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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Communication dans un congrès
Fotis Lazarinis, Efthimis N. Efthimiadis, Jesús Vilares and John Tait. ACM CIKM Workshop on Improving Non-English Web Searching : inews'08, Oct 2008, Napa Valley, CA, United States. ACM, pp.25--32, 2008, 〈10.1145/1460027.1460032〉
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https://hal.inria.fr/inria-00553518
Contributeur : Eric Villemonte de La Clergerie <>
Soumis le : vendredi 7 janvier 2011 - 15:00:01
Dernière modification le : vendredi 25 mai 2018 - 12:02:05

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Milagro Fernández Gavilanes, Éric De La Clergerie, Manuel Vilares Ferro. Mining Conceptual Graphs for Knowledge Acquisition. Fotis Lazarinis, Efthimis N. Efthimiadis, Jesús Vilares and John Tait. ACM CIKM Workshop on Improving Non-English Web Searching : inews'08, Oct 2008, Napa Valley, CA, United States. ACM, pp.25--32, 2008, 〈10.1145/1460027.1460032〉. 〈inria-00553518〉

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