Contextual ontology alignment of LOD with an upper ontology: a case study with proton

  • Authors:

  • Prateek Jain;Peter Z. Yeh;Kunal Verma;Reymonrod G. Vasquez;Mariana Damova;Pascal Hitzler;Amit P. Sheth

  • Affiliations:

  • Kno.e.sis Center, Wright State University, Dayton, OH and Accenture Technology Labs, San Jose, CA;Accenture Technology Labs, San Jose, CA;Accenture Technology Labs, San Jose, CA;Accenture Technology Labs, San Jose, CA;Ontotext AD, Sofia, Bulgaria;Kno.e.sis Center, Wright State University, Dayton, OH;Kno.e.sis Center, Wright State University, Dayton, OH

  • Venue:
  • ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
  • Year:
  • 2011

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Abstract

The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies - in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) - created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.