In this article we collect a corpus of texts which operate with a controlled language (ASD Simplified Technical English) in order to facilitate the development of a new domain-specific ontology (the aircraft structure) based on a technical discipline (aeronautical engineering) included in the so called "hard" sciences. This new repository should be compatible with the Core Ontology and the corresponding English Lexicon in FunGramKB (a multipurpose lexico-conceptual knowledge base for natural language processing (NLP)), and, in the same vein, should eventually give support to aircraft maintenance management systems. By contrast, in previous approaches we applied a stepwise methodology for the construction of a domain-specific subontology compatible with FunGramKB systems in criminal law, but the high occurrence of terminological banalisation and the scarce number of specific terms, due to the social nature of the discipline, were added problems to the most common NLP difficulties (polysemy and ambiguity). Taking into consideration previous results and the complexity of this task, here we only intend to take the first step towards the modelling of the aircraft ontology: the development of its taxonomic hierarchy. Consequently, the hierarchy starts with the whole system (i.e., an aircraft) and follows the traditional decomposition of the system down to the elementary components (top-down approach). At the same time,
Traditional corpus-based methods rely on manual inspection and extraction of lexical collocates in the study of selection preferences, which is a very costly, labor-intensive, and time-consuming task. Devising automatic methods for lexical collocate extraction becomes necessary to handle this task and the immensity of corpora available. With a view to leveraging the Sketch Engine platform and in-built corpora, we propose a working prototype of a Lexical Collocate Extractor (LeCoExt) command-line tool that mines lexical collocates from all types of verbs according to their syntactic constituents and Collocate Frequency Score (CFS). This might be the first tool that performs comprehensive corpus-based studies of the selection preferences of individual or groups of verbs exploiting the capabilities offered by Sketch Engine. This tool might facilitate the task of extracting rich lexico-semantic knowledge from diverse corpora in a few seconds and at a click away. We test its performance for ontology building and refinement departing from a previous detailed analysis of stealing verbs carried out by Fernández-Martínez & Faber (2020). We show how the proposed tool is used to extract conceptual-cognitive knowledge from the THEFT scenario and implement it into FunGramKB Core Ontology through the creation and modification of theft-related conceptual units.
FunGramKB (FGKB), on the one hand, is a multipurpose lexico-conceptual knowledge base for natural language processing (NLP) systems and comprises three major interrelated knowledge level modules: lexical, grammatical and conceptual. At the conceptual level the core ontology is presented as a hierarchical catalogue of the concepts that a person has in mind and a repository where semantic knowledge is stored. Axiology, on the other hand, is widely considered to be a primitive, basic or key parameter, among others, in the architecture of meaning construction at different levels. This parameter can be traced back to the three subontologies into which FunGramKB can be split: #ENTITY for nouns, # EVENT for verbs, and #QUALITY for adjectives. Even if most of the specific research conducted so far has been devoted to the category #QUALITY, there is no reason to consider verbs as less of an axiological category. Consequently, in this paper we shall concentrate on the subontology # EVENT and explore how the main categories and features of the axiological parameter (good-bad or positive-negative [+/-]) are represented and encoded within FunGramKB ontology, particularly inside semantic properties such as basic or terminal concepts and meaning postulates, or syntactic operators, such as modality or polarity.
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