Mining conceptual graphs for knowledge acquisition

This work addresses the use of computational linguistic analysis techniques for conceptual graphs learning from unstructured texts. A technique including both content mining and interpretation, as well as clustering and data cleaning, is introduced. Our proposal exploits sentence structure in order 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 supervision, minimizing the possibility of failing to retrieve information contained in the document, in order to extract non-taxonomic relations. Categories and Subject Descriptors I.2.6 [Learning]: Knowledge acquisition; I.2.7 [Natural Language Processing]: Language parsing and understanding General Terms Management Keywords Classification,Clustering, Knowledge synthesis and visualization, Text Mining

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Milagros Fernández, Eric Villemonte de la Clergerie, Manuel Vilares Ferro
• Keywords: CIKM 2008, Information Management, Knowledge Acquisition, Knowledge Acquisition Task, Proposal Exploits Sentence