weight value to form the link relation of the three-dimensional atlas,
and the first storage module, the second storage module and the third storage module form a physical path in the data server group.
Compared with the prior art, the invention has the beneficial effects that:
firstly, the method comprises the following steps: the knowledge unit is preprocessed to obtain at least one knowledge subset contained in the knowledge unit, feature codes corresponding to each knowledge subset, association codes corresponding to each feature code and weight values of each feature code, the knowledge subsets with the same feature codes are classified by the knowledge subsets, then the knowledge subsets with the same class form a knowledge set, the knowledge set is mapped to form a plane atlas by using the weight values of the knowledge set, and a three-dimensional atlas is formed by using the plane atlas, so that all the knowledge units with the same feature codes are subjected to three-dimensional simulation storage.
Secondly, the method comprises the following steps: through searching, the corresponding three-dimensional map is firstly presented on the display device, and the user can perform secondary searching according to the searching result highlighted by the three-dimensional map so as to obtain the interested result and the associated result.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic flow diagram of the system of the present invention;
FIG. 3 is an exemplary schematic of a plan view of the system of the present invention;
FIG. 4 is a schematic view of the connection of the apparatus of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific embodiments.
Referring to fig. 1, the invention provides a self-adaptive knowledge graph retrieval method, which comprises the following steps:
1) inputting a first retrieval instruction, decoding the first retrieval instruction, and acquiring a feature code contained in the first retrieval instruction and a weight value corresponding to the feature code;
2) acquiring a link relation according to the weight value, and acquiring a three-dimensional map according to the link relation;
3) presenting the three-dimensional map to a display device, and marking the three-dimensional map with the weight values contained in the retrieval;
4) and inputting a second retrieval instruction, wherein the second retrieval instruction carries out clicking or touch operation on any position of the common mark of the three-dimensional graph, and a plurality of groups of display results are output in parallel according to a display rule.
In step 3), marking the three-dimensional atlas with the weight values included in the search includes:
acquiring a corresponding weight value on the three-dimensional atlas,
extracting coordinates corresponding to the weighted values on the three-dimensional map by the weighted values,
and controlling the display buoy corresponding to the coordinates to perform highlighting.
In step 4), outputting a plurality of groups of display results in parallel according to the display rule comprises:
acquiring coordinates corresponding to the highlighted display buoy and a corresponding weight value;
acquiring a plane map corresponding to the coordinate on the three-dimensional map;
acquiring a corresponding knowledge set in each knowledge set according to the weight value;
acquiring association codes according to the weight values, and acquiring corresponding knowledge subsets on the knowledge set by the associated codes;
and sending the knowledge units corresponding to the weight values in the knowledge subsets to a display device for displaying.
Referring to fig. 2, the invention also provides an adaptive knowledge graph retrieval system, which comprises
A knowledge subset forming module for acquiring a plurality of knowledge units, preprocessing the knowledge units, acquiring at least one knowledge subset contained in the knowledge units, a feature code corresponding to each knowledge subset, an association code corresponding to each feature code, and a weight value of each feature code,
the first storage module is used for storing the feature codes corresponding to each knowledge subset, the association codes corresponding to each feature code and the weight value of each feature code;
the classification module is used for configuring classification attributes and classification rules corresponding to the classification attributes, writing the feature codes into the classification attributes, and setting and enabling the knowledge subsets to be classified to have the same classification attributes so that the knowledge subsets can be distributed to corresponding classification units by the classification rules;
the knowledge set forming module is used for arranging the knowledge subsets under the same classification unit by using the weight values of the feature codes and associating the arranged knowledge subsets under the same classification unit by using the association codes to form a knowledge set;
the second storage module is used for storing the knowledge set;
the plane map forming module is used for mapping the ownership weight value of each knowledge set to the plane map;
the three-dimensional map forming module is used for linking the plurality of plane maps according to the weight values to form a three-dimensional map;
the third storage module is used for storing the three-dimensional map and the corresponding link relation of the three-dimensional map;
the user server is used for receiving the retrieval command sent by the user, acquiring the retrieval command, carrying out corresponding response, and transmitting the execution command to the data server group after the response;
the data server group receives the execution command, starts the retrieval executor to determine the corresponding feature code and the weight value corresponding to the feature code in the executed command through at least one of the first execution command or the second execution command so as to search the corresponding knowledge unit in the storage server group,
and the display module is used for displaying the retrieved knowledge units.
