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A hybrid semantic approach to building dynamic maps of research communities

Osborne, Francesco; Scavo, Giuseppe and Motta, Enrico (2014). A hybrid semantic approach to building dynamic maps of research communities. In: Knowledge Engineering and Knowledge Management: 19th International Conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014, Proceedings, Springer International Publishing, pp. 356–372.

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URL: http://www.ida.liu.se/conferences/EKAW14/home.html
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-13704-9_28
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Abstract

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 The Authors
ISBN: 3-319-13703-4, 978-3-319-13703-2
Keywords: Semantic Web; community detection; change detection; trend detection; pattern recognition; data mining; scholarly data
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 41083
Depositing User: Francesco Osborne
Date Deposited: 21 Oct 2014 10:49
Last Modified: 25 Sep 2017 12:56
URI: http://oro.open.ac.uk/id/eprint/41083
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