Mining RDF Data for Property Axioms

详细信息    查看全文

• 作者：Daniel Fleischhacker (26)
  Johanna V?lker (26)
  Heiner Stuckenschmidt (26)
  
• 关键词：Linked Data ; Ontology Learning ; OWL2 ; Property Characteristics
• 刊名：Lecture Notes in Computer Science
• 出版年：2012
• 出版时间：2012
• 年：2012
• 卷：7566
• 期：1
• 页码：736-751
• 全文大小：615KB
• 参考文献：1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th Intl. Conference on Very Large Data Bases (VLDB), pp. 487-99. Morgan Kaufmann (1994)
  2. Antonie, M.-L., Za?ane, O.R.: Mining Positive and Negative Association Rules: An Approach for Confined Rules. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol.?3202, pp. 27-8. Springer, Heidelberg (2004) CrossRef
  3. Borgelt, C., Kruse, R.: Induction of association rules: Apriori implementation. In: Proc. of the 15th Conference on Computational Statistics (COMPSTAT), pp. 395-00. Physica Verlag (2002)
  4. David, J., Guillet, F., Briand, H.: Association rule ontology matching approach. International Journal on Semantic Web and Information Systems?3(2), 27-9 (2007) CrossRef
  5. Del Vasto Terrientes, L., Moreno, A., Sánchez, D.: Discovery of Relation Axioms from the Web. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS, vol.?6291, pp. 222-33. Springer, Heidelberg (2010) CrossRef
  6. Delteil, A., Faron-Zucker, C., Dieng, R.: Learning ontologies from rdf annotations. In: Proceedings of the 2nd Workshop on Ontology Learning (OL) at the 17th International Conference on Artificial Intelligence (IJCAI). CEUR Workshop Proceedings, vol.?38. CEUR-WS.org (2001)
  7. Fleischhacker, D., V?lker, J.: Inductive Learning of Disjointness Axioms. In: Meersman, R., Dillon, T., Herrero, P., Kumar, A., Reichert, M., Qing, L., Ooi, B.-C., Damiani, E., Schmidt, D.C., White, J., Hauswirth, M., Hitzler, P., Mohania, M. (eds.) OTM 2011, Part II. LNCS, vol.?7045, pp. 680-97. Springer, Heidelberg (2011) CrossRef
  8. Fleiss, J.L.: Measuring nominal scale agreement among many rater. Psychological Bulletin?76, 378-82 (1971) CrossRef
  9. Grimnes, G.A., Edwards, P., Preece, A.D.: Learning Meta-descriptions of the FOAF Network. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol.?3298, pp. 152-65. Springer, Heidelberg (2004) CrossRef
  10. Hellmann, S., Lehmann, J., Auer, S.: Learning of OWL class descriptions on very large knowledge bases. Intl. Journal on Semantic Web and Information Systems?5(2), 25-8 (2009) CrossRef
  11. Horrocks, I., Kutz, O., Sattler, U.: The even more irresistible SROIQ. In: Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning 2006, pp. 57-7. AAAI Press (2006)
  12. Jiang, T., Tan, A.-H.: Mining RDF Metadata for Generalized Association Rules. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol.?4080, pp. 223-33. Springer, Heidelberg (2006) CrossRef
  13. Lausen, G., Meier, M., Schmidt, M.: Sparqling constraints for rdf. In: Proceedings of the 11th International Conference on Extending Database Technology (EDBT): Advances in Database Technology, pp. 499-09. ACM, New York (2008) CrossRef
  14. Lehmann, J.: DL-Learner: learning concepts in description logics. Journal of Machine Learning Research (JMLR)?10, 2639-642 (2009)
  15. Lin, D., Pantel, P.: Dirt -discovery of inference rules from text. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 323-28. ACM, New York (2001) CrossRef
  16. Lin, T., Mausam, Etzioni, O.: Identifying functional relations in web text. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1266-276. Association for Computational Linguistics, Cambridge (2010)
  17. Lorey, J., Abedjan, Z., Naumann, F., B?hm, C.: Rdf ontology (re-)engineering through large-scale data mining. In: International Semantic Web Conference (ISWC) (November 2011); Finalist of the Billion Triple Challenge
  18. Morzy, M.: Efficient Mining of Dissociation Rules. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol.?4081, pp. 228-37. Springer, Heidelberg (2006) CrossRef
  19. M?dche, A., Staab, S.: Discovering conceptual relations from text. In: Proceedings of the 14th European Conference on Artificial Intelligence (ECAI), pp. 321-25. IOS Press (2000)
  20. Maedche, A., Zacharias, V.: Clustering Ontology-Based Metadata in the Semantic Web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol.?2431, pp. 348-60. Springer, Heidelberg (2002) CrossRef
  21. Nebot, V., Berlanga, R.: Mining Association Rules from Semantic Web Data. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010, Part II. LNCS, vol.?6097, pp. 504-13. Springer, Heidelberg (2010) CrossRef
  22. Oleson, D., Sorokin, A., Laughlin, G.P., Hester, V., Le, J., Biewald, L.: Programmatic gold: Targeted and scalable quality assurance in crowdsourcing. In: Human Computation. AAAI Workshops, vol. WS-11-11. AAAI (2011)
  23. Parundekar, R., Knoblock, C.A., Ambite, J.L.: Linking and Building Ontologies of Linked Data. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol.?6496, pp. 598-14. Springer, Heidelberg (2010) CrossRef
  24. Schoenmackers, S., Etzioni, O., Weld, D.S., Davis, J.: Learning first-order horn clauses from web text. In: Proceedings of the International Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1088-098. ACL (October 2010)
  25. Stumme, G., Hotho, A., Berendt, B.: Semantic web mining: State of the art and future directions. Journal of Web Semantics?4(2), 124-43 (2006) CrossRef
  26. V?lker, J., Niepert, M.: Statistical Schema Induction. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol.?6643, pp. 124-38. Springer, Heidelberg (2011) CrossRef
  27. Yu, Y., Heflin, J.: Extending Functional Dependency to Detect Abnormal Data in RDF Graphs. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol.?7031, pp. 794-09. Springer, Heidelberg (2011) CrossRef
  28. Zhang, C., Zhang, S.: Association rule mining: models and algorithms. Springer, Heidelberg (2002) CrossRef
  
• 作者单位：Daniel Fleischhacker (26)
  Johanna V?lker (26)
  Heiner Stuckenschmidt (26)
  
  26. KR & KM Research Group, University of Mannheim, Germany
  
• ISSN：1611-3349

文摘

The Linked Data cloud grows rapidly as more and more knowledge bases become available as Linked Data. Knowledge-based applications have to rely on efficient implementations of query languages like SPARQL, in order to access the information which is contained in large datasets such as DBpedia, Freebase or one of the many domain-specific RDF repositories. However, the retrieval of specific facts from an RDF dataset is often hindered by the lack of schema knowledge, that would allow for query-time inference or the materialization of implicit facts. For example, if an RDF graph contains information about films and actors, but only Titanic starring Leonardo_DiCaprio is stated explicitly, a query for all movies Leonardo DiCaprio acted in might not yield the expected answer. Only if the two properties starring and actedIn are declared inverse by a suitable schema, the missing link between the RDF entites can be derived. In this work, we present an approach to enriching the schema of any RDF dataset with property axioms by means of statistical schema induction. The scalability of our implementation, which is based on association rule mining, as well as the quality of the automatically acquired property axioms are demonstrated by an evaluation on DBpedia.