Using Frames to converting texts to ontologies. Solutions can be combined?
Alma Delia Cuevas (CIC-IPN and UAEM) México
Omega-S208 Campus Nord - UPC
Wed Nov 27, 2013
To automatically extract knowledge from natural language documents is an interesting chore, since it obtains information in a simple manner, without the need of human interpretation, which often consumes large amounts of time. But for a computer to “understand” a document is a non-trivial task, since natural language is ambiguous, full of synonyms, idioms, anaphora, word declinations, analogies… which persons solve not only through context, but also with previous knowledge, real world experience and common sense. None of these are salient features of a computer.
To obtain knowledge automatically from any text (prose, poetry, news, event descriptions, text books, cooking recipes, descriptive documents, etc.) and to be able to transform it to a representation which a computer can understand and process, is still far from reality. Nevertheless, progress in this acquisition is performed with the use of natural language processing (NLP), information retrieval and knowledge acquisition tools.
Aware of the problem of trying to interpret all types of text, the scope of SERCDD (System for Extracting and Representing Knowledge from Descriptive Documents) is descriptive texts: documents describing tools, plants, geographic places, etc.