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Chapitre D'ouvrage Année : 2016

Improving open information extraction for semantic web tasks

Résumé

Open Information Extraction (OIE) aims to automatically identify all the possible assertions within a sentence. Results of this task are usually a set of triples (subject, predicate, object). In this paper, we first present what OIE is and how it can be improved when we work in a given domain of knowledge. Using a corpus made up of sentences in building engineering construction, we obtain an improvement of more than 18%. Next, we show how OIE can be used at a base of a high-level semantic web task. Here we have applied OIE on formalisation of natural language definitions. We test this formalisation task on a corpus of sentences defining concepts found in the pizza ontology. At this stage, 70.27% of our 37 sentences-corpus are fully rewritten in OWL DL
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Dates et versions

hal-01229542 , version 1 (31-12-2015)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Cheikh Hito Kacfah Emani, Catarina Ferreira da Silva, Bruno Fies, Parisa Ghodous. Improving open information extraction for semantic web tasks. Transactions on computational collective intelligence XXI, 9630, Springer Verlag, p. 139-158, 2016, Lecture Notes in Computer Science, 978-3-662-49520-9. ⟨10.1007/978-3-662-49521-6_6⟩. ⟨hal-01229542⟩
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