CN116595182A - Evaluation expert recommendation system based on knowledge graph and semantic understanding - Google Patents
Evaluation expert recommendation system based on knowledge graph and semantic understanding Download PDFInfo
- Publication number
- CN116595182A CN116595182A CN202210403303.2A CN202210403303A CN116595182A CN 116595182 A CN116595182 A CN 116595182A CN 202210403303 A CN202210403303 A CN 202210403303A CN 116595182 A CN116595182 A CN 116595182A
- Authority
- CN
- China
- Prior art keywords
- expert
- recommendation system
- semantic
- knowledge
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title description 2
- 238000012552 review Methods 0.000 claims abstract description 30
- 238000011160 research Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 4
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000012163 sequencing technique Methods 0.000 claims abstract description 4
- 238000004590 computer program Methods 0.000 claims description 7
- 238000000034 method Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 description 2
- 238000013077 scoring method Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/194—Calculation of difference between files
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- Computational Linguistics (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Data Mining & Analysis (AREA)
- General Business, Economics & Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Primary Health Care (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computing Systems (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application provides a review expert recommendation system based on knowledge graph and semantic understanding, which comprises the following steps: the review expert recommendation system based on the knowledge graph and the semantic understanding comprises: establishing an expert database, wherein an expert is responsible for maintaining personal basic information of the expert; collecting academic papers of experts, and constructing a domain label system, wherein each label has detailed text description; selecting a keyword, extracting semantic information, performing similarity calculation with a domain label, and screening experts with higher similarity; referring to the RFM model, scoring and sequencing the expert according to whether the expert recently sends articles in the related field, the number of the articles and the number of the articles to be led, and calculating the research depth and the field authority of the expert; expert social relations, constructing a knowledge graph library of the expert and the affiliated institution, constructing an expert social relation network, and deleting the expert to be avoided according to the network; and outputting a final expert list.
Description
Technical Field
The application relates to the technical field of Internet, in particular to a review expert recommendation system based on knowledge graph and semantic understanding.
Background
At present, most government institutions and scientific departments still use manual selection of experts to review scientific projects, and the mode not only consumes manpower, but also can cause unreasonable distribution, so that people are required to have good knowledge on the scientific research field and expert skill to which the scientific projects belong, and the review quality of the scientific projects can be influenced by mismatching of the experts and the scientific projects, and adverse effects are caused on the review results by simple human analysis.
Patent 1: CN202010884229.1
Disadvantages: the recommended collaborative scientific research expert searches academic published documents based on text information input by users, the document author is used as an expert search result, the social network condition of the expert is not considered,
patent 2: cn201911184563.X
Disadvantages: only the multisource database deduplication and scoring method is constructed, expert labels are not constructed, and expert social network information is not considered.
Disclosure of Invention
(one) solving the technical problems
In order to solve the problems, the application provides a review expert recommendation system based on knowledge graph and semantic understanding.
(II) technical scheme
In order to achieve the above purpose, the present application provides the following technical solutions:
the application provides a review expert recommendation system based on knowledge graph and semantic understanding, which comprises:
establishing an expert database, wherein the expert database contains history information of an expert, expert data in the expert database is unique, and the expert is responsible for maintaining personal basic information of the expert;
collecting expert academic papers, and constructing a domain label system, wherein the domain label system comprises a plurality of labels, and each label has detailed text description;
the method comprises the steps of selecting a word of a review project document, removing an stop word, extracting a keyword, extracting semantic information by using a bert model, and performing similarity calculation with a field label;
calculating the similarity between the project label and the expert, and screening the expert with higher similarity;
referring to the RFM model, scoring and sequencing the expert according to whether the expert recently sends articles in the related field, the number of the articles and the number of the articles to be led, and calculating the research depth and the field authority of the expert;
expert social relations, constructing a knowledge graph library of the expert and the affiliated institution, constructing an expert social relation network, and deleting the expert to be avoided according to the network;
outputting a final expert list;
expert data is updated annually.
Further, the basic information includes paper information, patent information, partner information, and project information.
Further, the number of experts is greater than 30.
