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Improving Open Information Extraction for Semantic Web Tasks
- 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⟩