Abstract
Context-specific description of entities –expressed in RDF– poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner.
This research was supported by the European project QualiChain (number 822404).
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Acosta, M., Vidal, M.-E., Lampo, T., Castillo, J., Ruckhaus, E.: ANAPSID: an adaptive query processing engine for SPARQL endpoints. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 18–34. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_2
Beek, W., Schlobach, S., van Harmelen, F.: A contextualised semantics for owl:sameAs. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 405–419. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34129-3_25
Collarana, D., Galkin, M., Ribón, I.T., Vidal, M., Lange, C., Auer, S.: MINTE: semantically integrating RDF graphs. In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, WIMS, pp. 22:1–22:11 (2017)
Endris, K.M., Galkin, M., Lytra, I., Mami, M.N., Vidal, M.-E., Auer, S.: MULDER: querying the linked data web by bridging RDF molecule templates. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A., Wagner, R.R. (eds.) DEXA 2017. LNCS, vol. 10438, pp. 3–18. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64468-4_1
Isele, R., Bizer, C.: Active learning of expressive linkage rules using genetic programming. J. Web Semant. 23, 2–15 (2013)
Jagadish, H.V., et al.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)
Knoblock, C.A., et al.: Semi-automatically mapping structured sources into the Semantic Web. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 375–390. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30284-8_32
Michelfeit, J., Knap, T.: Linked data fusion in ODCleanStore. In: ISWC Posters and Demonstrations Track (2012)
Ribón, I.T., Vidal, M., Kämpgen, B., Sure-Vetter, Y.: GADES: a graph-based semantic similarity measure. In: SEMANTICS - 12th International Conference on Semantic Systems, Leipzig, Germany, pp. 101–104 (2016)
Schultz, A., Matteini, A., Isele, R., Mendes, P.N., Bizer, C., Becker, C.: LDIF - a framework for large-scale linked data integration. In: International World Wide Web Conference (2012)
Verroios, V., Garcia-Molina, H., Papakonstantinou, Y.: Waldo: an adaptive human interface for crowd entity resolution. In: International Conference on Management of Data, pp. 1133–1148 (2017)
Vychodil, V.: A new algorithm for computing formal concepts. na (2008)
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Tasnim, M., Collarana, D., Graux, D., Galkin, M., Vidal, ME. (2019). COMET: A Contextualized Molecule-Based Matching Technique. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_13
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DOI: https://doi.org/10.1007/978-3-030-27615-7_13
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