An Automatic Instance Expansion Framework for Mapping Instances to Linked Data Resources

Linked Data is an utterly valuable component for semantic technologies because it can be used for accessing and distributing knowledge from one data source to other data sources via structured links. Therefore, mapping instances to Linked Data resources plays a key role for consuming knowledge in Linked Data resources so that we can understand instances more precisely. Since an instance, which can be aligned to Linked Data resources, is enriched its information by other instances, the instance then is full of information, which perfectly describes itself. Nevertheless, mapping instances to Linked Data resources is still challenged due to the heterogeneity problem and the multiple data source problem as well. Most techniques focus on mapping instances between two specific data sources and deal with the heterogeneity problem. Mapping instances particularly relying on two specific data sources is not enough because it will miss an opportunity to map instances to other sources. We therefore present the Instance Expansion Framework, which automatically discover and map instances more than two specific data sources in Linked Data resources. The framework consists of three components: Candidate Selector, Instance Matching and Candidate Expander. Experiments show that the Candidate Expander component is significantly important for mapping instances to Linked Data resources.

[1]  Ian Horrocks,et al.  OWL Web Ontology Language Reference-W3C Recommen-dation , 2004 .

[2]  Yi Li,et al.  RiMOM: A Dynamic Multistrategy Ontology Alignment Framework , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Christian Bizer,et al.  The Berlin SPARQL Benchmark , 2009, Int. J. Semantic Web Inf. Syst..

[4]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[5]  François Scharffe,et al.  Final results of the ontology alignment evaluation initiative 2011 , 2011 .

[6]  Tim Berners-Lee,et al.  Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..

[7]  Ryutaro Ichise,et al.  Learning approach for domain-independent linked data instance matching , 2012, MDS '12.

[8]  Jeff Heflin,et al.  The Semantic Web – ISWC 2012 , 2012, Lecture Notes in Computer Science.

[9]  Haofen Wang,et al.  Zhishi.links results for OAEI 2011 , 2011, OM.

[10]  Ryutaro Ichise,et al.  SLINT: a schema-independent linked data interlinking system , 2012, OM.

[11]  Abraham Bernstein,et al.  The Semantic Web - ISWC 2009, 8th International Semantic Web Conference, ISWC 2009, Chantilly, VA, USA, October 25-29, 2009. Proceedings , 2009, SEMWEB.

[12]  Ryutaro Ichise,et al.  Interlinking Linked Data Sources Using a Domain-Independent System , 2012, JIST.

[13]  William W. Cohen,et al.  A Comparison of String Metrics for Matching Names and Records , 2003 .

[14]  Heiner Stuckenschmidt,et al.  Results of the Ontology Alignment Evaluation Initiative , 2007 .

[15]  Federico Caimi,et al.  Ontology and Instance Matching for the Linked Open Data Cloud , 2012 .

[16]  Jan Hidders,et al.  SERIMI: Class-based Disambiguation for Effective Instance Matching over Heterogeneous Web Data , 2012, WebDB.

[17]  Martin Gaedke,et al.  Discovering and Maintaining Links on the Web of Data , 2009, SEMWEB.

[18]  Yuzhong Qu,et al.  A self-training approach for resolving object coreference on the semantic web , 2011, WWW.

[19]  Qiang Yang,et al.  A Machine Learning Approach for Instance Matching Based on Similarity Metrics , 2012, SEMWEB.