Production matching for large learning systems
Production matching for large learning systems
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Ontology Matching
Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Linking Life Sciences Data Using Graph-Based Mapping
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Web Semantics: Science, Services and Agents on the World Wide Web
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
SKIMMR: machine-aided skim-reading
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Proceedings of the 19th international conference on Intelligent User Interfaces
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We aim at providing a complementary layer for the web semantics, catering for bottom-up phenomena that are empirically observable on the Semantic Web rather than being merely asserted by it. We focus on meaning that is not associated with particular semantic descriptions, but emerges from the multitude of explicit and implicit links on the web of data. We claim that the current approaches are mostly top-down and thus lack a proper mechanisms for capturing the emergent aspects of the web meaning. To fill this gap, we have proposed a framework based on distributional semantics (a successful bottom-up approach to meaning representation in computational linguistics) that is, however, still compatible with the top-down Semantic Web principles due to inherent support of rules. We evaluated our solution in a knowledge consolidation experiment, which confirmed the promising potential of our approach.