DocumentCode
658352
Title
Automatic Domain Identification for Linked Open Data
Author
Lalithsena, Sarasi ; Hitzler, Pascal ; Sheth, Amit ; Jain, Paril
Author_Institution
Kno.e.sis Center, Wright State Univ., Dayton, OH, USA
Volume
1
fYear
2013
fDate
17-20 Nov. 2013
Firstpage
205
Lastpage
212
Abstract
Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.
Keywords
Internet; data structures; Freebase; LOD; automatic domain identification; interlinked structured datasets; knowledge sources; linked open data; Animals; Drugs; Educational institutions; Rocks; TV; Vegetation; Dataset search; Domain Identification; Linked Open Data Cloud;
fLanguage
English
Publisher
ieee
Conference_Titel
Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on
Conference_Location
Atlanta, GA
Print_ISBN
978-1-4799-2902-3
Type
conf
DOI
10.1109/WI-IAT.2013.206
Filename
6690016
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