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Method for constructing knowledge graph based on entity extraction and relationship mining of rule model

A technology of entity extraction and relationship mining, applied in special data processing applications, instruments, electrical digital data processing, etc., to optimize and improve search quality

Active Publication Date: 2017-06-20
湖南中科优信科技有限公司
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Problems solved by technology

Entity extraction and relationship mining in a specific field do not have good results. In order to solve the defects of the existing technology, the present invention proposes a method for constructing a knowledge map based on rule model entity extraction and relationship mining

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  • Method for constructing knowledge graph based on entity extraction and relationship mining of rule model
  • Method for constructing knowledge graph based on entity extraction and relationship mining of rule model
  • Method for constructing knowledge graph based on entity extraction and relationship mining of rule model

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Embodiment Construction

[0045] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0046] The present invention is a method for constructing a knowledge map based on rule-based entity extraction and relationship mining. The specific implementation steps are as follows (taking the construction of a food safety knowledge map as an example):

[0047]Step 1: crawl the encyclopedia knowledge base data in the target field, and define dictionaries such as food, pesticides, nutrition, diseases and insect pests, etc., to facilitate rule mining:

[0048] (1) According to the national food standard classification, pesticide classification, and possible pests and diseases of nutrients, fruits and vegetables, relevant encyclopedia data is crawled and manual participation is used to construct a dictionary, and the mapping relationship between partial abbreviations and full names is established.

[0049] (2) Accor...

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Abstract

The invention relates to a method for constructing a knowledge graph based on entity extraction and relationship mining of a rule model. The method comprises the following steps: step 1: crawling data of an encyclopedia knowledge base of a target region, and defining dictionaries of foods, pesticides, nutrition and plant diseases and insect pests, so as to be convenient for rule mining; step 2: carrying out HTML (Hypertext Markup Language) label removal on encyclopedia type data to obtain Chinese texts and obtaining a URL (Uniform Resource Locator) link, so as to be convenient for subsequent processing; step 3: obtaining more complete entity attribute information by adding manually annotated relation attribute information; and step 4: obtaining an event and establishing a graph relation. According to the method provided by the invention, text information is converted into word vector mathematical information; vector similarity comparison is carried out and a relation between entities is labeled according to a relation between numbers, so as to represent a core knowledge base for the field and improve and optimize search quality; and a process from a simple character string to entity comprehending is realized.

Description

technical field [0001] The invention relates to a method for constructing a knowledge graph, in particular to a method for constructing a knowledge graph based on entity extraction and relationship mining of a rule model, and belongs to the technical field of data mining in natural language processing. Background technique [0002] In the past two years, with the full development of Linking Open Data1 and other projects, the number of Semantic Web data sources has increased sharply, and a large amount of RDF data has been released. The Internet is changing from the Document Web (Document Web), which only contains web pages and hyperlinks between web pages, to the Data Web (Data Web), which contains a large number of descriptions of various entities and rich relationships between entities. [0003] In this context, search engine companies such as Google, Baidu, and Sogou have built knowledge graphs based on this, namely Knowledge Graph, Zhixin, and Zhicube, to improve search ...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/3335G06F16/3344G06F16/3346G06F16/374
Inventor 段大高赵宁韩忠明
Owner 湖南中科优信科技有限公司
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