skip to main content
10.1145/2309996.2310004acmconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
research-article

Moving beyond SameAs with PLATO: partonomy detection for linked data

Published: 25 June 2012 Publication History

Abstract

The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.
Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.
In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.

References

[1]
K. Alexander, R. Cyganiak, M. Hausenblas, and J. Zhao. Describing Linked Datasets -- On the Design and Usage of voiD, the 'Vocabulary of Interlinked Datasets'. In WWW2009 Workshop on Linked Data on the Web (LDOW2009), Madrid, Spain, 2009.
[2]
A. Artale, E. Franconi, N. Guarino, and L. Pazzi. Part-whole relations in object-centered systems: An overview. Data & Knowledge Engineering, 20(3):347--383, 1996.
[3]
Michael K. Bergman and Frédérick Giasson. UMBEL ontology, volume 1, technical documentation. Technical Report 1, Structured Dynamics, 2008. Available from: http://umbel.org/doc/UMBELOntology_vA1.pdf.
[4]
Christian Bizer, Tom Heath, and Tim Berners Lee. Linked data - the story so far. International Journal on Semantic Web and Information Systems, 5(3):1--22, 2009.
[5]
Christian Bizer, Jens Lehmann, Georgi Kobilarov, Sören Auer, Christian Becker, Richard Cyganiak, and Sebastian Hellmann. DBpedia-A crystallization point for the Web of Data. Journal of Web Semantics, 7(3):154--165, 2009.
[6]
Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka Jr., and Tom M. Mitchell. Toward an architecture for never-ending language learning. In Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010), 2010.
[7]
R. Casati and A.C. Varzi. Parts and places: The structures of spatial representation. The MIT Press, 1999.
[8]
Namyoun Choi, Il-Yeol Song, and Hyoil Han. A survey on ontology mapping. SIGMOD Rec., 35(3):34--41, 2006.
[9]
Philipp Cimiano, Andreas Hotho, and Steffen Staab. Learning concept hierarchies from text corpora using formal concept analysis. J. Artif. Int. Res., 24:305--339, August 2005.
[10]
Jérôme Euzenat and Pavel Shvaiko. Ontology matching. Springer-Verlag, Heidelberg (DE), 2007.
[11]
Christiane Fellbaum, editor. WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, illustrated edition edition, May 1998.
[12]
David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A Kalyanpur, Adam Lally, J William Murdock, Eric Nyberg, and John Prager. Building watson: An overview of the deepqa project. AI Magazine, 31(3):59--79, 2010.
[13]
P. Gerstl and S. Pribbenow. A conceptual theory of part-whole relations and its applications. Data & Knowledge Engineering, 20(3):305--322, 1996.
[14]
R. Girju, A. Badulescu, and D. Moldovan. Automatic discovery of part-whole relations. Computational Linguistics, 32(1):83--135, 2006.
[15]
Michael Hausenblas. Exploiting linked data to build web applications. IEEE Internet Computing, 13:68--73, 2009.
[16]
Marti A. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics -- Volume 2, COLING '92, pages 539--545, Stroudsburg, PA, USA, 1992.
[17]
P. Hitzler, M. Krötzsch, B. Parsia, P.F. Patel-Schneider, and S. Rudolph, editors. OWL 2 Web Ontology Language: Primer. W3C Recommendation, 27 October 2009. Available at http://www.w3.org/TR/owl2-primer/.
[18]
Ian Horrocks, Peter F. Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, and Mike Dean. SWRL: A Semantic Web Rule Language Combining OWL and RuleML. W3C Member Submission 21 May 2004, 2004. Available from http://www.w3.org/Submission/SWRL/.
[19]
Prateek Jain, Pascal Hitzler, Peter Z. Yeh, Kunal Verma, and Amit P. Sheth. Linked Data is Merely More Data. In Linked Data Meets Artificial Intelligence, pages 82--86. AAAI Press, Menlo Park, CA, 2010.
[20]
Prateek Jain, Peter Z. Yeh, Kunal Verma, Cory A. Henson, and Amit P. Sheth. SPARQL query re-writing using partonomy based transformation rules. In Proceedings of the 3rd International Conference on GeoSpatial Semantics, GeoS '09, pages 140--158, Berlin, Heidelberg, 2009. Springer-Verlag.
[21]
Adila Krisnadhi, Frederick Maier, and Pascal Hitzler. OWL and Rules. In Reasoning Web. Semantic Technologies for the Web of Data -- 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures, volume 6848 of Lecture Notes in Computer Science, pages 382--415. Springer, Heidelberg, 2011.
[22]
Markus Krötzsch, Frederick Maier, Adila A. Krisnadhi, and Pascal Hitzler. A better uncle for OWL: Nominal schemas for integrating rules and ontologies. In Proceedings of the 20th International World Wide Web Conference, WWW2011, Hyderabad, India, March/April 2011, pages 645--654. ACM, New York, 2011.
[23]
Jens Lehmann and Pascal Hitzler. Concept learning in description logics using refinement operators. Machine Learning, 78(1--2):203--250, 2010.
[24]
B. Motik, P.F. Patel-Schneider, and B. Parsia, editors. OWL 2 Web Ontology Language: Structural Specification and Functional-Style Syntax. W3C Recommendation, 27 October 2009. Available at http://www.w3.org/TR/owl2-syntax/.
[25]
Boris Motik, Ulrike Sattler, and Rudi Studer. Query answering for OWL DL with rules. Journal of Web Semantics, 3(1):41--60, 2005.
[26]
Patrick Pantel and Marco Pennacchiotti. Espresso: leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, ACL-44, pages 113--120, Stroudsburg, PA, USA, 2006.
[27]
Alan Rector, Chris Welty, Natasha Noy, and Evan Wallace. Simple part-whole relations in OWL Ontologies available at http://www.w3.org/2001/sw/bestpractices/oep/simplepartwhole/, August 2005.
[28]
Barry Smith. The basic tools of formal ontology. In Formal Ontology in Information Systems, 1998.
[29]
Nectaria Tryfona and Max J. Egenhofer. Consistency among parts and aggregates: A computational model. Transactions in GIS, 1(3):189--206, 1996.
[30]
Willem van Hage, Hap Kolb, and Guus Schreiber. A method for learning part-whole relations. In The Semantic Web - ISWC 2006, volume 4273 of Lecture Notes in Computer Science, pages 723--735. Springer Berlin / Heidelberg, 2006.
[31]
J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov. Silk--A Link Discovery Framework for the Web of Data. In 2nd Linked Data on the Web Workshop (LDOW2009), Madrid, Spain, 2009. Available from http://ceur-ws.org/Vol-538/ldow2009_paper13.pdf.
[32]
Julius Volz, Christian Bizer, Martin Gaedke, and Georgi Kobilarov. Discovering and maintaining links on the web of data. In ISWC '09: Proceedings of the 8th International Semantic Web Conference, pages 650--665, Berlin, Heidelberg, 2009. Springer-Verlag.
[33]
Morton E. Winston, Roger Chaffin, and Douglas Herrmann. A taxonomy of part-whole relations. Cognitive Science, 11(4):417--444, 1987.

