An algorithm for suffix stripping
Readings in information retrieval
ACM SIGMOD Record
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Matching
Learning Disjointness for Debugging Mappings between Lightweight Ontologies
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Linking and building ontologies of linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Linked Data
Mid-Ontology learning from linked data
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
A statistical and schema independent approach to identify equivalent properties on linked data
Proceedings of the 9th International Conference on Semantic Systems
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The Linked Open Data (LOD) includes over 31 billion Resource Description Framework (RDF) triples interlinked by around 504 million SameAs links (as of September 2011). The data sets of the LOD use different ontologies to describe instances, that cause the ontology heterogeneity problem. Dealing with the heterogeneous ontologies is a challenging problem and it is time-consuming to manually learn big ontologies in the LOD. The heterogeneity of ontologies in the LOD can be reduced by automatically integrating related ontology classes and properties, which can be retrieved from interlinked instances. The interlinked instances can be represented as an undirected graph, from which we can discover the characteristics of instances and retrieve related ontology classes and properties that are important for linking instances. In this paper, we retrieve graph patterns from several linked data sets and perform ontology alignment methods on each graph pattern to identify related ontology classes and properties from the data sets. We successfully integrate various ontologies, analyze the characteristics of interlinked instances, and detect mistaken properties in the real data sets. Furthermore, our approach solves the ontology heterogeneity problem and helps Semantic Web application developers easily query on various data sets with the integrated ontology.