Abstract
As Linked Data (or LD) increasingly expands its capacity, ambiguity in vocabularies on LD has become more problematic. This paper deals with a part of the ambiguity, namely, class ambiguity and property ambiguity. In this paper, we propose a novel clustering method, CPClustering, which clusters synonymous classes and properties in an interleaving manner. CPClustering groups classes by their related properties, and, inversely, groups properties by their related classes. CPClustering iteratively clusters classes and properties, and updates their representations in terms of immediate clustering results.
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Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)
Morzy, M., Ławrynowicz, A., Zozuliński, M.: Using substitutive itemset mining framework for finding synonymous properties in linked data. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 422–430. Springer, Heidelberg (2015). doi:10.1007/978-3-319-21542-6_27
Steinley, D.: Properties of the hubert-arable adjusted rand index. Psychol. Methods 9(3), 386 (2004)
W3C: SPARQL Query Language for RDF (2008). https://www.w3.org/TR/rdf-sparql-query/
Zhang, Z., Gentile, A.L., Blomqvist, E., Augenstein, I., Ciravegna, F.: Statistical knowledge patterns: identifying synonymous relations in large linked datasets. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 703–719. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41335-3_44
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This research was partly supported by the program Research and Development on Real World Big Data Integration and Analysis of the Ministry of Education, Culture, Sports, Science and Technology, and RIKEN, Japan.
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Komamizu, T., Amagasa, T., Kitagawa, H. (2016). Interleaving Clustering of Classes and Properties for Disambiguating Linked Data. In: Morishima, A., Rauber, A., Liew, C. (eds) Digital Libraries: Knowledge, Information, and Data in an Open Access Society. ICADL 2016. Lecture Notes in Computer Science(), vol 10075. Springer, Cham. https://doi.org/10.1007/978-3-319-49304-6_30
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DOI: https://doi.org/10.1007/978-3-319-49304-6_30
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Online ISBN: 978-3-319-49304-6
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