Reference : Popularity-driven Ontology Ranking using Qualitative Features

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/40972

Title :	Popularity-driven Ontology Ranking using Qualitative Features
Language :	English
Author, co-author :	Kolbe, Niklas [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Kubler, Sylvain [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > ; Université de Lorraine > Research Center for Automatic Control]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2019
Main document title :	The Semantic Web - ISWC 2019
Peer reviewed :	Yes
Event name :	18th International Semantic Web Conference
Event date :	26/10/2019 - 30/10/2019
Keywords :	[en] Learning to Rank ; Ontology Reuse ; Web of Things ; Linked Vocabularies ; Semantic Interoperability
Abstract :	[en] Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies' occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies' popularity, resulting in ineffective ranking scores. This motivated us to investigate - based on the problematic WoT case - whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies' popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.
Permalink :	http://hdl.handle.net/10993/40972
DOI :	10.1007/978-3-030-30793-6_19
Framework Programme / European Project :	H2020 ; 688203 - bIoTope - Building an IoT OPen innovation Ecosystem for connected smart objects

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	Fulltext file(s): File Commentary Version Size Access Open access 2019-07-02_iswc19-ranking-final.pdf The final authenticated version is available online at https://doi.org/10.1007/978-3-030-30793-6_19 Publisher postprint 574.28 kB View/Open

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