short-paper

Context and target configurations for mining RDF data

Authors:

Ziawasch Abedjan and

Felix NaumannAuthors Info & Claims

SMER '11: Proceedings of the 1st international workshop on Search and mining entity-relationship data

October 2011

Pages 23 - 24

https://doi.org/10.1145/2064988.2064998

Published: 28 October 2011 Publication History

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Abstract

Association rule mining has been widely studied in the context of basket analysis and sale recommendations [1]. In fact, the concept can be applied to any domain with many items or events in which interesting relationships can be inferred from co-occurrence of those items or events in existing subsets (transactions). The increasing amount of Linked Open Data (LOD) in the World Wide Web raises new opportunities and challenges for the data mining community [5]. LOD is often represented in the Resource Description Framework (RDF) data model. In RDF, data is represented by a triple structure consisting of subject, predicate, and object (SPO). Each triple represents a statement/fact. We propose an approach that applies association rule mining at statement level by introducing the concept of mining configurations.

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Cited By

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C.B. AMahesh K(2023)Ontology-based data interestingness: A state-of-the-art reviewNatural Language Processing Journal10.1016/j.nlp.2023.1000214(100021)Online publication date: Sep-2023
https://doi.org/10.1016/j.nlp.2023.100021
Sumathi GAkilandeswari J(2020)Improved fuzzy weighted‐iterative association rule based ontology postprocessing in data mining for query recommendation applicationsComputational Intelligence10.1111/coin.1226936:2(773-782)Online publication date: 24-Jan-2020
https://doi.org/10.1111/coin.12269
Issa SHamdi FCherfi S(2019)Enhancing the Conciseness of Linked Data by Discovering Synonym PredicatesKnowledge Science, Engineering and Management10.1007/978-3-030-29551-6_65(739-750)Online publication date: 21-Aug-2019
https://doi.org/10.1007/978-3-030-29551-6_65
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Index Terms

Context and target configurations for mining RDF data
1. Information systems
  1. Information systems applications

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Published In

SMER '11: Proceedings of the 1st international workshop on Search and mining entity-relationship data

October 2011

36 pages

ISBN:9781450309578

DOI:10.1145/2064988

Program Chairs:
Haggai Roitman
IBM Research, Israel
,
Ralf Schenkel
Saarland University and Max-Planck-Institut Informatik - Saarbrücken, Germany
,
Marko Grobelnik
J. Stefan Institute, Department for Intelligent Systems, Slovenia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 October 2011

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CIKM '11

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CIKM '11: International Conference on Information and Knowledge Management

October 28, 2011

Glasgow, Scotland, UK

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Cited By

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C.B. AMahesh K(2023)Ontology-based data interestingness: A state-of-the-art reviewNatural Language Processing Journal10.1016/j.nlp.2023.1000214(100021)Online publication date: Sep-2023
https://doi.org/10.1016/j.nlp.2023.100021
Sumathi GAkilandeswari J(2020)Improved fuzzy weighted‐iterative association rule based ontology postprocessing in data mining for query recommendation applicationsComputational Intelligence10.1111/coin.1226936:2(773-782)Online publication date: 24-Jan-2020
https://doi.org/10.1111/coin.12269
Issa SHamdi FCherfi S(2019)Enhancing the Conciseness of Linked Data by Discovering Synonym PredicatesKnowledge Science, Engineering and Management10.1007/978-3-030-29551-6_65(739-750)Online publication date: 21-Aug-2019
https://doi.org/10.1007/978-3-030-29551-6_65
Asadifar SKahani M(2017)Semantic association rule mining: A new approach for stock market prediction2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)10.1109/CSIEC.2017.7940158(106-111)Online publication date: Mar-2017
https://doi.org/10.1109/CSIEC.2017.7940158
MAHULE RVYAS O(2016)Leveraging linked open data information extraction for data mining applicationsTURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES10.3906/elk-1412-2824(4874-4884)Online publication date: 2016
https://doi.org/10.3906/elk-1412-28
Santosh DVardhan B(2016)PROO ontology development for learning feature specific sentiment relationship rules on reviews categorisationInternational Journal of Metadata, Semantics and Ontologies10.1504/IJMSO.2016.07810511:1(29-38)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJMSO.2016.078105
Collovini SMachado GVieira R(2016)Extracting and Structuring Open Relations from Portuguese TextComputational Processing of the Portuguese Language10.1007/978-3-319-41552-9_16(153-164)Online publication date: 21-Jun-2016
https://doi.org/10.1007/978-3-319-41552-9_16
Rabatel JCroitoru MIenco DPoncelet P(2014)Contextual itemset mining in DBpediaProceedings of the 1st International Conference on Linked Data for Knowledge Discovery - Volume 123210.5555/3053827.3053830(22-31)Online publication date: 19-Sep-2014
https://dl.acm.org/doi/10.5555/3053827.3053830
Abedjan ZGruetze TJentzsch ANaumann F(2014)Profiling and mining RDF data with ProLOD++2014 IEEE 30th International Conference on Data Engineering10.1109/ICDE.2014.6816740(1198-1201)Online publication date: Mar-2014
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Abedjan ZNaumann F(2014)Amending RDF Entities with New FactsThe Semantic Web: ESWC 2014 Satellite Events10.1007/978-3-319-11955-7_11(131-143)Online publication date: 16-Oct-2014
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