Question answering system for chemistry—A semantic agent extension

Digital Chemical Engineering

Sebastian Mosbach¹  Daniel Nurkowski²  Jethro Akroyd³  Markus Kraft³  Angiras Menon⁴  Xiaochi Zhou⁴ 

[1] Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge CB3 0AS, UK;CMCL Innovations, Sheraton House, Cambridge CB3 0AX, UK;Cambridge Centre for Advanced Research and Education in Singapore (CARES), CREATE Tower, 1 Create Way, 138602, Singapore;Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, West Site, Cambridge CB3 0AS, UK;

关键词: Question answering;    Semantic agent;    Knowledge graph;   

DOI  :  

来源: DOAJ

【 摘 要 】

This paper introduces an extension of a previously developed question answering (QA) system for chemistry, operating on a knowledge graph (KG) called Marie. This extension enables the automatic invocation of semantic agents to answer questions when static data is absent from the KG. The agents are semantically described using the agent ontology, OntoAgent, to enable automated agent discovery and invocation.The natural language processing (NLP) models of the QA system need to be trained in order to interpret questions to be answered by new agents. For this purpose, we extend OntoAgent so that it becomes possible to automatically create training material for the NLP models.We evaluate the extended QA system with two example chemistry-related agents and an evaluation question set. The evaluation result shows that the extension allows the QA system to discover the suitable agent and to invoke the agent by automatically constructing requests from the semantic agent description, thereby increasing the range of questions the QA system can answer.

【 授权许可】

Unknown