Mining Conceptual Graphs for Knowledge Acquisition

Abstract : This work addresses the use of computational linguistic anal- ysis techniques for conceptual graphs learning from unstruc- tured texts. A technique including both content mining and interpretation, as well as clustering and data cleaning, is introduced. Our proposal exploits sentence structure in or- der to generate concept hypothese, rank them according to plausibility and select the most credible ones. It enables the knowledge acquisition task to be performed without su- pervision, minimizing the possibility of failing to retrieve information contained in the document, in order to extract non-taxonomic relations.
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Conference papers
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https://hal.inria.fr/inria-00553518
Contributor : Eric Villemonte de la Clergerie <>
Submitted on : Friday, January 7, 2011 - 3:00:01 PM
Last modification on : Monday, August 26, 2019 - 4:50:30 PM

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Milagro Fernández Gavilanes, Éric Villemonte de la Clergerie, Manuel Vilares Ferro. Mining Conceptual Graphs for Knowledge Acquisition. ACM CIKM Workshop on Improving Non-English Web Searching : inews'08, Oct 2008, Napa Valley, CA, United States. pp.25--32, ⟨10.1145/1460027.1460032⟩. ⟨inria-00553518⟩

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