It should be noted that, the corresponding feature codes in each knowledge subset include at least one, in different knowledge subsets, the feature codes have the conditions of whether the feature codes are completely consistent, multiple consistency, one consistency, and the like, when writing the classification attribute, all the feature codes in the same knowledge subset are written into the classification attribute, and then the knowledge subsets are associated through the association codes, so that the knowledge subsets with the same feature codes are distributed into the same classification unit,
the multiple consistency, one consistency and the like are distributed to other classification units, so that the knowledge sets formed by different classification units form a coincidence point when mapping is carried out, the coincidence point is that the single feature codes of the knowledge subsets in different classification units are consistent, and the coincidence point can be responded by setting the weight value of the feature codes. It should be noted that, because the coincidence of the unique feature codes in the knowledge unit is relatively high, the same feature codes exist in different plane maps.
Referring to fig. 3, fig. 3 exemplarily reflects a situation that different classification units are mapped on a plane coordinate, which represents a plane map, where a reference numeral 1 in fig. 3 represents a first classification unit, a reference numeral 2 represents a second classification unit, and a reference numeral 3 represents a third classification unit, an X, Y axis and corresponding coordinates form a plane, the coordinates represent a weight value per millimeter, and a portion where the reference numerals 1, 2, and 3 overlap indicates that the first classification unit, the second classification unit, and the third classification unit have the same weight value, that is, the same feature code. After a plurality of plane maps shown in fig. 3 are linked according to the weight values, a three-dimensional map is formed.
It should be noted again that the weight value is not a parameter such as the priority of the retrieval presentation or the like, but is a number defined for different feature codes, which is convenient for query retrieval.
The knowledge unit is preprocessed to obtain at least one knowledge subset contained in the knowledge unit, feature codes corresponding to each knowledge subset, association codes corresponding to each feature code and weight values of each feature code, the knowledge subsets with the same feature codes are classified by the knowledge subsets, then the knowledge subsets with the same class form a knowledge set, the knowledge set is mapped to form a plane atlas by using the weight values of the knowledge set, and a three-dimensional atlas is formed by using the plane atlas, so that all the knowledge units with the same feature codes are subjected to three-dimensional simulation storage.
In the above, the data server group includes the first storage module, the second storage module, and the third storage module, and includes a link path among the first storage module, the second storage module, and the third storage module.
In the above, further comprising: a retrieval module for obtaining a first retrieval instruction,
and the decoding module is used for decoding the input first retrieval instruction and acquiring the feature codes contained in the first retrieval instruction and the weight values corresponding to the feature codes.
Further, the link path includes:
a classification path established by the feature code;
an arrangement path is established by the weight value corresponding to the feature code;
the associated path is established by the associated code corresponding to the characteristic code;
mapping the ownership weight value of the knowledge set into a mapping path of the planar atlas;
the planar atlas is linked by the weight value to form the link relation of the three-dimensional atlas,
and the first storage module, the second storage module and the third storage module form a physical path in the data server group.
Referring to fig. 4, the invention also provides a self-adaptive knowledge graph retrieval device, which comprises
Input means for inputting a first search command or a second search command;
the user server receives a first retrieval command or a second retrieval command input by the input device and responds to the first retrieval command or the second retrieval command;
the data server group receives the execution command, starts the retrieval executor to determine the feature code corresponding to the executed command and the weight value corresponding to the feature code through at least one of the first execution command or the second execution command so as to search the corresponding knowledge unit in the storage server group;
and the display device is used for displaying the searched knowledge units.
Further, the input device includes:
a user client, and an input component possessed by the user client.
Further, the data server group comprises a first storage module, a second storage module and a third storage module, and comprises a link path among the first storage module, the second storage module and the third storage module;
the link path includes:
a classification path established by the feature code;
an arrangement path is established by the weight value corresponding to the feature code;
the associated path is established by the associated code corresponding to the characteristic code;
mapping the ownership weight value of the knowledge set into a mapping path of the planar atlas;
the planar atlas is linked by the weight value to form the link relation of the three-dimensional atlas,
and the first storage module, the second storage module and the third storage module form a physical path in the data server group.
Through searching, a corresponding three-dimensional map is firstly presented on the display device, and a user can perform secondary searching according to the searching result highlighted by the three-dimensional map so as to obtain an interesting result and an associated result.
The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present invention. The foregoing is only a preferred embodiment of the present invention, and it should be noted that there are objectively infinite specific structures due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes may be made without departing from the principle of the present invention, and the technical features described above may be combined in a suitable manner; such modifications, variations, or combinations, or other applications of the inventive concepts and solutions as may be employed without such modifications, are intended to be included within the scope of the present invention.
