Knowledge representation involves the modeling of systems that use artificial intelligence to process information. The form it takes basically depends on the task for which the knowledge in question is required. Therefore, the type and quantity of knowledge to perform a task are taken into consideration, as is the approach adopted to code and store it.

The TALP basically works on the acquisition of linguistic knowledge, particularly of lexical and semantic knowledge, for the enrichment of ontologies useful in NLP tasks and applications such as the disambiguation of meaning, machine translation and answers to questions.

Large-scale lexical-semantic ontologies, rule systems and computational lexicons with different content and targets (verb diathesis models, total and partial grammars, selectional restrictions, etc.) are the most commonly used structures.


A very active current line of research is the building and expansion of these ontologies using automatic and semiautomatic media: the syntactic and semantic analysis of large quantities of texts makes it possible to learn and acquire new concepts and to create new relationships between them, which then go on to form part of the knowledge stored in the ontology.

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