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Author: Houcine Senoussi

Affiliation: Quartz Laboratory, EISTI, Cergy and France

Keyword(s): DBpedia, DBpedia Categories, Linked Open Data, Similarity Measure, Semantic Web.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaboration and e-Services ; e-Business ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Semantic Web ; Soft Computing ; Symbolic Systems

Abstract: Similarity is defined as the degree of resemblance between two objects. In this paper we present a new method to evaluate similarity between resources in Linked Open Data. The input of our method is a pair of resources belonging to the same type (e.g. Person or Painter), described by their Dbpedia categories. We first compute the ’distance’ between each pair of categories. For that we need to explore the graph whose vertices are the categories and whose edges connect categories and sub-categories. Then we deduce a measure of the similarity/dissimilarity between the two resources. The output of our method is not limited to this measure but includes other quantitative and qualitative informations explaining similarity/dissimilarity of the two resources. In order to validate our method, we implemented it and applied it to a set of DBpedia resources that refer to painters belonging to different countries, centuries and artistic movements.

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Paper citation in several formats:
Senoussi, H. (2018). Using DBpedia Categories to Evaluate and Explain Similarity in Linked Open Data. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD; ISBN 978-989-758-330-8; ISSN 2184-3228, SciTePress, pages 117-127. DOI: 10.5220/0006939001170127

@conference{keod18,
author={Houcine Senoussi.},
title={Using DBpedia Categories to Evaluate and Explain Similarity in Linked Open Data},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD},
year={2018},
pages={117-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006939001170127},
isbn={978-989-758-330-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - KEOD
TI - Using DBpedia Categories to Evaluate and Explain Similarity in Linked Open Data
SN - 978-989-758-330-8
IS - 2184-3228
AU - Senoussi, H.
PY - 2018
SP - 117
EP - 127
DO - 10.5220/0006939001170127
PB - SciTePress