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Title: Question answering system for chemistry—a semantic agent extension
Authors: Zhou, Xiaochi
Nurkowski, Daniel
Menon, Angiras
Akroyd, Jethro
Mosbach, Sebastian
Kraft, Markus
Keywords: Engineering
Issue Date: 2022
Source: Zhou, X., Nurkowski, D., Menon, A., Akroyd, J., Mosbach, S. & Kraft, M. (2022). Question answering system for chemistry—a semantic agent extension. Digital Chemical Engineering, 3, 100032-.
Journal: Digital Chemical Engineering
Abstract: 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.
ISSN: 2772-5081
DOI: 10.1016/j.dche.2022.100032
Schools: School of Chemical and Biomedical Engineering 
Organisations: Cambridge Centre for Advanced Research and Education in Singapore (CARES)
Rights: © 2022 The Authors. Published by Elsevier Ltd on behalf of Institution of Chemical Engineers (IChemE). This is an open access article under the CC BY license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Journal Articles

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