Knowledge Integration to Overcome Ontological Heterogeneity: Challenges from Financial Information Systems

Firat, Aykut; Madnick, Stuart E.; Grosof, Benjamin N.

doi:10.2139/ssrn.336205

Cited by 14 publications

(13 citation statements)

References 16 publications

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“…Integration can be based on extensions or concepts, and is aimed at indemnifying inconsistencies and mismatches in the concepts. For example, the COIN technique [77] addresses data-level heterogeneities among data sources expressed in terms of context axioms and provides a comprehensive approach to knowledge integration. An extension of COIN is ECOIN, which improves upon COIN through its ability to handle both data-level and ontological heterogeneities in a single framework [77].…”

Section: Knowledge Integrationmentioning

confidence: 99%

“…For example, the COIN technique [77] addresses data-level heterogeneities among data sources expressed in terms of context axioms and provides a comprehensive approach to knowledge integration. An extension of COIN is ECOIN, which improves upon COIN through its ability to handle both data-level and ontological heterogeneities in a single framework [77]. Knowledge integration is highly useful in medicine, to integrate concepts and information within various medical data sources.…”

Section: Knowledge Integrationmentioning

confidence: 99%

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Literature Review of Attribute Level and Structure Level Data Linkage Techniques

Gollapalli¹

2015

IJDKP

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Section: Knowledge Integrationmentioning

confidence: 99%

Section: Knowledge Integrationmentioning

confidence: 99%

Literature Review of Attribute Level and Structure Level Data Linkage Techniques

Gollapalli¹

2015

IJDKP

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“…The Extended COntext INterchange (ECOIN) system is an extension of the core COIN system, aimed to resolve equational ontological conflicts, which is defined as "the heterogeneity in the way data items are calculated from other data items in terms of definitional equations" [5,6]. In ECOIN, modifiers are used to specify the definitional differences, and new constraints for basic mathematical operations such as addition, subtraction and multiplication are added.…”

Section: Overview Of the Context Interchange Technologymentioning

confidence: 99%

“…Part of the desired relation ("structure" 5 ) is shown in The columns in the above table are self-explanatory. For example, IBM Far East Holdings B. V. is a wholly owned subsidiary of IBM, and International Information Products is 80% owned by IBM Far East Holdings.…”

Section: Datamentioning

confidence: 99%

The Design and Implementation of a Corporate Householding Knowledge Processor to Improve Data Quality

2003

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Advances in Corporate Householding are needed to address certain categories of data quality problems caused by data misinterpretation. In this paper, we first summarize some of these data quality problems and our more recent results from studying corporate householding applications and knowledge exploration. Then we outline a technical approach to a Corporate Householding Knowledge Processor (CHKP) to solve a particularly important type of corporate householding problem -entity aggregation. We illustrate the operation of the CHKP by using a motivational example in account consolidation. Our CHKP design and implementation uses and expands on the COntext INterchange (COIN) technology to manage and process corporate householding knowledge.

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“…Therefore, identifying interoperability issues and concerns first at an ontological level will enable more productive work. In their example about the concept o f profit in financial institutions, Firat, M adnick, and Grosof, (Firat et al 2002) shows how the same concepts are used differently depending on the context and how concept am biguity becomes the source o f multiple data corruption and application misalignments.…”

Section: Gomezmentioning

confidence: 99%

Evaluating data interoperability : a policy-driven ontological analysis model

Garigue¹

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This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality o f the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps.

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Knowledge Integration to Overcome Ontological Heterogeneity: Challenges from Financial Information Systems

Abstract: Abstract

Cited by 14 publications

References 16 publications

Literature Review of Attribute Level and Structure Level Data Linkage Techniques

Literature Review of Attribute Level and Structure Level Data Linkage Techniques

The Design and Implementation of a Corporate Householding Knowledge Processor to Improve Data Quality

Evaluating data interoperability : a policy-driven ontological analysis model

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