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
Link To Document :
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