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2016 | OriginalPaper | Buchkapitel

A Self-Adaptive and Incremental Approach for Data Profiling in the Semantic Web

verfasst von : Kenza Kellou-Menouer, Zoubida Kedad

Erschienen in: Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIX

Verlag: Springer Berlin Heidelberg

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Abstract

The increasing adoption of linked data principles has led to the availability of a huge amount of datasets on the Web. However, the use of these datasets is hindered by the lack of descriptive information about their content. Indeed, interlinking, matching or querying them requires some knowledge about the types and properties they contain.

In this paper, we tackle the problem of describing the content of an RDF dataset by profiling its entities, which consists in discovering the implicit types and providing their description. Each type is described by a profile composed of properties and their probabilities. Our approach relies on a clustering algorithm. It is self-adaptive, as it can automatically detect the most appropriate similarity threshold according to the dataset. Our algorithms generate overlapping clusters, enabling the detection of several types for an entity. As a dataset may evolve, our approach is incremental and can assign a type to a new entity and update the type profiles without browsing the whole dataset. We also present some experimental evaluations to demonstrate the effectiveness of our approach.

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Vorheriges Kapitel A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

Resource Description Framework: http://www.w3.org/RDF/.

RDF Schema: http://www.w3.org/TR/rdf-schema/.

Web Ontology Language: http://www.w3.org/OWL/.

Knofuss: technologies.kmi.open.ac.uk/knofuss.

Silk: wifo5-03.informatik.uni-mannheim.de/bizer/silk.

Conference: data.semanticweb.org/dumps/conferences/dc-2010-complete.rdf.

BNF: datahub.io/fr/dataset/data-bnf-fr.

DBpedia: dbpedia.org.

http://datahub.io/group/lodcloud.

http://vocab.deri.ie/void.

The World Wide Web Consortium (w3c) - RDF 1.1 concepts and abstract syntax. https://www.w3.org/TR/rdf11-concepts/

Abedjan, Z., Gruetze, T., Jentzsch, A., Naumann, F.: Profiling and mining RDF data with ProLOD++. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 1198–1201. IEEE (2014)

Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: Optics: ordering points to identify the clustering structure. ACM Sigmod Record 28, 49–60 (1999). ACMCrossRef

Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A nucleus for a web of open data the semantic web. The Semantic Web (2007)

Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. ACM SIGMOD Record 19, 322–331 (1990). ACMCrossRef

Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy C-Means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)CrossRef

Böhm, C., Lorey, J., Naumann, F.: Creating void descriptions for web-scale data. Web Semant. Sci. Serv. Agents World Wide Web 9(3), 339–345 (2011)CrossRef

Böhm, C., Naumann, F., Abedjan, Z., Fenz, D., Grütze, T., Hefenbrock, D., Pohl, M., Sonnabend, D.: Profiling linked open data with ProLOD. In: 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW), pp. 175–178. IEEE (2010)

Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.-Y.: SPARQL web-querying infrastructure: ready for action? In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 277–293. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41338-4_18 CrossRef

10.

Christodoulou, K., Paton, N.W., Fernandes, A.A.A.: Structure inference for linked data sources using clustering. In: Hameurlain, A., Küng, J., Wagner, R., Bianchini, D., Antonellis, V., Virgilio, R. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX. LNCS, vol. 8990, pp. 1–25. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46562-2_1

11.

Clerkin, P., Cunningham, P., Hayes, C.: Ontology discovery for the semantic web using hierarchical clustering. Semantic Web Min. 1, 17 (2001)

12.

Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques (1991)

13.

Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. 39, 1–38 (1977)MathSciNetMATH

14.

Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proceedings of the 24th International Conference on Very Large Data Bases, pp. 323–333. Morgan Kaufmann Publishers Inc. (1998)

15.

Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)

16.

Fanizzi, N., Amato, C.D., Esposito, F.: Metric-based stochastic conceptual clustering for ontologies. Inf. Syst. 34(8), 792–806 (2009)CrossRef

17.

