From hyperlinks to Semantic Web properties using Open Knowledge Extraction

Issue title: Selected papers from the combined EKAW 2014 and Semantic Web journal track

Guest editors: Stefan Schlobach and Krzysztof Janowicz

Article type: Research Article

Authors: Presutti, Valentina^{a; *} | Nuzzolese, Andrea Giovanni^a | Consoli, Sergio^a | Gangemi, Aldo^{a; b} | Reforgiato Recupero, Diego^a

Affiliations: [a] STLab, Institute of Cognitive Sciences and Technologies, National Research Council, via San Martino della Battaglia 44, 00185, Roma, Italy. E-mails: [email protected], [email protected], [email protected], [email protected] | [b] LIPN, Université Paris 13 – Sorbonne Cité – CNRS, France

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Open information extraction approaches are useful but insufficient alone for populating the Web with machine readable information as their results are not directly linkable to, and immediately reusable from, other Linked Data sources. This work proposes a novel paradigm, named Open Knowledge Extraction, and its implementation (Legalo) that performs unsupervised, open domain, and abstractive knowledge extraction from text for producing machine readable information. The implemented method is based on the hypothesis that hyperlinks (either created by humans or knowledge extraction tools) provide a pragmatic trace of semantic relations between two entities, and that such semantic relations, their subjects and objects, can be revealed by processing their linguistic traces (i.e. the sentences that embed the hyperlinks) and formalised as Semantic Web triples and ontology axioms. Experimental evaluations conducted on validated text extracted from Wikipedia pages, with the help of crowdsourcing, confirm this hypothesis showing high performances. A demo is available at http://wit.istc.cnr.it/stlab-tools/legalo.

Keywords: Open Knowledge Extraction, open information extraction, abstractive summarisation, link semantics, relation extraction

DOI: 10.3233/SW-160221

Journal: Semantic Web, vol. 7, no. 4, pp. 351-378, 2016