Medical query expansion method based on knowledge graph

Abstract

The invention discloses a medical query expansion method based on a knowledge graph. According to the query expansion technology in the automatic question-answering system, the semantic difference between question-answering sentences is reduced by supplementing the expansion information into the question sentences, so that the accuracy of the question-answering system is improved. In the field of medical questions and answers, an existing query expansion method does not fully combine a co-occurrence association relationship and a reasoning association relationship among medical terms under different query intentions, so that obtained expansion words are not accurate enough. According to the method, the medical knowledge graph is taken as a knowledge source of the extension words, the candidate extension words are obtained by utilizing reasoning association of the medical terms under different query intentions, and the final extension words are screened in combination with negative medical term recognition and mutual information technologies, so that the accuracy of a medical question and answer system is finally improved.

Description

technical field [0001] The invention relates to the field of natural language processing, in particular to the processing of queries in question answering systems. Query expansion is an important link and key technology in automatic question answering system. Background technique [0002] With the rapid development of the Internet, more and more patients tend to seek medical help through online health communities. However, the dramatically increasing number of questions placed a huge response burden on physicians. In order to alleviate the workload of doctors and meet the needs of users to get answers quickly, a large number of researchers have devoted themselves to the research in the field of medical question answering. In the medical question answering system, the word mismatch caused by the different expressions between the question and answer sentences and the semantic deviation caused by the difference in the amount of information between the question and answer sent...

AI Technical Summary

Problems solved by technology

However, keyword-based query expansion methods only select keywords from the statistical level, ignoring the semantic information of the query, so many irrelevant medical entities may be expanded to introduce "noise" into the original query, thereby affecting the quality of answer selection

Semantic-based query expansion utilizes medical ontology database or medical semantic dictionary to mine potential semantics in queries other than surface literals, but current research on semantic-based query expansion selects candidate expansions based on the concept of medical entities in the stage of obtaining candidate expansion words words, ignoring the important role of inferential associations between medical entities in question-answer sentences in guiding the acquisition of candidate expansion words

In the expansion word screening stage, some researchers use mutual information to screen candidate words, but they ignore the interference of negative medical entities on the mutual information value between entities

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