Paper Info
Title
Aligning ontologies with subsumption and equivalence relations in Linked Data.
Abstract
With the profusion of RDF resources and Linked Data, ontology alignment has gained significance in providing highly comprehensive knowledge embedded in disparate sources. Ontology alignment, however, in Linking Open Data (LOD) has traditionally focused more on the instance-level rather than the schema-level. Linked Data supports schema-level alignment, provided that instance-level alignment is already established. Linked Data is a hotbed for instance-based schema alignment, which is considered a better solution for aligning classes with ambiguous or obscure names. This study proposes an instance-based schema alignment algorithm, IUT, which builds a unified taxonomy to discover subsumption and equivalence relations between two classes. A scaling algorithm is also developed that reduces pair-wise similarity computations during the taxonomy construction. The IUT is tested with DBpedia and YAGO2, and compared with two state-of-the-art schema alignment algorithms in light of four alignment tasks with different combinations of the two data sets. The experiment results show that the IUT outperforms the two algorithms in efficiency and effectiveness, and demonstrate the IUT can provide an instance-based schema alignment solution with scalability and high performance, for ontologies containing a large number of instances in LOD.
Year
DOI
Venue
2015
10.1016/j.knosys.2014.11.022
Knowl.-Based Syst.
Keywords
Field
DocType
linked data,ontology alignment
Ontology (information science),Data mining,Ontology alignment,Data set,Equivalence relation,Information retrieval,Computer science,Linked data,Schema (psychology),RDF,Scalability
Journal
Volume
Issue
ISSN
76
1
0950-7051
Citations 
PageRank 
References 
9
0.48
29
Authors
6
Name
Order
Citations
PageRank
Nansu Zong1455.68
Sejin Nam22823.20
Jae-Hong Eom3868.91
Jinhyun Ahn4255.65
Hyunwhan Joe5102.85
Hong-Gee Kim610418.80