A semi-automatic knowledge map construction method

Abstract

The invention discloses a semi-automatic knowledge map construction method, Most of the existing relationship extraction methods rely on the pre-determined relationship type system, This process is complex and time-consuming, the invention is based on dependency analysis, aiming at several Chinese sentence patterns, combined with a semantic dictionary, While exporting open relationships, semantictagging of words in relation, The semantics of unknown words are inferred based on statistics, and the semantic relation patterns on a large number of corpus are statistically clustered to form a relation type system. In this process, most of the links are carried out automatically, in which the results of semantic annotation and relation clustering of unknown words can be checked manually. Compared with the existing open relation extraction method, the invention optimizes and expands, and the extraction of the open relation and the formation of the semantic relation type complement each other, so as to improve the accuracy rate of the two.

Description

technical field [0001] The present invention relates to the technical field of information extraction, in particular to a semi-automatic knowledge map construction method. Background technique [0002] In recent years, with the development of Internet technology, the World Wide Web has gradually become an important source of information, and how to quickly obtain interesting information has become the focus of research. It is against this background that information extraction technology emerged. The main purpose of information extraction is to extract specified entities, relationships, events and other factual information from natural language texts, and transform unstructured information in texts into structured information. . Entity relationship extraction refers to determining whether there is a certain semantic relationship between entities. It is an integral part of information extraction, including text mining, machine learning and natural language processing technol...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing relationship extraction methods rely on the pre-determined relationship type system, and then carry out corpus annotation and model training according to these relationship types. In order to achieve high accuracy, it is necessary to carry out a large number of corpus annotations for each relationship type. The disadvantage is that The establishment process of the relationship type system requires multiple revision iterations, resulting in frequent revisions of the corpus annotation process

[0006] At present, there are many open relationship extraction technologies in English, which are relatively mature. Part of the reason is that English is simpler than Chinese, and there are relatively few open relationship extraction technologies for Chinese.

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