Home // SEMAPRO 2010, The Fourth International Conference on Advances in Semantic Processing // View article

Detecting Hidden Relations in Geographic Data

Authors:
Ngoc-Thanh Le
Ryutaro Ichise
Hoai-Bac Le

Keywords: Linked Data; Knowledge Discovery; Link Prediction

Abstract:
The amount of linked data is growing rapidly, and so ?nding suitable entities to link together requires greater effort. For small data sets, it is easy enough to ?nd entities in the data sources and link these together manually; however, doing so for large data sets is impractical. For large sets, a way is needed to discover entities and connect them automatically. In this paper, we present an algorithm to detect hidden owl:sameAs links or hidden relations in data sets. Since geographic names are often highly ambiguous, we used data sets comprising geographic names to implement and evaluate our algorithm. We experimentally compare our algorithm with a naive algorithm that only uses a URI’s name feature. We found that it is more accurate than the naive algorithm in most cases, especially for resources in which there is little matching information about features.

Pages: 61 to 68

Copyright: Copyright (c) IARIA, 2010

Publication date: October 25, 2010

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-104-5

Location: Florence, Italy

Dates: from October 25, 2010 to October 30, 2010