research-article

Feedback-based data set recommendation for building linked data applications

Authors:

Hélio Rodrigues de Oliveira,

Alberto Trindade Tavares,

Bernadette Farias LóscioAuthors Info & Claims

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

Pages 49 - 55

https://doi.org/10.1145/2362499.2362507

Published: 05 September 2012 Publication History

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Abstract

The huge and growing volume of linked data is increasing the interest in developing applications on top of such data. One of the distinguishing features of linked data applications is that the data could come from any RDF data set available on the Web. Different from conventional applications, where the data sources are under control of the application's owner or developer, linked data applications follow the Semantic Web vision of a world full of reusable data. Considering a potentially large number of data sets, one of the primary challenges facing the development of such solutions is the identification of suitable data sources, i.e., data sets that could give a good contribution to the answer of user queries. In this paper, we discuss this problem and we present a feedback-based approach to incrementally identify new data sets for domain-specific linked data application.

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Alserafi AAbelló ARomero OCalders T(2020)Keeping the Data Lake in FormACM Transactions on Information Systems10.1145/338887038:3(1-30)Online publication date: 18-May-2020
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Chen ZJia HHeflin JDavison BChampin PGandon FMédini LLalmas MIpeirotis P(2018)Generating Schema Labels through Dataset Content AnalysisCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191601(1515-1522)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191601
Achichi MBen Ellefi MBellahsene ZTodorov K(2018)Doing Web Data: from Dataset Recommendation to Data LinkingNoSQL Data Models10.1002/9781119528227.ch3(57-91)Online publication date: 6-Aug-2018
https://doi.org/10.1002/9781119528227.ch3
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Index Terms

Feedback-based data set recommendation for building linked data applications
1. Information systems
  1. Data management systems
  2. Information systems applications

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Published In

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

September 2012

215 pages

ISBN:9781450311120

DOI:10.1145/2362499

Editors:
Harald Sack
Hasso-Plattner-Institut für Softwaresystemtechnik GmbH
,
Tassilo Pellegrini
University of Applied Sciences St. Pölten
,
Program Chairs:
Valentina Presutti
Institute for Cognitive Science and Technology, Rome - Italy
,
H. Sofia Pinto
Technical University of Lisbon - Portugal

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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Association for Computing Machinery

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Published: 05 September 2012

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I-SEMANTICS '12

I-SEMANTICS '12: I-SEMANTICS 2012 - 8th International Conference on Semantic Systems

September 5 - 7, 2012

Graz, Austria

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Alserafi AAbelló ARomero OCalders T(2020)Keeping the Data Lake in FormACM Transactions on Information Systems10.1145/338887038:3(1-30)Online publication date: 18-May-2020
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Chen ZJia HHeflin JDavison BChampin PGandon FMédini LLalmas MIpeirotis P(2018)Generating Schema Labels through Dataset Content AnalysisCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191601(1515-1522)Online publication date: 23-Apr-2018
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Achichi MBen Ellefi MBellahsene ZTodorov K(2018)Doing Web Data: from Dataset Recommendation to Data LinkingNoSQL Data Models10.1002/9781119528227.ch3(57-91)Online publication date: 6-Aug-2018
https://doi.org/10.1002/9781119528227.ch3
Martins Yda Mota FCavalcanti M(2016)DSCrank: A Method for Selection and Ranking of DatasetsMetadata and Semantics Research10.1007/978-3-319-49157-8_29(333-344)Online publication date: 4-Nov-2016
https://doi.org/10.1007/978-3-319-49157-8_29
Ben Ellefi MBellahsene ZDietze STodorov K(2016)Beyond Established Knowledge Graphs-Recommending Web Datasets for Data LinkingWeb Engineering10.1007/978-3-319-38791-8_15(262-279)Online publication date: 25-May-2016
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Ben Ellefi MBellahsene ZDietze STodorov K(2016)Dataset Recommendation for Data LinkingProceedings of the 13th International Conference on The Semantic Web. Latest Advances and New Domains - Volume 967810.1007/978-3-319-34129-3_3(36-51)Online publication date: 29-May-2016
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Wagner AHaase PRettinger ALamm H(2014)Entity-Based Data Source Contextualization for Searching the Web of DataThe Semantic Web: ESWC 2014 Satellite Events10.1007/978-3-319-11955-7_3(25-41)Online publication date: 16-Oct-2014
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Rabello Lopes GPaes Leme LPereira Nunes BCasanova MDietze S(2014)Two Approaches to the Dataset Interlinking Recommendation ProblemWeb Information Systems Engineering – WISE 201410.1007/978-3-319-11749-2_25(324-339)Online publication date: 2014
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Gunaratna KLalithsena SSheth A(2014)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: 1-Mar-2014
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Lalithsena SHitzler PSheth AJain P(2013)Automatic Domain Identification for Linked Open DataProceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 0110.1109/WI-IAT.2013.206(205-212)Online publication date: 17-Nov-2013
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