Mitra, P., Wiederhold, G. and Jannink, J. (1999). Semi-automatic integration of knowledge sources.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for this purpose. Finally, LSD did not consider in depth the semantics of a mapping, as we do here. We now describe other related work to GLUE from several perspectives. Ontology Matching: Many works have addressed ontology matching in the context of ontology design and integration (e.g. [11, 24, 28, 27]) These works do not deal with explicit notions of similarity. They use a variety of heuristics to match ontology elements. They do not use machine learning and do not exploit information in the data instances. However, many of them [24, 28] have powerful features that allow for e#cient user ....

P. Mitra, G. Wiederhold, and J. Jannink. Semiautomatic Integration of Knowledge Sources. In Proceedings of Fusion'99.

....process. Because the users must be in the loop, only semiautomatic methods can be considered. Numerous such methods have been developed, in the areas of databases, AI, e commerce, and the Semantic Web (e.g. MZ98, PSU98, CA99, LC00, PE95, CHR97, MBR01, MMGR02, MHH00, DR02, Cha00, MFRW00, NM00, MWJ, NM01, RHS01] see [RB01] for an excellent survey of automatic approaches developed by the database community) The proposed approaches have built efficient specialized mapping strategies, and significantly advanced our understanding of representation matching. However, these approaches suffer ....

....section we review and compare these solutions to ours from several perspectives. 86 6.2.1 Rule versus Learner based Approaches Rule based Solutions: The vast majority of current solutions employ hand crafted rules to match representations. Works in this approach include [MZ98, PSU98, CA99, MWJ, MBR01, MMGR02] in databases and [Cha00, MFRW00, NM00, MWJ] in AI. In general, hand crafted rules exploit schema information such as element names, data types, structures, and number of subelements. A broad variety of rules have been considered. For example, the TranScm system [MZ98] employs ....

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proceedings of Fusion'99.

....section we review and compare these solutions to ours from several perspectives. 86 6.2.1 Rule versus Learner based Approaches Rule based Solutions: The vast majority of current solutions employ hand crafted rules to match representations. Works in this approach include [MZ98, PSU98, CA99, MWJ, MBR01, MMGR02] in databases and [Cha00, MFRW00, NM00, MWJ] in AI. In general, hand crafted rules exploit schema information such as element names, data types, structures, and number of subelements. A broad variety of rules have been considered. For example, the TranScm system [MZ98] employs ....

....several perspectives. 86 6.2.1 Rule versus Learner based Approaches Rule based Solutions: The vast majority of current solutions employ hand crafted rules to match representations. Works in this approach include [MZ98, PSU98, CA99, MWJ, MBR01, MMGR02] in databases and [Cha00, MFRW00, NM00, MWJ] in AI. In general, hand crafted rules exploit schema information such as element names, data types, structures, and number of subelements. A broad variety of rules have been considered. For example, the TranScm system [MZ98] employs rules such as two elements match if they have the same name ....

P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proceedings of Fusion'99.

....is used in the models, the implementation of a generic Match operation will rely on auxiliary information such as dictionaries of synonyms, name transformations, analysis of instances, and ultimately a human 3 arbiter. Approaches to perform automatic schema matching have been investigated in [3,5,7,8,9,10,12,13]. By analogy to outer join in relational databases, we use OuterMatch to ensure that all objects of an input model are represented in the match result. For instance, RightOuterMatch (M 1 , M 2 ) creates and returns a mapping map that covers M 2 .Thatis, every object o in M 2 is in the range of ....

Mitra, P., Wiederhold , G., Jannink, J.: Semi-automatic Integration of Knowledge Sources. Proc. of Fusion '99, Sunnyvale, USA, July 1999

....model of these approaches all terms of a domain are arranged in a complex structure. Each information source is related to the terms 6 of the global ontology (e.g. with articulation axioms (Collet et al. 1991) However, the scalability of such a fixed and static common domain model is low (Mitra et al. 1999), because the kind of information sources which can be integrated in the future is limited. In OBSERVER (Mena et al. 1996) and SKC (Mitra et al. 1999) it is assumed, that a predefined ontology for each information source exists. Consequently, new information sources can easily be added and ....

