A knowledge graph based question answering method for medical domain

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.

Automated summary

Question answering (QA) in the domain of Natural Language Processing faces challenges when answering questions that require knowledge-specific information. Traditional QA is insufficient for areas like disease diagnosis or drug recommendation which require specialized knowledge. Recent research has shifted towards knowledge-based question answering (KBQA), but issues persist such as limited historical data and extensive human labor. To tackle these challenges, this paper introduces knowledge graph based question answering (KGQA) for the medical domain, showcasing a method that constructs a medical knowledge graph from named entities and relations in medical documents. By extracting key information from questions and understanding their intent, the approach uses an inference method based on weighted path ranking to score and extract relevant entities for answering questions efficiently.