PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Leveraging data and structure in ontology integration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Merging models based on given correspondences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Data integration with uncertainty
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Schema merging and mapping creation for relational sources
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Interactive generation of integrated schemas
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Bootstrapping pay-as-you-go data integration systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Top-k generation of integrated schemas based on directed and weighted correspondences
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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Schema integration plays a central role in numerous database applications, such as Deep Web, DataSpaces and Ontology Merging. Although there have been many researches on schema integration, they all neglect user preference which is a very important factor for improving the quality of mediated schemas. In this paper, we propose the automatic multi-schema integration based on user preference. A new concept named reference schema is introduced to represent user preference. This concept can guide the process of integration to generate mediated schemas according to user preference. Different from previous solutions, our approach employs F-measure and "attribute density" to measure the similarity between schemas. Based on this similarity, we design a top-k ranking algorithm that retrieves k mediate schemas which users really expect. The key component of the algorithm is a pruning strategy which makes use of Divide and Conquer to narrow down the search space of the candidate schemas. Finally, the experimental study demonstrates the effectiveness and good performance of our approach.