Towards a standard upper ontology
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Web-scale information extraction in knowitall: (preliminary results)
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Schema and ontology matching with COMA++
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Combining linguistic and statistical analysis to extract relations from web documents
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Collective entity resolution in relational data
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Yago: a core of semantic knowledge
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Leveraging data and structure in ontology integration
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ACM Computing Surveys (CSUR)
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Large scale integration of senses for the semantic web
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Large-Scale Deduplication with Constraints Using Dedupalog
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Combining a Logical and a Numerical Method for Data Reconciliation
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RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
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Deriving a large scale taxonomy from Wikipedia
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Ontology matching with semantic verification
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Sig.ma: live views on the web of data
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When owl: sameAs isn't the same: an analysis of identity in linked data
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A self-training approach for resolving object coreference on the semantic web
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The ERC webdam on foundations of web data management
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LINDA: distributed web-of-data-scale entity matching
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SIGMa: simple greedy matching for aligning large knowledge bases
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Fria: fast and robust instance alignment
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AMIE: association rule mining under incomplete evidence in ontological knowledge bases
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Colledge: a vision of collaborative knowledge networks
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Aligning freebase with the YAGO ontology
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PIDGIN: ontology alignment using web text as interlingua
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Big data challenge: a data management perspective
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Mining rules to align knowledge bases
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Integration of large scale knowledge bases using probabilistic graphical models
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An automatic key discovery approach for data linking
Web Semantics: Science, Services and Agents on the World Wide Web
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One of the main challenges that the Semantic Web faces is the integration of a growing number of independently designed ontologies. In this work, we present paris, an approach for the automatic alignment of ontologies. paris aligns not only instances, but also relations and classes. Alignments at the instance level cross-fertilize with alignments at the schema level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic, i.e., we measure degrees of matchings based on probability estimates. This allows paris to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with some of the world's largest ontologies.