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An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation

An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation

Alexandros Vassiliades, Nick Bassiliades, Theodore Patkos, Dimitris Vrakas
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 34
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.309421
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MLA

Vassiliades, Alexandros, et al. "An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation." IJSWIS vol.18, no.1 2022: pp.1-34. http://doi.org/10.4018/IJSWIS.309421

APA

Vassiliades, A., Bassiliades, N., Patkos, T., & Vrakas, D. (2022). An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-34. http://doi.org/10.4018/IJSWIS.309421

Chicago

Vassiliades, Alexandros, et al. "An Open-Ended Web Knowledge Retrieval Framework for the Household Domain With Explanation and Learning Through Argumentation," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-34. http://doi.org/10.4018/IJSWIS.309421

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Abstract

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provides access to commonsense knowledge about household environments and performs semantic matching between entities from the web knowledge graph ConceptNet, using semantic knowledge from DBpedia and WordNet, with the ones existing in the knowledge graph. They offer a set of predefined SPARQL templates that directly address the ontology on which their knowledge retrieval framework is built and querying through SPARQL. The framework also features an argumentation component, where the user can argue against the answers of the knowledge retrieval component of the framework under two different scenarios: the missing knowledge scenario, where an entity should be in the answers, and the wrong knowledge scenario, where an entity should not be in the answers. This argumentation dialogue can end up in learning a new piece of knowledge when the user wins the dialogue.