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Improving Open Information Extraction for Semantic Web Tasks

Authors :
Kacfah Emani, Cheikh Hito
Ferreira Da Silva, Catarina
Fies, Bruno
Ghodous, Parisa
Centre Scientifique et Technique du Bâtiment (CSTB)
Service Oriented Computing (SOC)
Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS)
Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)
Université de Lyon-École Centrale de Lyon (ECL)
Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)
Université de Lyon-Université Lumière - Lyon 2 (UL2)
Source :
Transactions on Computational Collective Intelligence, Transactions on Computational Collective Intelligence, Springer, 2016, Lecture Notes in Computer Science, 9630, ⟨10.1007/978-3-662-49521-6_6⟩
Publication Year :
2016
Publisher :
HAL CCSD, 2016.

Abstract

International audience; 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

Details

Language :
English
ISSN :
21909288 and 25116053
Database :
OpenAIRE
Journal :
Transactions on Computational Collective Intelligence, Transactions on Computational Collective Intelligence, Springer, 2016, Lecture Notes in Computer Science, 9630, ⟨10.1007/978-3-662-49521-6_6⟩
Accession number :
edsair.dedup.wf.001..f3298078f757e2241fd748a616487a68
Full Text :
https://doi.org/10.1007/978-3-662-49521-6_6⟩