An Automatic sameAs Link Discovery from Wikipedia

The importance of manual assessment in link discovery

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09 ◽

10.1145/1571941.1572084 ◽

2009 ◽

Cited By ~ 1

Author(s):

Wei Che Huang ◽

Andrew Trotman ◽

Shlomo Geva

Keyword(s):

Link Discovery

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An Automatic sameAs Link Discovery from Wikipedia

Semantic Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-319-14122-0_29 ◽

2014 ◽

pp. 399-413 ◽

Cited By ~ 2

Author(s):

Kosuke Kagawa ◽

Susumu Tamagawa ◽

Takahira Yamaguchi

Keyword(s):

Link Discovery

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Link Mining and Link Discovery

Encyclopedia of Machine Learning ◽

10.1007/978-0-387-30164-8_480 ◽

2011 ◽

pp. 606-609 ◽

Cited By ~ 2

Author(s):

Geoffrey I. Webb ◽

Claude Sammut ◽

Claudia Perlich ◽

Tamás Horváth ◽

Stefan Wrobel ◽

...

Keyword(s):

Link Discovery

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Simple routing algorithm with link discovery between source and destination hosts in SDN networks

10.1109/icai52893.2021.9639742 ◽

2021 ◽

Author(s):

Dimitar Todorov ◽

Hristo Valchanov ◽

Veneta Aleksieva

Keyword(s):

Routing Algorithm ◽

Link Discovery

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Performance Evaluation Metrics for Link Discovery Systems

Intelligent Systems Design and Applications ◽

10.1007/978-3-540-44999-7_26 ◽

2003 ◽

pp. 273-282 ◽

Cited By ~ 2

Author(s):

M. Afzal Upal

Keyword(s):

Performance Evaluation ◽

Evaluation Metrics ◽

Link Discovery

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Evaluating Outliers for Cross-Context Link Discovery

Artificial Intelligence in Medicine - Lecture Notes in Computer Science ◽

10.1007/978-3-642-22218-4_44 ◽

2011 ◽

pp. 343-347 ◽

Cited By ~ 1

Author(s):

Borut Sluban ◽

Matjaž Juršič ◽

Bojan Cestnik ◽

Nada Lavrač

Keyword(s):

Link Discovery

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ORCHID – Reduction-Ratio-Optimal Computation of Geo-spatial Distances for Link Discovery

Advanced Information Systems Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-642-41335-3_25 ◽

2013 ◽

pp. 395-410 ◽

Cited By ~ 14

Author(s):

Axel-Cyrille Ngonga Ngomo

Keyword(s):

Reduction Ratio ◽

Link Discovery

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An Unsupervised Approach for Determining Link Specifications

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2018100106 ◽

2018 ◽

Vol 13 (4) ◽

pp. 104-123

Author(s):

Khayra Bencherif ◽

Mimoun Malki ◽

Djamel Amar Bensaber

Keyword(s):

Linked Data ◽

Open Data ◽

Real Data ◽

Knowledge Bases ◽

Structured Data ◽

Data Sets ◽

Novel Approach ◽

Link Discovery ◽

Unsupervised Approach

This article describes how the Linked Open Data Cloud project allows data providers to publish structured data on the web according to the Linked Data principles. In this context, several link discovery frameworks have been developed for connecting entities contained in knowledge bases. In order to achieve a high effectiveness for the link discovery task, a suitable link configuration is required to specify the similarity conditions. Unfortunately, such configurations are specified manually; which makes the link discovery task tedious and more difficult for the users. In this article, the authors address this drawback by proposing a novel approach for the automatic determination of link specifications. The proposed approach is based on a neural network model to combine a set of existing metrics into a compound one. The authors evaluate the effectiveness of the proposed approach in three experiments using real data sets from the LOD Cloud. In addition, the proposed approach is compared against link specifications approaches to show that it outperforms them in most experiments.

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