Further, an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the knowledge-graph and semantic-understanding based review expert recommendation system according to any of claims 1 to 3 when executing the program.
Further, a non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the knowledge graph and semantic understanding based review expert recommendation system according to any of claims 1 to 3.
Advantageous effects
The beneficial effects of the application are as follows: the review expert recommendation system based on knowledge graph and semantic understanding has the following advantages:
1. the method has the advantages that proper authoritative experts are recommended for the review projects quickly and efficiently, and the recommendation success rate is improved;
2. solving the problem of avoiding expert by using a social network;
3. an expert label system is constructed to portray an expert;
4. and (3) referring to the RFM model to establish an expert scoring method of the recommendation system, comprehensively considering recent research hotspots, historical research quantity and quotation conditions of the expert, and giving weight to the expert label.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and together with the embodiments of the application and do not constitute a limitation to the application, and in which:
FIG. 1 is a flow chart of the review expert recommendation system of the present application based on knowledge graph and semantic understanding.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to more clearly and completely describe the technical scheme of the application, the application is further described below with reference to the accompanying drawings.
Referring to fig. 1, the present application proposes a review expert recommendation system based on knowledge graph and semantic understanding, the review expert recommendation system based on knowledge graph and semantic understanding includes: establishing an expert database, wherein the expert database contains history information of an expert, expert data in the expert database is unique, and the expert is responsible for maintaining personal basic information of the expert; collecting expert academic papers, and constructing a domain label system, wherein the domain label system comprises a plurality of labels, and each label has detailed text description; the method comprises the steps of selecting a word of a review project document, removing an stop word, extracting a keyword, extracting semantic information by using a bert model, and performing similarity calculation with a field label; calculating the similarity between the project label and the expert, and screening the expert with higher similarity; referring to the RFM model, scoring and sequencing the expert according to whether the expert recently sends articles in the related field, the number of the articles and the number of the articles to be led, and calculating the research depth and the field authority of the expert; expert social relations, constructing a knowledge graph library of the expert and the affiliated institution, constructing an expert social relation network, and deleting the expert to be avoided according to the network; outputting a final expert list; annual update of expert data; the basic information comprises paper information, patent information, partner information and project information; the expert number is greater than 30; an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the knowledge-graph and semantic-understanding based review expert recommendation system of any one of claims 1 to 3 when the program is executed by the processor; a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the knowledge-graph and semantic-understanding based review expert recommendation system of any one of claims 1 to 3.
In this embodiment, manually selecting the review expert consumes manpower, does not follow a unified standard, is not efficient and accurate enough, and can avoid the expert by using a more complete expert social network, so that the project review is fair and fair, the review project description and the author information are input into a recommendation system, the system carries out semantic understanding on the review project, the extracted key information calculates similarity with the expert portrait, calculates field authority of the expert with higher similarity, researches depth and ranks, deletes the expert to be avoided, and outputs a recommendation expert list.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description. Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
In the description of the present application, it should be understood that the azimuth or positional relationships indicated by the azimuth terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal", and "top, bottom", etc., are generally based on the azimuth or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify the description, and these azimuth terms do not indicate and imply that the apparatus or elements referred to must have a specific azimuth or be constructed and operated in a specific azimuth, and thus should not be construed as limiting the scope of protection of the present application; the orientation word "inner and outer" refers to inner and outer relative to the contour of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present application.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.
Claims (5)
1. A knowledge-graph and semantic-understanding-based review expert recommendation system, characterized in that the knowledge-graph and semantic-understanding-based review expert recommendation system comprises:
establishing an expert database, wherein the expert database contains history information of an expert, expert data in the expert database is unique, and the expert is responsible for maintaining personal basic information of the expert;
collecting expert academic papers, and constructing a domain label system, wherein the domain label system comprises a plurality of labels, and each label has detailed text description;
the method comprises the steps of selecting a word of a review project document, removing an stop word, extracting a keyword, extracting semantic information by using a bert model, and performing similarity calculation with a field label;
calculating the similarity between the project label and the expert, and screening the expert with higher similarity;
referring to the RFM model, scoring and sequencing the expert according to whether the expert recently sends articles in the related field, the number of the articles and the number of the articles to be led, and calculating the research depth and the field authority of the expert;
expert social relations, constructing a knowledge graph library of the expert and the affiliated institution, constructing an expert social relation network, and deleting the expert to be avoided according to the network;
outputting a final expert list;
expert data is updated annually.
