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
The Chimaera Ontology Environment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Obtaining the consensus and inconsistency among a set of assertions on a qualitative attribute
Expert Systems with Applications: An International Journal
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Towards automatic merging of domain ontologies: The HCONE-merge approach
Web Semantics: Science, Services and Agents on the World Wide Web
On the identification of temporal clauses
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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A person adds new knowledge to his/her mind, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions, and inconsistencies between what he/she knows and new knowledge that he/she acquires. This way, he/she incrementally acquires information keeping it at all times consistent. This information can be represented by Ontologies. In contrast to human approach, algorithms of Ontologies fusion lack these features, merely being computer-aided editors where a person solves the details and inconsistencies. This article presents a method for Ontology Merging (OM), its algorithm and implementation to fuse or join two ontologies (obtained from Web documents) in an automatic fashion (without human intervention), producing a third ontology, and taking into account the inconsistencies, contradictions, and redundancies between both ontologies, thus delivering a result close to reality. The repeated use of OM allows acquisition of much information about the same topic.