Claims (5)
1. An adaptive knowledge graph retrieval system is characterized by comprising
A knowledge subset forming module for obtaining a plurality of knowledge units, preprocessing the knowledge units, obtaining at least one knowledge subset contained in the knowledge units, a feature code corresponding to each knowledge subset, an association code corresponding to each feature code, and a weight value of each feature code, wherein the weight value is a number which is defined by different feature codes and is convenient for query and retrieval,
the first storage module is used for storing the feature codes corresponding to each knowledge subset, the association codes corresponding to each feature code and the weight value of each feature code;
the classification module is used for configuring classification attributes and classification rules corresponding to the classification attributes, writing the feature codes into the classification attributes, setting and enabling the knowledge subsets to be classified to have the same classification attributes and then to be distributed to corresponding classification units by the classification rules;
the knowledge set forming module is used for arranging the knowledge subsets under the same classification unit by using the weight values of the feature codes and associating the arranged knowledge subsets under the same classification unit by using the association codes to form a knowledge set;
the corresponding feature codes in each knowledge subset at least comprise one, and in different knowledge subsets, the feature codes are completely consistent, a plurality of the feature codes are consistent, and one feature code is consistent; when the knowledge subsets are completely consistent, all the feature codes in the same knowledge subset are written into the classification attributes when the classification attributes are written, and then the knowledge subsets are associated through the association codes, so that the knowledge subsets with the same feature codes are distributed into the same classification units; the multiple coincidences and one coincidence are distributed to other classification units, so that the knowledge sets formed by different classification units form a coincidence point when mapping is carried out, the coincidence point is the coincidence of single feature codes of knowledge subsets in different classification units,
the second storage module is used for storing the knowledge set;
the plane map forming module is used for mapping the ownership weight value of each knowledge set to the plane map;
the three-dimensional map forming module is used for linking the plurality of plane maps according to the weight values to form a three-dimensional map;
the third storage module is used for storing the three-dimensional map and the corresponding link relation of the three-dimensional map;
the user server is used for receiving the retrieval command sent by the user, acquiring the retrieval command, carrying out corresponding response, and transmitting the execution command to the data server group after the response;
the data server group receives the execution command, starts the retrieval executor to determine the corresponding feature codes and the weight values corresponding to the feature codes in the execution command through at least one of the first execution command or the first execution command so as to search the corresponding knowledge units in the storage server group,
the display module is used for displaying the retrieved knowledge units;
the storage server group comprises a first storage module, a second storage module and a third storage module, and comprises a link path among the first storage module, the second storage module and the third storage module;
the link path includes:
a classification path established by the feature code;
an arrangement path is established by the weight value corresponding to the feature code;
the associated path is established by the associated code corresponding to the characteristic code;
mapping the ownership weight value of the knowledge set into a mapping path of the planar atlas;
the planar atlas is linked by the weight value to form the link relation of the three-dimensional atlas,
and the first storage module, the second storage module and the third storage module form a physical path in the storage server group.
2. The adaptive knowledge-graph retrieval system of claim 1, further comprising: a retrieval module for obtaining a first retrieval instruction,
and the decoding module is used for decoding the input first retrieval instruction to obtain the feature codes contained in the first retrieval instruction and the weight values corresponding to the feature codes.
3. The method for searching a knowledge-graph adaptive search system according to any one of claims 1-2, comprising the steps of:
1) acquiring a plurality of knowledge units, preprocessing the knowledge units, acquiring at least one knowledge subset contained in the knowledge units, a feature code corresponding to each knowledge subset, an association code corresponding to each feature code, and a weight value of each feature code,
configuring classification attributes and classification rules corresponding to the classification attributes, writing the feature codes into the classification attributes, setting and enabling the knowledge subsets to be classified to have the same classification attributes to be allocated to the corresponding classification units by the classification rules,
arranging a plurality of knowledge subsets under the same classification unit by using the weight values of the feature codes, and associating the arranged knowledge subsets under the same classification unit by using the association codes to form a knowledge set,
the corresponding feature codes in each knowledge subset at least comprise one, and in different knowledge subsets, the feature codes are completely consistent, and a plurality of feature codes are consistent, wherein one feature code is consistent;
when the knowledge subsets are completely consistent, all the feature codes in the same knowledge subset are written into the classification attributes when the classification attributes are written, and then the knowledge subsets are associated through the association codes, so that the knowledge subsets with the same feature codes are distributed into the same classification units;
the multiple coincidences and one coincidence are distributed to other classification units, so that the knowledge sets formed by different classification units form a coincidence point when mapping is carried out, the coincidence point is the coincidence of single feature codes of knowledge subsets in different classification units,
mapping the ownership weight value of each knowledge set to a planar map, linking a plurality of planar maps according to the weight values to form a three-dimensional map,
inputting a first retrieval instruction, decoding the first retrieval instruction, and acquiring a feature code contained in the first retrieval instruction and a weight value corresponding to the feature code;
2) acquiring a link relation according to the weight value, and acquiring a three-dimensional map according to the link relation;
3) presenting the three-dimensional map to a display device, and marking the three-dimensional map with the weight values contained in the retrieval;
4) and inputting a second retrieval instruction, wherein the second retrieval instruction carries out clicking or touch operation on any position of the three-dimensional map mark, and outputs a plurality of groups of display results in parallel according to a display rule.
