Conceptual schema analysis: techniques and applications
ACM Transactions on Database Systems (TODS)
Comparison of Schema Matching Evaluations
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Candidate reduction and alignment improvement techniques used in aligning ontologies
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
| Hi-index | 0.00 |
Ontology alignment is a crucial task to enable interoperability among different agents. However, the complexity of the alignment task especially for large ontologies requires automated support for the creation of alignment methods. When looking at current ontology alignment methods one can see that they are either not optimized for given ontologies or their optimization by machine learning means is mostly restricted to the extensional definition of ontologies. With APFEL (Alignment Process Feature Estimation and Learning) we present a machine learning approach that explores the user validation of initial alignments for optimizing alignment methods. The methods are based on extensional and intensional ontology definitions. Core to APFEL is the idea of a generic alignment process, the steps of which may be represented explicitly. APFEL then generates new hypotheses for what might be useful features and similarity assessments and weights them by machine learning approaches. APFEL compares favorably in our experiments to competing approaches.