@conference {94,
	title = {Automatic Domain Identification for Linked Open Data},
	booktitle = {2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013},
	year = {2013},
	pages = {205{\textendash}212},
	address = {Atlanta, GA, USA},
	abstract = {<p class="rtejustify">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.</p>
},
	keywords = {dataset search, Domain Identification, Linked Open Data Cloud},
	doi = {10.1109/WI-IAT.2013.206},
	url = {http://dx.doi.org/10.1109/WI-IAT.2013.206},
	author = {Sarasi Lalithsena and Pascal Hitzler and Amit Sheth and Prateek Jain}
}
