article

Introduction to the special issue on semantic integration

Authors:

Natalya F. Noy,

Alon Y. HalevyAuthors Info & Claims

ACM SIGMOD Record, Volume 33, Issue 4

Pages 11 - 13

https://doi.org/10.1145/1041410.1041412

Published: 01 December 2004 Publication History

Abstract

Semantic heterogeneity is one of the key challenges in integrating and sharing data across disparate sources, data exchange and migration, data warehousing, model management, the Semantic Web and peer-to-peer databases. Semantic heterogeneity can arise at the schema level and at the data level. At the schema level, sources can differ in relations, attribute and tag names, data normalization, levels of detail, and the coverage of a particular domain. The problem of reconciling schema-level heterogeneity is often referred to as schema matching or schema mapping. At the data level, we find different representations of the same real-world entities (e.g., people, companies, publications, etc.). Reconciling data-level heterogeneity is referred to as data deduplication, record linkage, and entity/object matching. To exacerbate the heterogeneity challenges, schema elements of one source can be represented as data in another. This special issue presents a set of articles that describe recent work on semantic heterogeneity at the schema level.

References

[1]

C. Batini, M. Lenzerini, and S. Navathe. A comparative analysis of methodologies for database schema integration. ACM Computing Survey, 18(4):323--364, 1986.]]

Digital Library

[2]

J. Berlin and A. Motro. Database schema matching using machine learning with feature selection. In Proceedings of the Conf. on Advanced Information Systems Engineering (CAiSE), 2002.]]

Digital Library

[3]

P. Bernstein. Applying model management to classical meta data problems. In Proceedings of the Conf. on Innovative Database Research (CIDR), 2003.]]

[4]

C. Clifton, E. Housman, and A. Rosenthal. Experience with a combined approach to attribute-matching across heterogeneous databases. In Proc. of the IFIP Working Conference on Data Semantics (DS-7), 1997.]]

[5]

H. Do and E. Rahm. Coma: A system for flexible combination of schema matching approaches. In Proceedings of the 28th Conf. on Very Large Databases (VLDB), 2002.]]

Digital Library

[6]

A. Doan, P. Domingos, and A. Halevy. Reconciling schemas of disparate data sources: A machine learning approach. In Proceedings of the ACM SIGMOD Conference, 2001.]]

Digital Library

[7]

A. Doan and A. Halevy. Semantic integration research in the database community: A brief survey. AI Magazine, Special Issue on Semantic Integration. To appear. Available at http://anhai.cs.uiuc.edu/home, 2005.]]

Digital Library

[8]

A. Doan, A. Y. Halevy, and N. F. Noy. Semantic integration workshop at the 2nd int. semantic web conf. (iswc-2003). SIGMOD Record, 33(1), 2004.]]

[9]

D. Embley, D. Jackman, and L. Xu. Multifaceted exploitation of metadata for attribute match discovery in information integration. In Proc. of the WIIW-01, 2001.]]

[10]

A. Halevy. Answering queries using views: A survey. The VLDB Journal, 10(4):270--294, 2001.]]

Digital Library

[11]

B. He and K. Chang. Statistical schema matching across web query interfaces. In Proc. of the ACM SIGMOD Conf. (SIGMOD), 2003.]]

Digital Library

[12]

J. Kang and J. Naughton. On schema matching with opaque column names and data values. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data (SIGMOD-03), 2003.]]

Digital Library

[13]

M. Lenzerini. Data integration; a theoretical perspective. In Proc. of PODS-02, 2002.]]

Digital Library

[14]

J. Madhavan, P. Bernstein, A. Doan, and A. Halevy. Corpus-based schema matching. In Proc. of the 18th IEEE Int. Conf. on Data Engineering (ICDE), 2005.]]

Digital Library

[15]

R. McCann, A. Doan, A. Kramnik, and V. Varadarajan. Building data integration systems via mass collaboration. In Proc. of the SIGMOD-03 Workshop on the Web and Databases (WebDB-03), 2003.]]

