The aim of Natural Language Processing is to create computational systems for the production and comprehension of language by machines. In this regard, symbolic approaches to language put forth conceptual models which represent both common and specialised knowledge. This paper describes the ontological modelling of the “collective criminal agent” and its implementation in FunGramKB, a knowledge base for language processing and artificial reasoning. More specifically, the study focuses on the conceptual definition of three terminological units from the domains of terrorism and organised crime: cartel, oriented cluster, and terrorist cell. The main assumption is that ontological modelling applied to language technologies can play a major role in combating a variety of security threats to today’s society.
Speakers do not always attribute agency straightforwardly when they communicate. While complying with the maxims of explicitness and relevance, they may depict states of affairs headed by an identifiable source. More often than not, however, it seems they leave out this source for a number of reasons and through different mechanisms. This paper is a corpus-based study of one such non-identifying structures, namely the extrapositional have-it-that construction, in examples such as Several hypotheses have it that land-use changes. Drawing on data from the BNC, this paper investigates the use, distribution and functioning of the have-it-that construction. The paper also highlights the usefulness of simple collexeme analysis in revealing systematic co-selection relationships within the construction.
ste artículo describe las bases metodológicas para la construcción de subontologías terminológicas en la base de conocimiento FunGramKB y se parte de la hipótesis de que el modelo multinivel de la Ontología nuclear (nivel metaconceptual, nivel básico y nivel terminal) puede conectarse a una subontología con el fin de minimizar la redundancia informativa y maximizar el conocimiento. en el marco de FunGramKB, las subontologías se caracterizan, en primer lugar, por contener conceptos pertenecientes a dominios de conocimiento especializado y, en segundo lugar, por orientar esos conceptos a la semántica profunda –a diferencia del enfoque de la gran mayoría de las ontologías terminológicas, que suelen orientarse a la semántica superficial. el desarrollo de las subontologías permitirá la aplicación futura de FunGramKB a tareas de procesamiento de lenguaje especializado y razonamiento experto.
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