Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Semantic integration of semistructured and structured data sources
ACM SIGMOD Record
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
The object data standard: ODMG 3.0
The object data standard: ODMG 3.0
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Clustering seasonality patterns in the presence of errors
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Synthesizing an Integrated Ontology
IEEE Internet Computing
Natural Language Engineering
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
Ontology Matching
Domain kernels for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Ensemble methods for unsupervised WSD
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Management of probabilistic data: foundations and challenges
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incorporating Uncertainty Metrics into a General-Purpose Data Integration System
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Bootstrapping pay-as-you-go data integration systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Word Sense Disambiguation as the Primary Step of Ontology Integration
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Schema Matching and Mapping-based Data Integration: Architecture, Approaches and Evaluation
Schema Matching and Mapping-based Data Integration: Architecture, Approaches and Evaluation
Data integration with uncertainty
The VLDB Journal — The International Journal on Very Large Data Bases
Schema Normalization for Improving Schema Matching
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
Mapping validation by probabilistic reasoning
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implementing database access control policy from unconstrained natural language text
Proceedings of the 2013 International Conference on Software Engineering
Building linked ontologies with high precision using subclass mapping discovery
Artificial Intelligence Review
Semantic annotation of the CEREALAB database by the AGROVOC linked dataset
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
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Schema matching is the problem of finding relationships among concepts across data sources that are heterogeneous in format and in structure. Starting from the ''hidden meaning'' associated with schema labels (i.e. class/attribute names), it is possible to discover lexical relationships among the elements of different schemata. In this work, we propose an automatic method aimed at discovering probabilistic lexical relationships in the environment of data integration ''on the fly''. Our method is based on a probabilistic lexical annotation technique, which automatically associates one or more meanings with schema elements w.r.t. a thesaurus/lexical resource. However, the accuracy of automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns and abbreviations. We address this problem by including a method to perform schema label normalization which increases the number of comparable labels. From the annotated schemata, we derive the probabilistic lexical relationships to be collected in the Probabilistic Common Thesaurus. The method is applied within the MOMIS data integration system but can easily be generalized to other data integration systems.