poster

Semantic queries in databases: problems and challenges

Authors:

Min WangAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1505 - 1508

https://doi.org/10.1145/1645953.1646157

Published: 02 November 2009 Publication History

Abstract

Supporting semantic queries in relational databases is essential to many advanced applications. Recently, with the increasing use of ontology in various applications, the need for querying relational data together with its related ontology has become more urgent. In this paper, we identify and discuss the problem of querying relational data with its ontologies. Two fundamental challenges make the problem interesting. First, it is extremely difficult to express queries against graph structured ontology in the relational query language SQL, and second, in many cases where data and its related ontology are complicated, queries are usually not precise, that is, users often have only a vague notion, rather than a clear understanding and definition, of what they query for. We outline a query-by-example approach that enables us to support semantic queries in relational databases with ease. Instead of endeavoring to incorporate ontology into relational form and create new language constructs to express such queries, we ask the user to provide a small number of examples that satisfy the query she has in mind. Using these examples as seeds, the system infers the exact query automatically, and the user is therefore shielded from the complexity of interfacing with the ontology.

References

[1]

A. Balter. Mastering Microsoft Office Access 2007 Development. Sams, 2007.

Digital Library

[2]

M. Belkhatir, P. Mulhem, and Y. Chiaramella. A conceptual image retrieval architecture combining keyword-based querying with transparent and penetrable query-by-example. In ACM International Conference on Image and Video Retrieval (CIVR), pages 528--539, 2005.

Digital Library

[3]

G. Boccignone, A. Chianese, V. Moscato, and A. Picariello. Animate system for query by example in image databases. In EuroIMSA, pages 451--456, 2005.

[4]

A. K. Choupo, L. Berti-Equille, and A. Morin. Optimizing progressive query-by-example over pre-clustered large image databases. In V. Benzaken, editor, BDA, 2005.

[5]

ASTM E2369-05 Standard Specification for Continuity of Care Record (CCR). http://www.astm.org.

[6]

S. Das, E. I. Chong, G. Eadon, and J. Srinivasan. Supporting ontology-based semantic matching in RDBMS. In Proc. of Very Large Database (VLDB), pages 1054--1065, 2004.

Digital Library

[7]

Health level seven. http://www.hl7.org.

[8]

International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). http://www.cdc.gov/nchs/about/otheract/icd9/abticd9.htm.

[9]

Systematized Nomenclature of Medicine-Clinical Terms. http://www.ihtsdo.org/.

[10]

R. Krishnamurthy, S. P. Morgan, and M. M. Zloof. Query-by-example: Operations on piecewise continuous data(extended abstract). In Proc. of Very Large Database (VLDB), pages 305--308, 1983.

Digital Library

[11]

L. Lim, H. Wang, and M. Wang. Semantic data management: Towards querying data with their meaning. In Int. Conf. Data Engineering (ICDE), pages 1438--1442, 2007.

[12]

L. Lim, H. Wang, and M. Wang. Unifying data and domain knowledge using virtual views. In Proc. of Very Large Database (VLDB), pages 255--266, 2007.

Digital Library

[13]

L. Ma, Z. Su, Y. Pan, L. Zhang, and T. Liu. RStar: An RDF storage and query system for enterprise resource management. In Intl' Conf. on Information and Knowledge Management (CIKM), 2004.

Digital Library

[14]

National cancer institute thesaurus. http://www.nci.nih.gov/cancerinfo/terminologyresources.

[15]

OntoBroker. http://ontobroker.aifb.uni-karlsruhe.de/index_ob.html.

[16]

OTK tool repository: Ontoedit. http://www.ontoknowledge.org/tools/ontoedit.shtml.

[17]

N. Rasiwasia, N. Vasconcelos, and P. J. Moreno. Query by semantic example. In ACM International Conference on Image and Video Retrieval (CIVR), pages 51--60, 2006.

Digital Library

[18]

SWAD-Europe Deliverable 10.2: Mapping Semantic Web Data with RDBMSes. http://www.w3.org/2001/sw/Europe/reports/scalable_rdbms_mapping_report/.