Cited By

View all
  • (2018)Towards Compiling Textbooks from WikipediaAI 2018: Advances in Artificial Intelligence10.1007/978-3-030-03991-2_75(828-842)Online publication date: 10-Nov-2018
  • (2018)Alignment and dataset identification of linked data in Semantic WebWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11214:2(139-151)Online publication date: 14-Dec-2018
  • (2013)Constructing consumer profiles from social media data2013 IEEE International Conference on Big Data10.1109/BigData.2013.6691641(710-716)Online publication date: Oct-2013
  • Show More Cited By

Index Terms

  1. Moving beyond SameAs with PLATO: partonomy detection for linked data

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      HT '12: Proceedings of the 23rd ACM conference on Hypertext and social media
      June 2012
      340 pages
      ISBN:9781450313353
      DOI:10.1145/2309996
      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]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 June 2012

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. linked open data cloud
      2. mereology
      3. part of relation

      Qualifiers

      • Research-article

      Conference

      HT '12
      Sponsor:
      HT '12: 23rd ACM Conference on Hypertext and Social Media
      June 25 - 28, 2012
      Wisconsin, Milwaukee, USA

      Acceptance Rates

      HT '12 Paper Acceptance Rate 33 of 120 submissions, 28%;
      Overall Acceptance Rate 378 of 1,158 submissions, 33%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)4
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 05 Mar 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2018)Towards Compiling Textbooks from WikipediaAI 2018: Advances in Artificial Intelligence10.1007/978-3-030-03991-2_75(828-842)Online publication date: 10-Nov-2018
      • (2018)Alignment and dataset identification of linked data in Semantic WebWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11214:2(139-151)Online publication date: 14-Dec-2018
      • (2013)Constructing consumer profiles from social media data2013 IEEE International Conference on Big Data10.1109/BigData.2013.6691641(710-716)Online publication date: Oct-2013
      • (2012)Controlled knowledge base enrichment from web documentsProceedings of the 13th international conference on Web Information Systems Engineering10.1007/978-3-642-35063-4_23(312-325)Online publication date: 28-Nov-2012

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media