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The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

Published online by Cambridge University Press:  17 May 2010

JOANNA JÓZEFOWSKA ,
AGNIESZKA ŁAWRYNOWICZ  and
TOMASZ ŁUKASZEWSKI
JOANNA JÓZEFOWSKA
Affiliation:
Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznan, Poland (e-mail: [email protected], [email protected], [email protected])
AGNIESZKA ŁAWRYNOWICZ
Affiliation:
Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznan, Poland (e-mail: [email protected], [email protected], [email protected])
TOMASZ ŁUKASZEWSKI
Affiliation:
Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60-965 Poznan, Poland (e-mail: [email protected], [email protected], [email protected])
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Abstract

We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

Keywords

frequent pattern discoveryontologiesSemantic WebDL-safe rules
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 10 , Special Issue 3: Logic Programming in Databases: from Datalog to Semantic-Web Rules , May 2010 , pp. 251 - 289
Copyright
Copyright © Cambridge University Press 2010

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References

Baader, F., Calvanese, D., McGuinness, D., Nardi, D. and Patel-Schneider, P., Eds. 2003. The description logic handbook: Theory, implementation and applications. Cambridge University Press, Cambridge.Google Scholar
Berners-Lee, T., Hendler, J. and Lassila, O. 2001. The Semantic Web. Scientific American 284, 5, 34–43.Google Scholar
Calì, A., Gottlob, G. and Lukasiewicz, T. 2009. A general Datalog-based framework for tractable query answering over ontologies. In PODS, Paredaens, J. and Su, J., Eds. ACM, 77–86.Google Scholar
Calvanese, D., Giacomo, G., Lembo, D., Lenzerini, M. and Rosati, R. 2007. Tractable reasoning and efficient query answering in description logics: The DL-Lite family. Journal of Automated Reasoning 39, 3, 385–429.Google Scholar
Cumbo, C., Faber, W., Greco, G. and Leone, N. 2004. Enhancing the magic-set method for disjunctive Datalog programs. In Proc. of the 20th International Conference on Logic Programming (ICLP'04). 371–385.CrossRefGoogle Scholar
d'Amato, C., Staab, S. and Fanizzi, N. 2008. On the influence of description logics ontologies on conceptual similarity. In EKAW, Gangemi, A. and Euzenat, J., Eds. Lecture Notes in Computer Science, vol. 5268. Springer, 48–63.Google Scholar
de Raedt, L. and Ramon, J. 2004. Condensed representations for inductive logic programming. In Proc. of the Ninth International Conference on Principles of Knowledge Representation and Reasoning (KR 2004). 438–446.Google Scholar
Dehaspe, L. and Toivonen, H. 1999. Discovery of frequent Datalog patterns. Data Mining and Knowledge Discovery 3, 1, 7–36.CrossRefGoogle Scholar
Dehaspe, L., Toivonen, H. and King, R. D. 1998. Finding frequent substructures in chemical compounds. In Proc. of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD'1998). AAAI Press, 30–36.Google Scholar
Donini, F., Lenzerini, M., Nardi, D. and Schaerf, A. 1998. -log: Integrating Datalog and description logics. Journal of Intelligent Information Systems 10, 3, 227–252.Google Scholar
Dzeroski, S. and Lavrac, N., Eds. 2001. Relational Data Mining. Springer.Google Scholar
Eiter, T., Gottlob, G. and Mannila, H. 1997. Disjunctive Datalog. ACM Transactions on Database Systems 22, 3, 364–418.CrossRefGoogle Scholar
Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2008. Combining answer set programming with description logics for the Semantic Web. Artificial Intelligence 172, 12-13, 1495–1539.CrossRefGoogle Scholar
Eiter, T., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2004a. Combining answer set programming with description logics for the Semantic Web. In Proc. of the International Conference of Knowledge Representation and Reasoning (KR 2004). 141–151.Google Scholar
Eiter, T., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2004b. Well-founded semantics for description logic programs in the Semantic Web. In Proceedings of the 3rd International Workshop on Rules and Rule Markup Languages for the Semantic Web (RuleML-2004). 81–97.Google Scholar
Fanizzi, N. and d'Amato, C. 2006. A declarative kernel for concept descriptions. In Foundations of Intelligent Systems, 16th International Symposium, Esposito, F., Ras, Z. W., Malerba, D. and Semeraro, G., Eds. Lecture Notes in Computer Science, vol. 4203. Springer, Berlin–Heidelberg, Germany, 322–331.Google Scholar
Fanizzi, N., D'Amato, C. and Esposito, F. 2008. Statistical learning for inductive query answering on OWL ontologies. In ISWC'08: Proceedings of the 7th International Conference on The Semantic Web. Springer-Verlag, Berlin, 195–212.Google Scholar
Grosof, B. N., Horrocks, I., Volz, R. and Decker, S. 2003. Description logic programs: Combining logic programs with description logic. In Proc. of the 12th International World Wide Web Conference (WWW 2003). ACM Press, 48–57.CrossRefGoogle Scholar
Horrocks, I., Patel-Schneider, P., Boley, H., Tabet, S., Grosof, B. and Dean, M. 2004. SWRL: A Semantic Web rule language combining OWL and RuleML. W3C Member Submission. Available at URL: http://www.w3.org/Submission/SWRL/.Google Scholar
