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Information integration using contextual knowledge and ontology merging

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dc.contributor.advisor Stuart E. Madnick. en_US
dc.contributor.author Firat, Aykut en_US
dc.contributor.other Sloan School of Management. en_US
dc.date.accessioned 2006-03-24T18:11:51Z
dc.date.available 2006-03-24T18:11:51Z
dc.date.copyright 2003 en_US
dc.date.issued 2003 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/30013
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2003. en_US
dc.description Includes bibliographical references (p. 145-152). en_US
dc.description.abstract With the advances in telecommunications, and the introduction of the Internet, information systems achieved physical connectivity, but have yet to establish logical connectivity. Lack of logical connectivity is often inviting disaster as in the case of Mars Orbiter, which was lost because one team used metric units, the other English while exchanging a critical maneuver data. In this Thesis, we focus on the two intertwined sub problems of logical connectivity, namely data extraction and data interpretation in the domain of heterogeneous information systems. The first challenge, data extraction, is about making it possible to easily exchange data among semi-structured and structured information systems. We describe the design and implementation of a general purpose, regular expression based Cameleon wrapper engine with an integrated capabilities-aware planner/optimizer/executioner. The second challenge, data interpretation, deals with the existence of heterogeneous contexts, whereby each source of information and potential receiver of that information may operate with a different context, leading to large-scale semantic heterogeneity. We extend the existing formalization of the COIN framework with new logical formalisms and features to handle larger set of heterogeneities between data sources. This extension, named Extended Context Interchange (ECOIN), is motivated by our analysis of financial information systems that indicates that there are three fundamental types of heterogeneities in data sources: contextual, ontological, and temporal. While COIN framework was able to deal with the contextual heterogeneities, ECOIN framework expands the scope to include ontological heterogeneities as well. en_US
dc.description.abstract (cont.) In particular, we are able to deal with equational ontological conflicts (EOC), which refer to the heterogeneity in the way data items are calculated from other data items in terms of definitional equations. ECOIN provides a context-based solution to the EOC problem based on a novel approach that integrates abductive reasoning and symbolic equation solving techniques in a unified framework. Furthermore, we address the merging of independently built ECOIN applications, which involves merging disparate ontologies and contextual knowledge. The relationship between ECOIN and the Semantic Web is also discussed. Finally, we demonstrate the feasibility and features of our integration approach with a prototype implementation that provides mediated access to heterogeneous information systems. en_US
dc.description.provenance Made available in DSpace on 2006-03-24T18:11:51Z (GMT). No. of bitstreams: 2 55023325.pdf: 9268088 bytes, checksum: 8ab925e2ec8b0454abb9cd530f29be88 (MD5) 55023325-MIT.pdf: 9267892 bytes, checksum: 5a18e378a27b18246e3ac63547b814aa (MD5) Previous issue date: 2003 en
dc.description.statementofresponsibility by Aykut Firat. en_US
dc.format.extent 152 p. en_US
dc.format.extent 9268088 bytes
dc.format.extent 9267892 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Sloan School of Management. en_US
dc.title Information integration using contextual knowledge and ontology merging en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Sloan School of Management. en_US
dc.identifier.oclc 55023325 en_US

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