[19]

The KArlsruhe ONtology and semantic web tool suite. http://kaon.semanticweb.org/.

[20]

The protégé ontology editor and knowledge acquisition system. http://protege.stanford.edu/.

[21]

M. M. Zloof. Query-by-example: the invocation and definition of tables and forms. In D. S. Kerr, editor, Proc. of Very Large Database (VLDB), pages 1--24. ACM, 1975.

Digital Library

[22]

M. M. Zloof. Query-by-example: A data base language. IBM Systems Journal, 16(4):324--343, 1977.

Digital Library

Cited By

Madkour AAref WPrabhakar SAli MBykau S(2018)TrueWebProceedings of the International Workshop on Semantic Big Data10.1145/3208352.3208357(1-6)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3208352.3208357
Cerny TTrnka MDonahoo M(2016)Towards Shared Security through Distributed Separation of ConcernsProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/2987386.2987394(169-172)Online publication date: 11-Oct-2016
https://dl.acm.org/doi/10.1145/2987386.2987394
Beheshti SBenatallah BSakr SGrigori DMotahari-Nezhad HBarukh MGater ARyu SBeheshti SBenatallah BSakr SGrigori DMotahari-Nezhad HBarukh MGater ARyu S(2016)Tools, Use Cases, and DiscussionsProcess Analytics10.1007/978-3-319-25037-3_6(135-150)Online publication date: 29-Mar-2016
https://doi.org/10.1007/978-3-319-25037-3_6
Show More Cited By

Index Terms

Semantic queries in databases: problems and challenges
1. Human-centered computing
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

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Published: 02 November 2009

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November 2 - 6, 2009

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Cited By

Madkour AAref WPrabhakar SAli MBykau S(2018)TrueWebProceedings of the International Workshop on Semantic Big Data10.1145/3208352.3208357(1-6)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3208352.3208357
Cerny TTrnka MDonahoo M(2016)Towards Shared Security through Distributed Separation of ConcernsProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/2987386.2987394(169-172)Online publication date: 11-Oct-2016
https://dl.acm.org/doi/10.1145/2987386.2987394
Beheshti SBenatallah BSakr SGrigori DMotahari-Nezhad HBarukh MGater ARyu SBeheshti SBenatallah BSakr SGrigori DMotahari-Nezhad HBarukh MGater ARyu S(2016)Tools, Use Cases, and DiscussionsProcess Analytics10.1007/978-3-319-25037-3_6(135-150)Online publication date: 29-Mar-2016
https://doi.org/10.1007/978-3-319-25037-3_6
Madkour AAref WMokbel MBasalamah S(2015)Geo-tagging non-spatial conceptsProceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems10.1145/2834126.2834138(31-39)Online publication date: 3-Nov-2015
https://dl.acm.org/doi/10.1145/2834126.2834138
Schlesinger PRahman N(2015)Self-Service Business Intelligence Resulting in Disruptive TechnologyJournal of Computer Information Systems10.1080/08874417.2015.1164579656:1(11-21)Online publication date: 7-Dec-2015
https://doi.org/10.1080/08874417.2015.11645796
Lim LWang HWang MGuerrini GPaton N(2013)Semantic queries by exampleProceedings of the 16th International Conference on Extending Database Technology10.1145/2452376.2452417(347-358)Online publication date: 18-Mar-2013
https://dl.acm.org/doi/10.1145/2452376.2452417
Hoang DTjoa APriebe T(2012)Query-by-example approach towards on-demand multidimensional analysisInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2012.0495517:3(137-151)Online publication date: 1-Oct-2012
https://dl.acm.org/doi/10.1504/IJBIDM.2012.049551
Aggarwal CWang H(2010)Graph Data Management and Mining: A Survey of Algorithms and ApplicationsManaging and Mining Graph Data10.1007/978-1-4419-6045-0_2(13-68)Online publication date: 18-Jan-2010
https://doi.org/10.1007/978-1-4419-6045-0_2

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