Data mining: concepts and techniques
Data mining: concepts and techniques
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Information-Flow-Based Ontology Mapping
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
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
Multiple strategies detection in ontology mapping
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Automatic complex schema matching across Web query interfaces: A correlation mining approach
ACM Transactions on Database Systems (TODS)
Constructing virtual documents for ontology matching
Proceedings of the 15th international conference on World Wide Web
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Combining Uncertain Outputs from Multiple Ontology Matchers
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A survey of schema-based matching approaches
Journal on Data Semantics IV
Partition-Based block matching of large class hierarchies
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
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Ontology mapping is one of the most important tasks for ontology interoperability and its main aim is to find semantic relationships between entities (i.e. concept, attribute, and relation) of two ontologies. However, most of the current methods only consider one to one (1:1) mappings. In this paper we propose a new approach (CHM: Concept Hierarchy based Mapping approach) which can find simple (1:1) mappings and complex (m:1 or 1:m) mappings simultaneously. First, we propose a new method to represent the concept names of entities. This method is based on the hierarchical structure of an ontology such that each concept name of entity in the ontology is included in a set. The parent-child relationship in the hierarchical structure of an ontology is then extended as a set-inclusion relationship between the sets for the parent and the child. Second, we compute the similarities between entities based on the new representation of entities in ontologies. Third, after generating the mapping candidates, we select the best mapping result for each source entity. We design a new algorithm based on the Apriori algorithm for selecting the mapping results. Finally, we obtain simple (1:1) and complex (m:1 or 1:m) mappings. Our experimental results and comparisons with related work indicate that utilizing this method in dealing with ontology mapping is a promising way to improve the overall mapping results.