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Klink-2: integrating multiple web sources to generate semantic topic networks

Osborne, Francesco and Motta, Enrico (2015). Klink-2: integrating multiple web sources to generate semantic topic networks. In: The Semantic Web - ISWC 2015 (Part 1), Lecture Notes in Computer Science, Springer International Publishing, Cham, pp. 408–424.

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The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i) they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics – e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii) they do not distinguish between different kinds of hierarchical relationships; and iii) they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities (including papers, authors, venues, and technologies) to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords (e.g., “ontology”) and separates them into the appropriate distinct topics – e.g., “ontology/philosophy” vs. “ontology/semantic web”. Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 Springer International Publishing AG
ISBN: 3-319-25006-X, 978-3-319-25006-9
ISSN: 0302-9743
Keywords: scholarly data; ontology learning; bibliographic data; scholarly ontologies; data mining; semantic web; linked data
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 43793
Depositing User: Francesco Osborne
Date Deposited: 23 Jul 2015 09:58
Last Modified: 17 Jun 2020 09:43
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