research-article

Structure inference for linked data sources using clustering

Authors:

Klitos Christodoulou,

Norman W. Paton, and

Alvaro A. A. FernandesAuthors Info & Claims

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

March 2013

Pages 60 - 67

https://doi.org/10.1145/2457317.2457328

Published: 18 March 2013 Publication History

Abstract

Linked Data (LD) is supplementing the World Wide Web of documents with a Web of data. This is becoming apparent from the number of LD repositories available as part of the Linked Open Data (LOD) cloud. At the instance-level, LD sources use a combination of terms from various vocabularies, expressed as RDFS/OWL, to describe their data and publish them to the Web. However, LD sources do not organise their data under a specific structure analogous to a relational schema; instead data can adhere to multiple vocabularies. Expressing SPARQL queries over LD sources -- usually over a SPARQL endpoint that is presented to the user -- requires a knowledge of the predicates used, to allow queries to express user requirements as graph patterns. Although LD provides low barriers to data publication using a homogeneous language (i.e., RDF), sources organise their data with different structures and terminologies. We would like to have a synopsis of how such data are organised in LD sources to inform the expressing of queries over such sources. With this paper we make the case that structural summaries over LD sources can inform query formulation and provide support for data integration and query processing over multiple LD sources. To fulfil this aim we propose an approach, that builds on a hierarchical clustering algorithm, for inferring structural summaries over LD sources. We have conducted an experimental evaluation using various LD sources to ascertain the extent to which our technique can successfully infer structural summaries from LD sources.

References

[1]

M. Arenas, C. Gutierrez, and J. Pérez. Foundations of rdf databases. In Reasoning Web, pages 158--204, 2009.

Digital Library

[2]

C. Bizer and R. Cyganiak. D2r server, publishing relational databases on the semantic web. Poster at the 5th International Semantic Web Conference (ISWC), 2006.

[3]

C. Bizer, T. Heath, and Tl Berners-Lee. Linked data - the story so far. Int. J. Semantic Web Inf. Syst., 5(3):1--22, 2009.

[4]

M. J. Franklin, A. Y. Halevy, and D. Maier. From databases to dataspaces: a new abstraction for information management. SIGMOD Record, 34(4):27--33, 2005.

Digital Library

[5]

R. Goldman and J. Widom. Dataguides: Enabling query formulation and optimization in semistructured databases. In Proceedings of the 23rd International Conference on Very Large Data Bases, VLDB '97, pages 436--445, San Francisco, CA, USA, 1997. Morgan Kaufmann Publishers Inc.

Digital Library

[6]

M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. Journal of Intelligent Information Systems, 17(2):107--145, 2001.

Digital Library

[7]

A. Harth, K. Hose, M. Karnstedt, A. Polleres, K. Sattler, and J. Umbrich. Data summaries for on-demand queries over linked data. In WWW, pages 411--420, 2010.

Digital Library

[8]

T. Heath and C. Bizer. Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web. Morgan & Claypool Publishers, 2011.

Digital Library

[9]

A. Hogan, A. Harth, J. Umbrich, S. Kinsella, A. Polleres, and S. Decker. Searching and browsing linked data with swse: The semantic web search engine. J. Web Sem., 9(4):365--401, 2011.

Digital Library

[10]

L. Kaufman and Peter J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley-Interscience, 9th edition, 1990.

[11]

G. Klyne and J. J. Carroll. Resource Description Framework (RDF): Concepts and Abstract Syntax. Technical report, W3C, 2004.

[12]

M. Konrath, T. Gottron, S. Staab, and A. Scherp. Schemex - efficient construction of a data catalogue by stream-based indexing of linked data. Web Semantics: Science, Services and Agents on the World Wide Web, 16(5), 2012.

Digital Library

[13]

B. Larsen and C. Aone. Fast and effective text mining using linear-time document clustering. In KDD, pages 16--22, 1999.

Digital Library

[14]

N. W. Paton, K. Christodoulou, A. A. A. Fernandes, B. Parsia, and C. Hedeler. Pay-as-you-go data integration for linked data: opportunities, challenges and architectures. In SWIM, page 3, 2012.

Digital Library

[15]

F. Prasser, A. Kemper, and K. A. Kuhn. Efficient distributed query processing for autonomous rdf databases. In EDBT, pages 372--383, 2012.

Digital Library

[16]

E. Prud'hommeaux and A. Seaborne. SPARQL query language for rdf. W3C Recommendation, 4:1--106, 2008.

[17]

B. Quilitz and U. Leser. Querying distributed rdf data sources with sparql. In ESWC, pages 524--538, 2008.

Digital Library

[18]

A. Schwarte, P. Haase, K. Hose, R. Schenkel, and M. Schmidt. Fedx: Optimization techniques for federated query processing on linked data. In International Semantic Web Conference (1), pages 601--616, 2011.

Digital Library

[19]

Jürgen Umbrich, K. Hose, M. Karnstedt, A. Harth, and A. Polleres. Comparing data summaries for processing live queries over linked data. WWW, 14(5-6):495--544, 2011.

Digital Library

[20]

J. Völker and M. Niepert. Statistical schema induction. In ESWC (1), pages 124--138, 2011.

Digital Library

[21]

Y. Zhao and G. Karypis. Evaluation of hierarchical clustering algorithms for document datasets. In CIKM, pages 515--524, 2002.

