Question answering system for chemistry - A semantic agent extension
- The Marie QA system is extended to invoke semantic agents to answer questions.
- Extended OntoAgent ontology to provide information for the creation of training material for NLP models.
- Implemented a mechanism to automatically include semantic agents into the QA system.
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.
- This paper draws from preprint 286: Question Answering System for Chemistry – a semantic agent extension
- Access the article at the publisher: DOI: 10.1016/j.dche.2022.100032