|
Type prediction in RDF knowledge bases using hierarchical multilabel classification
Melo, André
;
Paulheim, Heiko
;
Völker, Johanna
|
DOI:
|
https://doi.org/10.1145/2912845.2912861
|
|
URL:
|
https://madoc.bib.uni-mannheim.de/40970
|
|
Additional URL:
|
http://dl.acm.org/citation.cfm?doid=2912845.291286...
|
|
URN:
|
urn:nbn:de:bsz:180-madoc-409704
|
|
Document Type:
|
Conference or workshop publication
|
|
Year of publication:
|
2016
|
|
Book title:
|
Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016, Nîmes, France, June 13-15, 2016
|
|
Page range:
|
Article 14, 1-10
|
|
Conference title:
|
6th International Conference on Web Intelligence, Mining and Semantics, {WIMS} 2016
|
|
Location of the conference venue:
|
Nimes, France
|
|
Date of the conference:
|
June 13-15, 2016
|
|
Publisher:
|
Akerkar, Rajendra
|
|
Place of publication:
|
New York, NY
|
|
Publishing house:
|
ACM
|
|
ISSN:
|
978-1-4503-4056-4
|
|
Publication language:
|
English
|
|
Institution:
|
School of Business Informatics and Mathematics > Information Systems V: Web-based Systems (Bizer 2012-)
School of Business Informatics and Mathematics > Web Data Mining (Juniorprofessur) (Paulheim 2013-2017)
|
|
Subject:
|
004 Computer science, internet
|
|
Abstract:
|
Large Semantic Web knowledge bases are often noisy, incorrect, and incomplete with respect to type information. Automatic type prediction can help reduce such incompleteness, and, as previous works show, statistical methods are well-suited for this kind of data. Since most Semantic Web knowledge bases come with an ontology defining a type hierarchy, in this paper, we rephrase the type prediction problem as a hierarchical multilabel classification problem. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier. Our approach improves scalability, facilitating its application on large Semantic Web datasets with high-dimensional feature and label spaces. We compare the performance of our proposed method with a state-of-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale RDF datasets.
|
 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
 Search Authors in
BASE:
Melo, André
;
Paulheim, Heiko
;
Völker, Johanna
Google Scholar:
Melo, André
;
Paulheim, Heiko
;
Völker, Johanna
ORCID:
Melo, André, Paulheim, Heiko  ORCID: https://orcid.org/0000-0003-4386-8195 and Völker, Johanna
You have found an error? Please let us know about your desired correction here: E-Mail
Actions (login required)
 |
Show item |
|