4. The adaptive knowledge graph retrieval method according to claim 3, wherein in step 3), marking the three-dimensional graph with the weight values included in the retrieval comprises:
acquiring a corresponding weight value on the three-dimensional atlas,
extracting coordinates corresponding to the weight values on the three-dimensional map by the weight values,
and controlling the display buoy corresponding to the coordinates to perform highlighting.
5. The adaptive knowledge-graph retrieval method according to claim 3, wherein in step 4), outputting a plurality of groups of display results in parallel according to the display rules comprises:
acquiring coordinates corresponding to the highlighted display buoy and a corresponding weight value;
acquiring a plane map corresponding to the coordinate on the three-dimensional map;
acquiring a corresponding knowledge set in each plane map according to the weight value;
acquiring association codes according to the weight values, and acquiring corresponding knowledge subsets on a knowledge set by using the association codes;
and sending the knowledge units corresponding to the weight values in the knowledge subsets to a display device for displaying.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341215A (en) * | 2017-06-07 | 2017-11-10 | 北京航空航天大学 | A kind of vertical knowledge mapping classification ensemble querying method of multi-source based on Distributed Computing Platform |
| CN109947952A (en) * | 2019-03-20 | 2019-06-28 | 武汉市软迅科技有限公司 | Search method, device, equipment and storage medium based on english knowledge map |
| CN110019766A (en) * | 2017-09-25 | 2019-07-16 | 腾讯科技（深圳）有限公司 | Methods of exhibiting, device, mobile terminal and the readable storage medium storing program for executing of knowledge mapping |
| CN110795557A (en) * | 2019-12-11 | 2020-02-14 | 北京明略软件系统有限公司 | Knowledge graph display method and device |
| CN110851617A (en) * | 2019-10-10 | 2020-02-28 | 中国海洋大学 | A multi-source information drug screening method based on knowledge graph |
| CN111274332A (en) * | 2020-01-19 | 2020-06-12 | 中国科学院计算技术研究所 | A patent intelligent retrieval method and system based on knowledge graph |
| CN111813952A (en) * | 2020-06-19 | 2020-10-23 | 北京明略软件系统有限公司 | A three-dimensional display method and device of knowledge graph |
| CN112148702A (en) * | 2020-09-23 | 2020-12-29 | 平安直通咨询有限公司上海分公司 | File retrieval method and equipment |
| CN112633889A (en) * | 2020-11-12 | 2021-04-09 | 中科金审(北京)科技有限公司 | Enterprise gene sequencing system and method |
Family Cites Families (2)
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Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341215A (en) * | 2017-06-07 | 2017-11-10 | 北京航空航天大学 | A kind of vertical knowledge mapping classification ensemble querying method of multi-source based on Distributed Computing Platform |
| CN110019766A (en) * | 2017-09-25 | 2019-07-16 | 腾讯科技（深圳）有限公司 | Methods of exhibiting, device, mobile terminal and the readable storage medium storing program for executing of knowledge mapping |
| CN109947952A (en) * | 2019-03-20 | 2019-06-28 | 武汉市软迅科技有限公司 | Search method, device, equipment and storage medium based on english knowledge map |
| CN110851617A (en) * | 2019-10-10 | 2020-02-28 | 中国海洋大学 | A multi-source information drug screening method based on knowledge graph |
| CN110795557A (en) * | 2019-12-11 | 2020-02-14 | 北京明略软件系统有限公司 | Knowledge graph display method and device |
| CN111274332A (en) * | 2020-01-19 | 2020-06-12 | 中国科学院计算技术研究所 | A patent intelligent retrieval method and system based on knowledge graph |
| CN111813952A (en) * | 2020-06-19 | 2020-10-23 | 北京明略软件系统有限公司 | A three-dimensional display method and device of knowledge graph |
| CN112148702A (en) * | 2020-09-23 | 2020-12-29 | 平安直通咨询有限公司上海分公司 | File retrieval method and equipment |
| CN112633889A (en) * | 2020-11-12 | 2021-04-09 | 中科金审(北京)科技有限公司 | Enterprise gene sequencing system and method |
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