Using DBpedia Categories to Evaluate and Explain Similarity in Linked Open Data

Houcine Senoussi

2018

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.

Paper Citation

in Harvard Style

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) - Volume 2: KEOD; ISBN 978-989-758-330-8, SciTePress, pages 117-127. DOI: 10.5220/0006939001170127

in Bibtex Style

@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) - Volume 2: KEOD},
year={2018},
pages={117-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006939001170127},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 2: KEOD
TI - Using DBpedia Categories to Evaluate and Explain Similarity in Linked Open Data
SN - 978-989-758-330-8
AU - Senoussi H.
PY - 2018
SP - 117
EP - 127
DO - 10.5220/0006939001170127
PB - SciTePress