Noy, N., Musen, M.: Prompt: Algorithm and tool for automated ontology merging and alignment. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), Austin, Texas (2000) 450--455 Home/Search Document Details and Download
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Ontology Translation by Ontology Merging and Automated.. - Dou, McDermott, Qi (Correct)
....is obtained by taking the union of the terms and the axioms de ning them. We then add bridging axioms that relate the terms in one ontology to the terms in the other through the terms in the merge. It is some di erent with what some other people talk about ontology merging, such as PROMPT [15] and Chimaera [16] These works would like to treat ontology merging as some kind of ontology editing for two similar ontologies. They try to do ontology merging semi automatically but they only provide user some suggestions for merging similar terms usually because the name similarity. The nal ....
N. F. Noy and M. A. Musen. PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. Proceedings of the Seventeenth National Conference on Arti cial Intelligence (AAAI-2000), Austin, TX.
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....structures, and ii) information context. A user may then exploit these three types of information in order to create mappings into his own information structures (e.g. his ontology) which may be a lot easier than if the information a user gets is restricted to information structures [16] and or information proper only [4] We define deep annotation as an annotation process that utilizes information proper, information structures and information context in order to derive mappings between information structures. The mappings may then be exploited by the same or another user in ....
N. F. Noy and M. A. Musen. PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In Proc. of AAAI-2000, pages 450--455, 2000.
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....Thus, the semantic correspondences are in e#ect the glue that hold the ontologies together into a web of semantics . Without them, the Semantic Web is akin to an electronic version of the Tower of Babel. Unfortunately, manually specifying such correspondences is time consuming, error prone [28], and clearly not possible on the Web scale. Hence, the development of tools to assist in ontology mapping is crucial to the success of the Semantic Web [35] In this paper we describe the GLUE system, which applies machine learning techniques to semi automatically create such semantic mappings. ....
....cannot be matched automatically because they are simply ambiguous. For example, it is not clear whether networking and communication devices should match communication equipment or computer networks . A solution to this problem is to incorporate user interaction into the matching process [28, 12, 38]. GLUE currently tries to predict the best match for every node in the taxonomy. However, in some cases, such a match simply does not exist (e.g. unlike Cornell, the University of Washington does not have a School of Hotel Administration) Hence, an additional extension to GLUE is to make it be ....
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N. Noy and M. Musen. PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In Proceedings of the National Conference on Artificial Intelligence (AAAI), 2000.
Towards Semantic Web Mining - Berendt, Hotho, Stumme (2002) (10 citations) (Correct)
....based on the given source ontologies. Manual ontology merging using conventional editing tools without support is difficult, labor intensive and error prone. Therefore, several systems and frameworks for supporting the knowledge engineer in the ontology merging task have recently been proposed [24, 6, 36, 33]. The approaches rely on syntactic and semantic matching heuristics which are derived from the behavior of ontology engineers when confronted with the task of merging ontologies, i.e. human behavior is simulated. Another method is FCA MERGE which merges ontologies following a bottom up approach, ....
N. Noy and M. Musen. Prompt: Algorithm and tool for automated ontology merging and alignment. In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000.
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....in contrast to conventional annotation, which remains at the presentation layer. Emergent Semantics [18] builds on lightweight (e.g. a file structure with files as instances) and or heavyweight ontologies that di#erent participants have created. Also, the idea of merging ontologies is not new. In [19] a semi automatic system called PROMPT is presented, which makes propositions the user has to confirm or defer. 20] shows a general approach for it. Matching and and especially similarity are treated in [5] covering di#erent dimensions of similarity in ontologies. 21] have another nice ....
Noy, N.F., Musen, M.A.: Prompt: Algorithm and tool for automated ontology merging and alignment. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), Austin, TX, USA (2000)
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