A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Component-based software engineering: putting the pieces together
Component-based software engineering: putting the pieces together
Introduction to Algorithms
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
An information retrieval approach to ontology mapping
Data & Knowledge Engineering - Special issue: Application of natural language to information systems (NLDB04)
ACM SIGMOD Record
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology Matching
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Model management 2.0: manipulating richer mappings
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
An approach to Ontology Mapping based on the Lucene search engine library
DEXA '07 Proceedings of the 18th International Conference on Database and Expert Systems Applications
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
LOM: a linguistic ontology matcher based on information retrieval
Journal of Information Science
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A cognitive support framework for ontology mapping
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Journal on data semantics X
A string metric for ontology alignment
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Matching large scale ontology effectively
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
A vector space model for semantic similarity calculation and OWL ontology alignment
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Editorial: BeAware!-Situation awareness, the ontology-driven way
Data & Knowledge Engineering
Scaling up top-K cosine similarity search
Data & Knowledge Engineering
ACM SIGMOD Record
Rule-based construction of matching processes
Proceedings of the 20th ACM international conference on Information and knowledge management
A differentor-based adaptive ontology-matching approach
Journal of Information Science
A unified approach to matching semantic data on the Web
Knowledge-Based Systems
An ontology-driven framework towards building enterprise semantic information layer
Advanced Engineering Informatics
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Ontology mapping, or matching, aims at identifying correspondences among entities in different ontologies. Several strands of research come up with algorithms often combining multiple mapping strategies to improve the mapping accuracy. However, few approaches have systematically investigated the requirements of a mapping system both from the functional (i.e., the features that are required) and user point of view (i.e., how the user can exploit these features). This paper presents an ontology mapping software framework that has been designed and implemented to help users (both expert and non-expert) in designing and/or exploiting comprehensive mapping systems. It is based on a library of mapping modules implementing functions such as discovering mappings or evaluating mapping strategies. In particular, the strategy predictor module of the designed framework, for each specific mapping task, can ''predict'' mapping modules to be exploited and parameter values (e.g., weights and thresholds). The implemented system, called UFOme, assists users during the various phases of a mapping task execution by providing a user friendly ontology mapping environment. The UFOme implementation and its prediction capabilities and accuracy were evaluated on the Ontology Alignment Evaluation Initiative tests with encouraging results.