Lopes, G.R.; Leme, L.A.P.P.; Pereira Nunes, B.; Casanova, M.A.; Dietze, S.: Two approaches to the dataset interlinking recommendation problem. In: Benatallah, B.; Bestavros, A.; Manolopoulos, Y.; Vakali, A.; Zhang, Y. (Eds.): Web Information Systems Engineering – WISE 2014. Heidelberg : Springer Verlag, 2014 (Lecture Notes in Computer Science ; 8786), S. 324-339. DOI:
https://doi.org/10.1007/978-3-319-11749-2_25
Zusammenfassung: |
Whenever a dataset t is published on the Web of Data, an exploratory search over existing datasets must be performed to identify those datasets that are potential candidates to be interlinked with t. This paper introduces and compares two approaches to address the dataset interlinking recommendation problem, respectively based on Bayesian classifiers and on Social Network Analysis techniques. Both approaches define rank score functions that explore the vocabularies, classes and properties that the datasets use, in addition to the known dataset links. After extensive experiments using real-world datasets, the results show that the rank score functions achieve a mean average precision of around 60%. Intuitively, this means that the exploratory search for datasets to be interlinked with t might be limited to just the top-ranked datasets, reducing the cost of the dataset interlinking process. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11749-2_25.
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Lizenzbestimmungen: |
Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. |
Publikationstyp: |
BookPart |
Publikationsstatus: |
acceptedVersion |
Erstveröffentlichung: |
2014 |
Schlagwörter (englisch): |
Bayesian classifier, Data interlinking, Linked Data, Recommender systems, Social networks, Bayesian networks, Recommender systems, Social networking (online), Data interlinking, Exploratory search, Linked datum, Rank scores, Real-world datasets, Web of datum, Classification (of information)
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Fachliche Zuordnung (DDC): |
004 | Informatik
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