Fetahu, B., Dietze, S., Pereira Nunes, B., Antonio Casanova, M., Taibi, D., Nejdl, W.: A scalable approach for efficiently generating structured dataset topic profiles. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 519–534. Springer, Heidelberg (2014). doi:10.1007/978-3-319-07443-6_35 CrossRef

18.

Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2(2), 139–172 (1987)

19.

Heath, T., Bizer, C.: Linked data: Evolving the web into a global data space. Synth. Lect. Semant. Web Theor. Technol. 1(1), 1–136 (2011)CrossRef

20.

Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651–666 (2010)CrossRef

21.

Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. Reports of the Faculty of Mathematics and Informatics, Faculty of Mathematics and Informatics (1987)

22.

Kellou-Menouer, K., Kedad, Z.: Evaluating the gap between an RDF dataset and its schema. In: Jeusfeld, M.A., Karlapalem, K. (eds.) ER 2015. LNCS, vol. 9382, pp. 283–292. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25747-1_28 CrossRef

23.

Kellou-Menouer, K., Kedad, Z.: Schema discovery in RDF data sources. In: Johannesson, P., Lee, M.L., Liddle, S.W., Opdahl, A.L., López, Ó.P. (eds.) ER 2015. LNCS, vol. 9381, pp. 481–495. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25264-3_36 CrossRef

24.

Konrath, M., Gottron, T., Staab, S., Scherp, A.: Schemex: efficient construction of a data catalogue by stream-based indexing of linked data. Web Semant. Sci. Serv. Agents on the World Wide Web 16, 52–58 (2012)CrossRef

25.

Li, H.: Data profiling for semantic web data. In: Wang, F.L., Lei, J., Gong, Z., Luo, X. (eds.) WISM 2012. LNCS, vol. 7529, pp. 472–479. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33469-6_59 CrossRef

26.

Maali, F., Campinas, S., Decker, S.: Gagg: a graph aggregation operator. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 491–504. Springer, Heidelberg (2015). doi:10.1007/978-3-319-18818-8_30 CrossRef

27.

Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer Science & Business Media (2013)

28.

Naumann, F.: Data profiling revisited. ACM SIGMOD Record 42(4), 40–49 (2014)CrossRef

29.

Nestorov, S., Abiteboul, S., Motwani, R.: Extracting schema from semistructured data. ACM SIGMOD Record 27, 295–306 (1998). ACMCrossRef

30.

Pena, P.: Determining the similarity threshold for clustering algorithms in the logical combinatorial pattern recognition through a dendograme. In: 4th Iberoamerican Simposium of Pattern Recognition, pp. 259–265 (1999)

31.

Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008). doi:10.1007/978-3-540-68234-9_39 CrossRef

32.

Reyes-Gonzalez, R., Ruiz-Shulcloper, J.: An algorithm for restricted structuralization of spaces. Proc. IV SIARP 267 (1999)

33.

Rizzo, G., Fanizzi, N., d’Amato, C., Esposito, F.: Prediction of class and property assertions on OWL ontologies through evidence combination. In: Proceedings of the International Conference on Web Intelligence, Mining and Semantics, p. 45. ACM (2011)

34.

Sánchez-Díaz, G., Martínez-Trinidad, J.F.: Determination of similarity threshold in clustering problems for large data sets. In: Sanfeliu, A., Ruiz-Shulcloper, J. (eds.) CIARP 2003. LNCS, vol. 2905, pp. 611–618. Springer, Heidelberg (2003). doi:10.1007/978-3-540-24586-5_75 CrossRef

35.

Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web (2007)

36.

Wang, Q.Y., Yu, J.X., Wong, K.-F.: Approximate graph schema extraction for semi-structured data. In: Zaniolo, C., Lockemann, P.C., Scholl, M.H., Grust, T. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 302–316. Springer, Heidelberg (2000). doi:10.1007/3-540-46439-5_21 CrossRef

Titel: A Self-Adaptive and Incremental Approach for Data Profiling in the Semantic Web
verfasst von: Kenza Kellou-Menouer
Zoubida Kedad
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIX
Print ISBN: 978-3-662-54036-7

Electronic ISBN: 978-3-662-54037-4

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-662-54037-4_4

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