Limited domain-oriented knowledge graph updating method and system

Abstract

The invention provides a limited domain-oriented knowledge graph updating method and system, and the method comprises the steps: inputting limited domain question and answer corpora, extracting candidate entities of sentences in the corpora through word segmentation, screening common functional words in a word segmentation result through a word frequency dictionary, and obtaining a candidate entity set; constructing an inverted index dictionary according to the limited domain knowledge graph to obtain a similar vocabulary set of each candidate entity; training the candidate entities and the corresponding similar vocabulary sets into word vectors, and calculating cosine similarity so as to judge the types of the candidate entities; obtaining the relationship between every two candidate entities in the candidate entity set by using the trained Bert text classification model; and updating the candidate entity type and the relationship between the candidate entities into the knowledge graph according to the judgment. The knowledge graph updating method provided by the invention is higher in efficiency, can recognize the newly appearing entity type according to the existing entities inthe graph, and effectively improves the knowledge graph updating speed and accuracy.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a method and system for updating a knowledge map in the field of education. Background technique [0002] With the rapid development of society, we have entered the era of information explosion, and massive amounts of information are generated every day. How to obtain valuable information from massive data is a difficult problem, so the knowledge graph came into being. The knowledge graph describes concepts, entities and their relationships in the objective world in a structured form, and its basic units are "entity-relationship-entity" triples, and entities and their related attribute-value pairs. Entities are interconnected through relationships to form a networked knowledge structure. At present, the construction process of the knowledge map generally includes: preprocessing the original data, converting it into structured data, and then performing entity...

AI Technical Summary

Problems solved by technology

First of all, the complexity of the data source will lead to repeated redundancy in the knowledge triple extraction results, which will bring a heavy workload to the second step of knowledge fusion and alignment; The previously identified relationship types are not utilized when acquiring new knowledge triple relationships, resulting in waste of resource data

[0006] The knowledge map update technology for text data has the characteristics of low efficiency and poor accuracy for a long time, and new technical solutions are urgently needed in this field

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