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Mining semantic relations between research areas

Osborne, Francesco and Motta, Enrico (2012). Mining semantic relations between research areas. In: The Semantic Web – ISWC 2012: 11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part I, Lecture Notes in Computer Science, pp. 410–426.

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URL: http://iswc2012.semanticweb.org/
DOI (Digital Object Identifier) Link: http://doi.org/10.1007/978-3-642-35176-1_26
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Abstract

For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard.

Item Type: Conference Item
Copyright Holders: 2012 Springer-Verlag Berlin Heidelberg
ISBN: 3-642-35175-1, 978-3-642-35175-4
ISSN: 0302-9743
Keywords: research data; ontology population; bibliographic data; empirical evaluation; scholarly ontologies; data mining
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
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Item ID: 34898
Depositing User: Kay Dave
Date Deposited: 30 Oct 2012 16:35
Last Modified: 28 Mar 2016 19:25
URI: http://oro.open.ac.uk/id/eprint/34898
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