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Learning URI selection criteria to improve the crawling of linked open data (extended abstract)

Learning URI selection criteria to improve the crawling of linked open data (extended abstract)

Huang, Hai ORCID: 0000-0003-1412-0567 and Gandon, Fabien (2021) Learning URI selection criteria to improve the crawling of linked open data (extended abstract). In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI) Sister Conferences Best Papers. International Joint Conferences on Artificial Intelligence, Yokohama, Japan, pp. 4730-4734. ISBN 978-0999241165 (doi:https://doi.org/10.24963/ijcai.2020/655)

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Abstract

A Linked Data crawler performs a selection to focus on collecting linked RDF (including RDFa) data on the Web. From the perspectives of through-put and coverage, given a newly discovered and targeted URI, the key issue of Linked Data crawlers is to decide whether this URI is likely to dereference into an RDF data source and therefore it is worth downloading the representation it points to. Current solutions adopt heuristic rules to filter irrelevant URIs. But when the heuristics are too restrictive this hampers the coverage of crawling. In this paper, we propose and compare approaches to learn strategies for crawling Linked Data on the Web by predicting whether a newly discovered URI will lead to an RDF data source or not. We detail the features used in predicting the relevance and the methods we evaluated including a promising adaptation of FTRL-proximal online learning algorithm. We compare several options through extensive experiments including existing crawlers as baseline methods to evaluate their efficiency.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI) Sister Conferences Best Papers.
Uncontrolled Keywords: linked RDF data; crawling; FTRL
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Faculty of Liberal Arts & Sciences
Last Modified: 09 Jun 2022 11:05
URI: http://gala.gre.ac.uk/id/eprint/36599

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