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Raven: Towards zero-configuration link discovery (2011)
| Venue: | In Proceedings of OM@ISWC |
| Citations: | 1 - 1 self |
BibTeX
@INPROCEEDINGS{Ngomo11raven:towards,
author = {Axel-cyrille Ngonga Ngomo and Jens Lehmann},
title = {Raven: Towards zero-configuration link discovery},
booktitle = {In Proceedings of OM@ISWC},
year = {2011}
}
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Abstract
Abstract. With the growth of the Linked Data Web, time-efficient ap-proaches for computing links between data sources have become indis-pensable. Yet, in many cases, determining the right specification for a link discovery problem is a tedious task that must still be carried out manually. In this article we present RAVEN, an approach for the semi-automatic determination of link specifications. Our approach is based on the combination of stable solutions of matching problems and active learning leveraging the time-efficient link discovery framework LIMES. RAVEN is designed to require a small number of interactions with the user in order to generate classifiers of high accuracy. We focus with RAVEN on the computation and configuration of Boolean and weighted classifiers, which we evaluate in three experiments against link specifi-cations created manually. Our evaluation shows that we can compute linking configurations that achieve more than 90 % F-score by asking the user to verify at most twelve potential links.
Keyphrases
towards zero-configuration link discovery many case data source link specification right specification active learning linked data web link discovery problem tedious task time-efficient link discovery framework lime semi-automatic determination high accuracy potential link stable solution time-efficient ap-proaches present raven small number
