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Supporting Data Integration Tasks with Semi-Automatic Ontology Construction

Published: 22 October 2015 Publication History

Abstract

Data integration aims to facilitate the exploitation of heterogeneous data by providing the user with a unified view of data residing in different sources. Currently, ontologies are commonly used to represent this unified view in terms of a global target schema due to their flexibility and expressiveness. However, most approaches still assume a predefined target schema and focus on generating the mappings between this schema and the sources.
In this paper, we propose a solution that supports data integration tasks by employing semi-automatic ontology construction to create a target schema on the fly. To that end, we revisit existing ontology extraction, matching and merging techniques and integrate them into a single end-to-end system. Moreover, we extend the used techniques with the automatic generation of mappings between the extracted ontologies and the underlying data sources. Finally, to demonstrate the usefulness of our solution, we integrate it with an independent data integration system.

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cover image ACM Conferences
DOLAP '15: Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP
October 2015
108 pages
ISBN:9781450337854
DOI:10.1145/2811222
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 October 2015

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Author Tags

  1. data integration
  2. ontology extraction
  3. ontology matching

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DOLAP '15 Paper Acceptance Rate 8 of 31 submissions, 26%;
Overall Acceptance Rate 29 of 79 submissions, 37%

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  • (2020)Quarry: A User-centered Big Data Integration PlatformInformation Systems Frontiers10.1007/s10796-020-10001-yOnline publication date: 18-Apr-2020
  • (2019)Data discovery method for Extract- Transform-Load2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT)10.1109/ICMIMT.2019.8712027(205-212)Online publication date: Feb-2019
  • (2019)An Ontology and Multi-Agent Based Decision Support Framework for Prefabricated Component Supply ChainInformation Systems Frontiers10.1007/s10796-019-09941-xOnline publication date: 19-Jul-2019
  • (2019)Validation and Evaluation of the Mapping Process for Generating Ontologies from Relational DatabasesNew Knowledge in Information Systems and Technologies10.1007/978-3-030-16181-1_32(337-350)Online publication date: 27-Mar-2019
  • (2018)Modular Ontologies CompositionInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201810010313:4(35-60)Online publication date: 1-Oct-2018
  • (2018)Towards Automated Data Integration in Software AnalyticsProceedings of the International Workshop on Real-Time Business Intelligence and Analytics10.1145/3242153.3242159(1-5)Online publication date: 27-Aug-2018
  • (2018)Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA.2018.8612844(1-6)Online publication date: Oct-2018
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  • (2017)Mapping Relational Databases to Ontology RepresentationProceedings of the 1st International Conference on Digital Technology in Education10.1145/3134847.3134852(54-58)Online publication date: 6-Aug-2017
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