TripleRank: Ranking Semantic Web Data by Tensor Decomposition
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Invited paper: Sig.ma: Live views on the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
When owl: sameAs isn't the same: an analysis of identity in linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
LIMES: a time-efficient approach for large-scale link discovery on the web of data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Graph-based ontology analysis in the linked open data
Proceedings of the 8th International Conference on Semantic Systems
ICDEW '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering Workshops
Discovering concept coverings in ontologies of linked data sources
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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Linked Open Data (LOD) cloud has gained significant attention in the Semantic Web community recently. Currently it consists of approximately 295 interlinked datasets with over 50 billion triples including 500 million links, and continues to expand in size. This vast source of structured information has the potential to have a significant impact on knowledge-based applications. However, a key impediment to the use of LOD cloud is limited support for data integration tasks over concepts, instances, and properties. Efforts to address this limitation over properties have focused on matching data-type properties across datasets; however, matching of object-type properties has not received similar attention. We present an approach that can automatically match object-type properties across linked datasets, primarily exploiting and bootstrapping from entity co-reference links such as owl:sameAs. Our evaluation, using sample instance sets taken from Freebase, DBpedia, LinkedMDB, and DBLP datasets covering multiple domains shows that our approach matches properties with high precision and recall (on average, F measure gain of 57% - 78%).