Hustadt, U., Motik, B. and Sattler, U. 2004. Reducing description logic to disjunctive Datalog programs. In Proc. of the 9th International Conference on the Principles of Knowledge Representation and Reasoning (KR 2004). AAAI Press, 152–162.Google Scholar
Hustadt, U., Motik, B. and Sattler, U. 2005. Data complexity of reasoning in very expressive description logics. In Proc. of IJCAI 2005. 466–471.Google Scholar
Hustadt, U., Motik, B. and Sattler, U. 2007. Reasoning in description logics by a reduction to disjunctive Datalog. Journal of Automated Reasoning 39, 3, 351–384.Google Scholar
Józefowska, J., Ławrynowicz, A. and Łukaszewski, T. 2005. Towards discovery of frequent patterns in description logics with rules. In Proc. of International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML 2005), Adi, A., Stoutenburg, S. and Tabet, S., Eds. Lecture Notes in Computer Science, vol. 3791. Springer, 84–97.CrossRefGoogle Scholar
Józefowska, J., Ławrynowicz, A. and Łukaszewski, T. 2006. Frequent pattern discovery in OWL DLP knowledge bases. In Managing Knowledge in a World of Networks, Proc. of EKAW 2006, Staab, S. and Svatek, V., Eds. Lecture Notes in Artificial Intelligence, vol. 4248. Springer, 287–302.Google Scholar
Józefowska, J., Ławrynowicz, A. and Łukaszewski, T. 2008. On reducing redundancy in mining relational association rules from the Semantic Web. In Proc. of the Second International Conference on Web Reasoning and Rule Systems (RR'2008), Calvanese, D. and Lausen, G., Eds. Lecture Notes in Computer Science, vol. 5341. Springer, 205–213.Google Scholar
King, R. D., Karwath, A., Clare, A. and Dehaspe, L. 2000a. Accurate prediction of protein class in the M. tuberculosis and E. coli genomes using data mining. Yeast (Comparative and Functional Genomics) 17, 4, 283–293.Google Scholar
King, R. D., Karwath, A., Clare, A. and Dehaspe, L. 2000b. Genome scale prediction of protein functional class from sequence using data mining. In Proc. of the Sixth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'2000). 384–389.CrossRefGoogle Scholar
King, R. D., Karwath, A., Clare, A. and Dehaspe, L. 2001. The utility of different representations of protein sequence for predicting functional class. Bioinformatics 17, 5, 445–454.CrossRefGoogle ScholarPubMed
Levy, A. and Rousset, M.-C. 1998. Combining Horn rules and description logics in CARIN. Artificial Intelligence 104, 1-2, 165–209.Google Scholar
Lisi, F. 2007. Reasoning with OWL-DL in inductive logic programming. In Proc. of the Third International Workshop, OWL: Experiences and Directions (OWLED 2007).Google Scholar
Lisi, F. and Malerba, D. 2004. Inducing multi-level association rules from multiple relations. Machine Learning Journal 55, 2, 175–210.Google Scholar
Lisi, F. A. and Esposito, F. 2008. Foundations of onto-relational learning. In ILP, Zelezný, F. and Lavrac, N., Eds. Lecture Notes in Computer Science, vol. 5194. Springer, 158–175.Google Scholar
Lukasiewicz, T. 2007. A novel combination of answer set programming with description logics for the Semantic Web. In ESWC, Franconi, E., Kifer, M. and May, W., Eds. Lecture Notes in Computer Science, vol. 4519. Springer, 384–398.Google Scholar
McGuinness, D. and van Harmelen, F. 2004. OWL Web ontology language overview. W3C Recommendation. Available at URL: http://www.w3.org/TR/owl-features/.Google Scholar
Motik, B. 2006. Reasoning in Description Logics Using Resolution and Deductive Databases. PhD thesis, Universitaet Karlsruhe (TH), Karlsruhe, Germany.Google Scholar
Motik, B. and Rosati, R. 2007. A faithful integration of description logics with logic programming. In Proc. of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007). 477–482.Google Scholar
Motik, B. and Sattler, U. 2006. A comparison of reasoning techniques for querying large description logic ABoxes. In Proc. of the 13th International Conference on Logic for Programming Artificial Intelligence and Reasoning (LPAR 2006).Google Scholar
Motik, B., Sattler, U. and Studer, R. 2005. Query answering for OWL-DL with rules. Journal of Web Semantics: Science, Services and Agents on the World Wide Web 3, 1, 41–60.CrossRefGoogle Scholar
Nienhuys-Cheng, S. and de Wolf, R. 1997. Foundations of inductive logic programming. Lecture Notes in Artificial Intelligence, vol. 1228. Springer.CrossRefGoogle Scholar
Nijssen, S. and Kok, J. 2001. Faster association rules for multiple relations. In Proc. of the 17th International Joint Conference on Artificial Intelligence (IJCAI'2001). 891–897.Google Scholar
Nijssen, S. and Kok, J. 2003. Efficient frequent query discovery in FARMER. In Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2003). Lecture Notes in Artificial Intelligence, vol. 2431. 350–362.Google Scholar
Parsia, B., Kolovski, V. and Sirin, E. 2006. Extending the (D) tableaux with DL-safe rules: First results. In Proc. of the Int. Description Logics Workshop (DL 2006), CEUR Workshop Proceedings 189.Google Scholar
Rosati, R. 2006. + log: Tight integration of description logics and disjunctive Datalog. In Proc. of KR 2006, 68–78.Google Scholar
Ruttenberg, A., Rees, J. and Luciano, J. 2005. Experience using OWL DL for the exchange of biological pathway information. In Proc. of OWL-ED 05. Vol. 188. CEUR.Google Scholar
Stevens, R., Aranguren, M. E., Wolstencroft, K., Sattler, U., Drummond, N., Horridge, M. and Rector, A. L. 2007. Using OWL to model biological knowledge. International Journal of Man-Machine Studies 65, 7, 583–594.Google Scholar