Determining relationships among attributes for interoperability of multi-database systems

Yu, Clement; Sun, Wei; Dao, Son; Keirsey, D.

doi:10.1109/ims.1991.153717

Cited by 42 publications

(14 citation statements)

References 11 publications

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“…5 , or similar entities such as the common concept approach. 6 To summarize the thrust of this paper: ontologies can help drive verification and validation by; ⅷ providing an explicit and coherent conceptualization of the domain; ⅷ allowing the expert to inspect the distinctions made in the ontology rather than being forced to make judgement calls on the rules, making the role of the expert better defined and less subjective; ⅷ providing a means of structuring testing; ⅷ suggesting appropriate responses to flaws indicated by testing. This paper is a revised and extended version of a paper presented at a workshop on verification, validation, and integrity issues in expert and database systems, a track in the Ninth International Workshop on Database and Expert System Applications, Vienna 1998.…”

Section: Discussionmentioning

confidence: 99%

The role of ontologies in the verification and validation of knowledge-based systems

Bench‐Capon

2001

Int. J. Intell. Syst.

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In this paper, the author gives some preliminary examination of the ways in which an ontologyᎏan explicit specification of the conceptualization of the domainᎏcan support the verification and validation of a knowledge-based system. The discussion is focused on a simple, well-known, example relating to the identification of animals. Key elements of the support provided by the ontology relate to attempting to give coherence to the domain conceptualization; making the role of experts in verification and validation more structured and less at the mercy of interpretation; constraining the number of test cases required to give good coverage of the possible cases; and structuring the testing to give better assurance of its efficacy, and a possible basis for greater automation of the testing process. Finally, the author makes some concluding remarks.

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Section: Discussionmentioning

confidence: 99%

The role of ontologies in the verification and validation of knowledge-based systems

Bench‐Capon

2001

Int. J. Intell. Syst.

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“…Clement Yu et al [44] argued that semantics of majority of attributes can be captured by consulting pre-established concepts that are analogous to standardized keyword index hierarchies. Then, the aggregation among attributes can be determined.…”

Section: Comparing Attribute Namesmentioning

confidence: 99%

SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

Li¹,

Clifton

2000

Data & Knowledge Engineering

267

173

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One step in interoperating among heterogeneous databases is semantic integration: Identifying relationships between attributes or classes in dierent database schemas. SEMantic INTegrator (SEMINT) is a tool based on neural networks to assist in identifying attribute correspondences in heterogeneous databases. SEMINT supports access to a variety of database systems and utilizes both schema information and data contents to produce rules for matching corresponding attributes automatically. This paper provides theoretical background and implementation details of SEMINT. Experimental results from large and complex real databases are presented. We discuss the eectiveness of SEMINT and our experiences with attribute correspondence identi®cation in various environments. Ó

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“…Reference [11] describes an approach to finding similar classes based on reasoning about fuzzy terminological relationships between names as defined in a terminological knowledge base. Other progress in the ontology direction can be seen in Reference [40], where it is proposed to replace the assertion-based characterization of attribute semantics used in previously described work with a characterization in terms of a "concept hierarchy." With respect to automation, Reference [18] repeats the assertion that "automatic conflict resolution is in general infeasible" and Reference [16] makes a related assertion to the effect that detecting semantic relationships among different databases cannot be completely automated, whereas Reference [40] proposes to semi-automate the identification of concepts using natural language processing of a supplied data dictionary.…”

Section: Schema Integrationmentioning

confidence: 99%

Concepts for automating systems integration

Barkmeyer¹,

Feeney²,

Denno³

et al. 2003

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Determining relationships among attributes for interoperability of multi-database systems

Cited by 42 publications

References 11 publications

The role of ontologies in the verification and validation of knowledge-based systems

The role of ontologies in the verification and validation of knowledge-based systems

SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

Concepts for automating systems integration

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