Digital Library

[22]

N. Zong, D. Im, S. Yang, H. Namgoong, and H. Kim. Dynamic generation of concepts hierarchies for knowledge discovering in bio-medical linked data sets. In ICUIMC, page 12, 2012.

Digital Library

Cited By

Kellou-Menouer KKardoulakis NTroullinou GKedad ZPlexousakis DKondylakis H(2021)A survey on semantic schema discoveryThe VLDB Journal10.1007/s00778-021-00717-x31:4(675-710)Online publication date: 27-Nov-2021
https://doi.org/10.1007/s00778-021-00717-x
Gómez SFillottrani P(2020)Materialization of OWL Ontologies from Relational Databases: A Practical ApproachComputer Science – CACIC 201910.1007/978-3-030-48325-8_19(285-301)Online publication date: 14-May-2020
https://doi.org/10.1007/978-3-030-48325-8_19
Bouhamoum RKellou-Menouer KLopes SKedad Z(2018)Scaling Up Schema Discovery for RDF Datasets2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2018.00021(84-89)Online publication date: Apr-2018
https://doi.org/10.1109/ICDEW.2018.00021
Show More Cited By

Index Terms

Structure inference for linked data sources using clustering
1. Information systems

Recommendations

Using the relation ontology Metarel for modelling Linked Data as multi-digraphs
Linked Data for Health Care and the Life Sciences

The Semantic Web standards OWL and RDF are often used to represent biomedical information as Linked Data; however, the OWL/RDF syntax, which combines both, was never optimised for querying. By combining two formal paradigms for modelling Linked Data, ...
Read More
Understanding Linked Open Data: For Linked Data Discovery, Consumption, Triplification and Application Development
Read More
Leveraging Linked Data to Discover Semantic Relations Within Data Sources
The Semantic Web – ISWC 2016
Abstract
Mapping data to a shared domain ontology is a key step in publishing semantic content on the Web. Most of the work on automatically mapping structured and semi-structured sources to ontologies focuses on semantic labeling, i.e., annotating data ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 Workshops

March 2013

423 pages

ISBN:9781450315999

DOI:10.1145/2457317

General Chair:
Giovanna Guerrini
Università di Genova, Italy

Copyright © 2013 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

EDBT/ICDT '13

EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences

March 18 - 22, 2013

Genoa, Italy

Acceptance Rates

EDBT '13 Paper Acceptance Rate 7 of 10 submissions, 70%;

Overall Acceptance Rate 7 of 10 submissions, 70%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
232
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Other Metrics

View Author Metrics

Citations

Cited By

Kellou-Menouer KKardoulakis NTroullinou GKedad ZPlexousakis DKondylakis H(2021)A survey on semantic schema discoveryThe VLDB Journal10.1007/s00778-021-00717-x31:4(675-710)Online publication date: 27-Nov-2021
https://doi.org/10.1007/s00778-021-00717-x
Gómez SFillottrani P(2020)Materialization of OWL Ontologies from Relational Databases: A Practical ApproachComputer Science – CACIC 201910.1007/978-3-030-48325-8_19(285-301)Online publication date: 14-May-2020
https://doi.org/10.1007/978-3-030-48325-8_19
Bouhamoum RKellou-Menouer KLopes SKedad Z(2018)Scaling Up Schema Discovery for RDF Datasets2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2018.00021(84-89)Online publication date: Apr-2018
https://doi.org/10.1109/ICDEW.2018.00021
Reformat MChen JYager R(2018)Clustering of Propositions Equipped with UncertaintyInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_59(715-726)Online publication date: 18-May-2018
https://doi.org/10.1007/978-3-319-91479-4_59
Cheng GJin CQu Y(2016)HIEDSProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3061053.3061137(3705-3711)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3061053.3061137
Christodoulou KFernandes APaton N(2015)Combining Syntactic and Semantic Evidence for Improving Matching over Linked Data SourcesProceedings, Part I, of the 16th International Conference on Web Information Systems Engineering --- WISE 2015 - Volume 941810.1007/978-3-319-26190-4_14(200-215)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-26190-4_14
Kellou-Menouer KKedad Z(2015)Discovering Types in RDF DatasetsThe Semantic Web: ESWC 2015 Satellite Events10.1007/978-3-319-25639-9_15(77-81)Online publication date: 31-May-2015
https://dl.acm.org/doi/10.1007/978-3-319-25639-9_15
Kellou-Menouer KKedad Z(2015)Schema Discovery in RDF Data SourcesConceptual Modeling10.1007/978-3-319-25264-3_36(481-495)Online publication date: 8-Dec-2015
https://doi.org/10.1007/978-3-319-25264-3_36
Chen JReformat M(2014)Modelling of Experienced-Based Data in Linked Data EnvironmentProceedings of the 2014 International Conference on Intelligent Networking and Collaborative Systems10.1109/INCoS.2014.122(731-736)Online publication date: 10-Sep-2014
https://dl.acm.org/doi/10.1109/INCoS.2014.122
Chen JReformat M(2014)Learning Categories from Linked Open DataInformation Processing and Management of Uncertainty in Knowledge-Based Systems10.1007/978-3-319-08852-5_41(396-405)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08852-5_41
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents