Automatic Domain Identification for Linked Open Data

被引:16
作者
Lalithsena, Sarasi [1 ]
Hitzler, Pascal [1 ]
Sheth, Amit [1 ]
Jain, Prateek [2 ]
机构
[1] Wright State Univ, Knoesis Ctr, Dayton, OH 45435 USA
[2] IBM TJ, Water Res Ctr, Yorktown Hts, NY USA
来源
2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 | 2013年
基金
美国国家科学基金会;
关键词
Linked Open Data Cloud; Domain Identification; Dataset search; WATSON; WEB;
D O I
10.1109/WI-IAT.2013.206
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:205 / 212
页数:8
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