A knowledge graph based question answering method for medical domain

Abstract

Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions’ intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach.

Review of artificial intelligence‐based question‐answering systems in healthcare

“…Answering the questions is a current topic in the field of natural language processing. Systems pose a major challenge as traditional systems are not sufficient for some knowledge domains (Huang et al, 2021). The difficulty in the biomedical field is that most current systems only handle a limited number of questions and responses, requiring further effort to increase their effectiveness (Zhou et al, 2018).…”

Towards Knowledge-Based Tourism Chinese Question Answering System

“…Combined with the BM25 algorithm and the fine-tuned BERT model, a better information retrieval scheme was developed. Huang [5] proposed a medical domain question answering (KGQA) method based on a knowledge graph. Firstly, the relationship between entities and entities was extracted from medical documents to construct a medical knowledge graph.…”

“…For example, Lin [3] proposed a QA system based on e-commerce. Das [4] examined case-based knowledge base natural language query reasoning, and Huang [5] studied a knowledge graph based on a question answering method for the medical domain. Sheng [6] proposed a QA system, DSQA, based on a knowledge graph for answering domain-specific medical questions.…”

A data-centric way to improve entity linking in knowledge-based question answering

“…Regular internet users may lack of the technical skills required to query such datasets easily. KBQA aims to answer the natural language question revolves around the KB, which provide a portable way to access KB for normal users ( Huang et al, 2021 ). In KBQA, entity linking (EL) is an important approach to connecting natural language queries to formalized KBs and is usually considered to be the first step in creating a KBQA system.…”