2. The knowledge-graph and semantic-understanding-based review expert recommendation system according to claim 1, wherein the basic information includes paper information, patent information, partner information, and project information.
3. The knowledge-graph and semantic-understanding-based review expert recommendation system of claim 1 wherein the number of experts is greater than 30.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the knowledge-graph and semantic-understanding based review expert recommendation system of any one of claims 1 to 3 when the program is executed by the processor.
5. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the knowledge-graph and semantic-understanding based review expert recommendation system of any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210403303.2A CN116595182A (en) | 2022-04-18 | 2022-04-18 | Evaluation expert recommendation system based on knowledge graph and semantic understanding |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210403303.2A CN116595182A (en) | 2022-04-18 | 2022-04-18 | Evaluation expert recommendation system based on knowledge graph and semantic understanding |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116595182A true CN116595182A (en) | 2023-08-15 |
Family
ID=87588600
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210403303.2A Pending CN116595182A (en) | 2022-04-18 | 2022-04-18 | Evaluation expert recommendation system based on knowledge graph and semantic understanding |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116595182A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117708351A (en) * | 2024-02-06 | 2024-03-15 | 国泰新点软件股份有限公司 | Deep learning-based technical standard auxiliary review method, system and storage medium |
-
2022
- 2022-04-18 CN CN202210403303.2A patent/CN116595182A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117708351A (en) * | 2024-02-06 | 2024-03-15 | 国泰新点软件股份有限公司 | Deep learning-based technical standard auxiliary review method, system and storage medium |
| CN117708351B (en) * | 2024-02-06 | 2024-04-30 | 国泰新点软件股份有限公司 | Deep learning-based technical standard auxiliary review method, system and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107180045B (en) | Method for extracting geographic entity relation contained in internet text | |
| US7912816B2 (en) | Adaptive archive data management | |
| CN107705066A (en) | Information input method and electronic equipment during a kind of commodity storage | |
| CN105975984B (en) | Network quality evaluation method based on evidence theory | |
| CN109493265A (en) | A kind of Policy Interpretation method and Policy Interpretation system based on deep learning | |
| CN111340334B (en) | Intelligent work order assignment method, system and medium | |
| CN109033284A (en) | The power information operational system database construction method of knowledge based map | |
| CN108875599A (en) | A kind of identification check of drawings method of building trade ENGINEERING CAD drawing | |
| CN106708929B (en) | Video program searching method and device | |
| CN107885793A (en) | A kind of hot microblog topic analyzing and predicting method and system | |
| CN109460725B (en) | Receipt consumption details content mergence and extracting method, equipment and storage medium | |
| CN104778201B (en) | A kind of first technology search method merged based on more Query Results | |
| CN103886020A (en) | Quick search method of real estate information | |
| CN111428503B (en) | Identification processing method and processing device for homonymous characters | |
| WO2020123689A1 (en) | Suggesting text in an electronic document | |
| CN107220307A (en) | Web search method and device | |
| CN114647741A (en) | Process automatic decision and reasoning method, device, computer equipment and storage medium | |
| CN107239564A (en) | A kind of text label based on supervision topic model recommends method | |
| US20220107980A1 (en) | Providing an object-based response to a natural language query | |
| CN112116331A (en) | Talent recommendation method and device | |
| CN113627797A (en) | Image generation method and device for employee enrollment, computer equipment and storage medium | |
| CN110310012B (en) | Data analysis method, device, equipment and computer readable storage medium | |
| CN112015907A (en) | Method and device for quickly constructing discipline knowledge graph and storage medium | |
| CN116595182A (en) | Evaluation expert recommendation system based on knowledge graph and semantic understanding | |
| JP2012093966A (en) | Document analysis apparatus and program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |