Mining RDF Data for Property Axioms

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.

[1]  Patrick Pantel,et al.  DIRT @SBT@discovery of inference rules from text , 2001, KDD '01.

[2]  Johanna Völker,et al.  Statistical Schema Induction , 2011, ESWC.

[3]  Ian Horrocks,et al.  The Even More Irresistible SROIQ , 2006, KR.

[4]  J. Fleiss.  Measuring nominal scale agreement among many raters. , 1971 .

[5]  Jens Lehmann,et al.  Learning of OWL Class Descriptions on Very Large Knowledge Bases , 2008, SEMWEB.

[6]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[7]  Johanna Völker,et al.  Inductive Learning of Disjointness Axioms , 2011, OTM Conferences.

[8]  Tao Jiang,et al.  Mining RDF Metadata for Generalized Association Rules , 2006, DEXA.

[9]  Catherine Faron-Zucker,et al.  Learning ontologies from RDF annotation , 2001 .

[10]  Jeffrey M. Bradshaw,et al.  Applying KAoS Services to Ensure Policy Compliance for Semantic Web Services Workflow Composition and Enactment , 2004, SEMWEB.

[11]  Jens Lehmann,et al.  DL-Learner: Learning Concepts in Description Logics , 2009, J. Mach. Learn. Res..

[12]  Oren Etzioni,et al.  Identifying Functional Relations in Web Text , 2010, EMNLP.

[13]  Nicolás García-Pedrajas,et al.  Trends in Applied Intelligent Systems - 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part I , 2010, IEA/AIE.

[14]  Felix Naumann,et al.  Ontology ( Re-) Engineering through Large-scale Data Mining , 2011 .

[15]  Lora Aroyo,et al.  The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I , 2011, SEMWEB.

[16]  Alun D. Preece,et al.  Learning Meta-descriptions of the FOAF Network , 2004, SEMWEB.

[17]  Ian Horrocks,et al.  The Semantic Web – ISWC 2010: 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I , 2010, SEMWEB.

[18]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[19]  Mikolaj Morzy,et al.  Efficient Mining of Dissociation Rules , 2006, DaWaK.

[20]  David Sánchez,et al.  Discovery of Relation Axioms from the Web , 2010, KSEM.

[21]  Georg Lausen,et al.  SPARQLing constraints for RDF , 2008, EDBT '08.

[22]  Christian Borgelt,et al.  Induction of Association Rules: Apriori Implementation , 2002, COMPSTAT.

[23]  Chengqi Zhang,et al.  Association Rule Mining , 2002, Lecture Notes in Computer Science.

[24]  Osmar R. Zaïane,et al.  Mining Positive and Negative Association Rules: An Approach for Confined Rules , 2004, PKDD.

[25]  Jérôme David,et al.  Association Rule Ontology Matching Approach , 2007, Int. J. Semantic Web Inf. Syst..

[26]  Dekang Lin,et al.  DIRT – Discovery of Inference Rules from Text , 2001 .

[27]  Lukas Biewald,et al.  Programmatic Gold: Targeted and Scalable Quality Assurance in Crowdsourcing , 2011, Human Computation.

[28]  Steffen Staab,et al.  Discovering Conceptual Relations from Text , 2000, ECAI.

[29]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[30]  Rafael Berlanga Llavori,et al.  Mining Association Rules from Semantic Web Data , 2010, IEA/AIE.

[31]  Shichao Zhang,et al.  Association Rule Mining: Models and Algorithms , 2002 .

[32]  Andreas Hotho,et al.  Semantic Web Mining: State of the art and future directions , 2006, J. Web Semant..

[33]  Tharam S. Dillon,et al.  On the Move to Meaningful Internet Systems, OTM 2010 , 2010, Lecture Notes in Computer Science.

[34]  Catherine Faron-Zucker,et al.  Learning Ontologies from RDF annotations , 2001, Workshop on Ontology Learning.

[35]  Alexander Maedche,et al.  Clustering Ontology-Based Metadata in the Semantic Web , 2002, PKDD.

[36]  Jeff Heflin,et al.  Extending Functional Dependency to Detect Abnormal Data in RDF Graphs , 2011, SEMWEB.

[37]  Wenfei Fan,et al.  Keys with Upward Wildcards for XML , 2001, DEXA.

[38]  Oren Etzioni,et al.  Learning First-Order Horn Clauses from Web Text , 2010, EMNLP.

[39]  Dino Pedreschi,et al.  Knowledge Discovery in Databases: PKDD 2004 , 2004, Lecture Notes in Computer Science.

[40]  Lora Aroyo,et al.  The Semantic Web: Research and Applications , 2009, Lecture Notes in Computer Science.

[41]  Craig A. Knoblock,et al.  Linking and Building Ontologies of Linked Data , 2010, SEMWEB.