[16]

A. Ouksel and A. P. Seth. Special issue on semantic interoperability in global information systems. SIGMOD Record, 28(1), 1999.]]

Digital Library

[17]

E. Rahm and P. Bernstein. On matching schemas automatically. VLDB Journal, 10(4), 2001.]]

Digital Library

[18]

W. Wu, C. Yu, A. Doan, and W. Meng. An interactive clustering-based approach to integrating source query interfaces on the Deep Web. In Proc. of the ACM SIGMOD Conf., 2004.]]

Digital Library

[19]

L. Yan, R. Miller, L. Haas, and R. Fagin. Data driven understanding and refinement of schema mappings. In Proceedings of the ACM SIGMOD, 2001.]]

Digital Library

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Buranarach MBuranasing WRungcharoensuksri SSarawasee PNgootip TChansanam W(2022)Metadata Integration Framework for Data Integration of Socio-Cultural Anthropology Digital Repositories: A Case Study of Princess Maha Chakri Sirindhorn Anthropology CentreInformatics10.3390/informatics90200389:2(38)Online publication date: 27-Apr-2022
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Selby PAbbeloos RBacklund JBasterrechea Salido MBauchet GBenites-Alfaro OBirkett CCalaminos VCarceller PCornut GVasques Costa BEdwards JFinkers RYanxin Gao SGhaffar MGlaser PGuignon VHok PKilian AKönig PLagare JLange MLaporte MLarmande PLeBauer DLyon DMarshall DMatthews DMilne IMistry NMorales NMueller LNeveu PPapoutsoglou EPearce BPerez-Masias IPommier CRamírez-González RRathore ARaquel ARaubach SRife TRobbins KRouard MSarma CScholz USempéré GShaw PSimon RSoldevilla NStephen GSun QTovar CUszynski GVerouden M(2019)BrAPI—an application programming interface for plant breeding applicationsBioinformatics10.1093/bioinformatics/btz19035:20(4147-4155)Online publication date: 23-Mar-2019
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Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 33, Issue 4

December 2004

92 pages

ISSN:0163-5808

DOI:10.1145/1041410

Issue’s Table of Contents

Copyright © 2004 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2004

Published in SIGMOD Volume 33, Issue 4

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Cited By

Buranarach MBuranasing WRungcharoensuksri SSarawasee PNgootip TChansanam W(2022)Metadata Integration Framework for Data Integration of Socio-Cultural Anthropology Digital Repositories: A Case Study of Princess Maha Chakri Sirindhorn Anthropology CentreInformatics10.3390/informatics90200389:2(38)Online publication date: 27-Apr-2022
https://doi.org/10.3390/informatics9020038
Carlson ATornow CPage EBrown McFadden APalmer Zimmerman T(2022)Development of the Purchase to Plate Crosswalk and Price Tool: Estimating prices for the National Health And Nutrition Examination Survey (NHANES) foods and measuring the healthfulness of retail food purchasesJournal of Food Composition and Analysis10.1016/j.jfca.2021.104344106(104344)Online publication date: Mar-2022
https://doi.org/10.1016/j.jfca.2021.104344
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https://doi.org/10.1093/bioinformatics/btz190
Hacherouf MNait-Bahloul SCruz C(2019)Transforming XML schemas into OWL ontologies using formal concept analysisSoftware and Systems Modeling (SoSyM)10.1007/s10270-017-0651-418:3(2093-2110)Online publication date: 1-Jun-2019
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Bilal MOyedele LMunir KAjayi SAkinade OOwolabi HAlaka H(2017)The application of web of data technologies in building materials information modelling for construction waste analyticsSustainable Materials and Technologies10.1016/j.susmat.2016.12.00411(28-37)Online publication date: Apr-2017
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Seedah DChoubassi CLeite F(2016)Ontology for Querying Heterogeneous Data Sources in Freight TransportationJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000054830:4Online publication date: Jul-2016
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