In:
ACM SIGAPP Applied Computing Review, Association for Computing Machinery (ACM), Vol. 11, No. 2 ( 2011-03), p. 17-29
Abstract:
An automated ontology matching methodology is presented, supported by various machine learning techniques, as implemented in the system MoTo. The methodology is two-tiered. On the first stage it uses a meta-learner to elicit certain mappings from those predicted by single matchers induced by a specific base-learner. Then, uncertain mappings are recovered passing through a validation process, followed by the aggregation of the individual predictions through linguistic quantifiers. Experiments on benchmark ontologies demonstrate the effectiveness of the methodology.
Type of Medium:
Online Resource
ISSN:
1559-6915
,
1931-0161
DOI:
10.1145/1964144.1964148
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2011
detail.hit.zdb_id:
2088099-6
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