Method and system for automatically constructing knowledge maps for mass unstructured texts

Abstract

The invention belongs to the technical field of computer software, and discloses a method and a system for automatically constructing knowledge maps for mass unstructured texts. The method comprises the steps of: abstracting a named entity recognition problem into a sequence labeling problem by giving a sentence and labeling each word in the sequence of sentences; designing effective features according to the training data, learning various classification models, and using trained classifiers to predict relationships; linking multiple existing knowledge to create a large-scale and unified knowledge network from the top; and capturing and integrating entity information from three online encyclopedias, open websites, related knowledge bases, or search engine logs. According to the method andthe system for automatically constructing knowledge maps for mass unstructured texts, the construction speed of the knowledge maps can be greatly improved, the time efficiency is improved, and the human resource cost is reduced by more than 30%. In addition, the method and the system have better domain portability, and the construction of the knowledge map can be quickly implemented by only optimizing the entities and relationship extraction algorithms in the invention.

Description

technical field [0001] The invention belongs to the technical field of computer software, and in particular relates to a method and system for automatically constructing a knowledge graph facing massive unstructured texts. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: Knowledge Graph aims to describe the entities of the objective world and the relationship between them. attribute composition. In 2012, Google first launched the knowledge graph and used it to enhance search results in search engines, which also marked the successful application of large-scale knowledge graphs in Internet semantic search. In other words, the knowledge graph is composed of a large amount of knowledge, and each piece of knowledge is represented by a triplet, for example: (China, capital, Beijing). At present, knowledge graphs are mostly extracted and constructed from encyclopedic structured data; knowledge graphs can serve cust...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the existing technology are: At present, there are few automatic construction methods of knowledge graphs for massive unstructured texts, and the technical difficulty is relatively high.

The existing methods are mainly manual, with a low degree of automation, requiring heavy manual labor to construct, trim, deduplicate, process, and align the atlas. The entire process is highly professional, heavy workload, and difficult to maintain.

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