Document Type
Conference Proceeding
Publication Date
11-2013
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.
Repository Citation
Lalithsena, S.,
Hitzler, P.,
Sheth, A. P.,
& Jain, P.
(2013). Automatic Domain Identification for Linked Open Data. Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 205-212.
https://corescholar.libraries.wright.edu/knoesis/537
DOI
10.1109/WI-IAT.2013.206
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Attached is author's accepted manuscript of the proceeding. The final, publisher's version can be found at http://dx.doi.org/10.1109/WI-IAT.2013.206.
Presented at the IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, Atlanta, GA, November 17-20, 2013.