Evaluating Uncertainty in Textual Document

Fadhela Kerdjoudj (1) , Olivier Curé (1)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge

In this work, we consider that a close collaboration between the research fields of Natural Language Processing and Knowledge Representation becomes essential to fulfill the vision of the Semantic Web. This will permit to retrieve information from vast amount of textual documents present on the Web and to represent these extractions in an amenable manner for querying and reasoning purposes. In such a context , uncertain, incomplete and ambiguous information must be handled properly. In the following, we present a solution that enables to qualify and quantify the uncertainty of extracted information from linguistic treatment.

Domaines: Intelligence artificielle [cs.AI]

Fadhela Kerdjoudj, Olivier Curé. Evaluating Uncertainty in Textual Document. URSW at ISWC, 2015, Bethlehem, United States. ⟨hal-01740539⟩