....ontology (e.g. with articulation axioms (Collet et al. 1991) However, the scalability of such a fixed and static common domain model is low (Mitra et al. 1999) because the kind of information sources which can be integrated in the future is limited. In OBSERVER (Mena et al. 1996) and SKC (Mitra et al. 1999) it is assumed, that a predefined ontology for each information source exists. Consequently, new information sources can easily be added and removed. But the comparison of the heterogeneous ontologies leads to many homonym, synonym, etc. problems, because the ontologies use their own vocabulary. ....

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Mitra, P., Wiederhold, G., and Jannink, J. (1999). Semi-automatic integration of knowledge sources. In Fusion '99, Sunnyvale CA.

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proc. of the 2nd Int. Conf. On Information FUSION'99, 1999.

....their parent (or child) nodes. The expert has the final decision whether to accept this educated guess generated by the articulation generator. Due to space limitations, we will not describe in detail all the heuristic algorithms that we use to match ontologies, but refer the interested reader to [14]. In the next section, we will briefly define an Ontology Algebra, which allows us to systematically compose information from diverse information sources. Since we focus on small, well maintained ontologies order to achieve highprecision, but we still want to serve substantial applications, we ....

P. Mitra, G. Wiederhold, and J. Jannink. Semiautomatic integration of knowledge sources. In Proc. of the 2nd Int. Conf. On Information FUSION'99, 1999.

....their parent (or child) nodes. The expert has the final decision whether to bless this educated guess generated by the articulation generator. Due to space limitations, we will not describe in detail all the heuristic algorithms that we use to match ontologies, but refer the interested reader to [18]. A Scalable Framework for the Interoperation of Information Sources In the next section, we will briefly define an Ontology Algebra, which allows us to systematically compose information from diverse information sources. Since we focus on small, well maintained ontologies in order to achieve ....

P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proc. of the 2nd Int. Conf. On Information FUSION'99, 1999.

....Engine The articulation engine is responsible for creating the articulation ontology and the semantic bridges betwen it and the the source ontologies based on the articulation rules. Onion is based on the SKAT (Semantic Knowledge Articulation Tool) system developed in recent years at Stanford [17]. Articulation rules are proposed by SKAT using expert rules and other external knowledge sources or semantic lexicons (e.g. Wordnet) and veri ed by the expert. The inference engine uses the articulation rules generated by SKAT and the rules from the individual source ontologies to derive more ....

P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proc. of the 2nd Int. Conf. On Information FUSION'99, 1999.

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Mitra, P., Wiederhold, G. and Jannink, J. (1999). Semi-automatic integration of knowledge sources.

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proc. of the 2nd Int. Conf. On Information FUSION'99, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proc. of Fusion-1999.

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Mitra, P., Wiederhold, G. and Jannink, J. Semi-automatic Integration of Knowledge Sources. in Proceedings of The 2nd International Conference on Information Fusion, Sunnyvale, CA, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. "Semi-automatic Integration of Knowledge sources". In Proc. of the 2nd Int. Conf. On Information FUSION, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. "Semi-automatic Integration of Knowledge sources". In Proc. of the 2nd Int. Conf. On Information FUSION, 1999.

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P. Mitra, G. Wiederhold and J. Jannink. Semi-automatic Integration of Knowledge Sources. Fusion, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. Semiautomatic Integration of Knowledge Sources. In Proceedings of Fusion'99, 1999. AnHai Doan et al.

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic integration of knowledge sources. In Proc. of Fusion-1999.

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P. Mitra, G. Wiederhold, and J. Jannink. "Semi-automatic Integration of Knowledge sources". In Proc. of the 2nd Int. Conf. On Information FUSION, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. "Semi-automatic Integration of Knowledge sources". In Proc. of the 2nd Int. Conf. On Information FUSION, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. Semiautomatic Integration of Knowledge Sources. In Proceedings of Fusion'99, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic Integration of Knowledge Sources. In Proc. the 2nd Int. Conf. on Information FUSION, 1999.

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P. Mitra, G. Wiederhold, and J. Jannink. Semi-automatic Integration of Knowledge Sources. In Proceedings of Fusion'99.

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