The paper presents an approach to ontology population as operations with Scott information sys tem. The deducibility relation in the ontology population information system corresponds to rules of input data processing and ontology population. To implement an ontology population process, we suggest a multi agent approach based on natural language semantic analysis. In the proposed multi agent model, agents of the following two types interact: information agents corresponding to meaningful units of the information being retrieved and rule agents implementing population rules of the given ontology based on the semanticsyntactic model of the language.
Abstract. We suggest an approach to the resolution of context-dependent lexical and syntactic ambiguity in a framework of ontology population from natural language texts. We show that a set of maximally determined ontology instances can be represented as a Scott information system with an entailment relation as a collection of information connections. Moreover, consistent primary lexical instances form FCA-concepts. These representations are used to justify the correctness of lexical disambiguation and to define syntactic ambiguity and its resolution. This information system generates a multi-agent system in which agents resolve the ambiguity of both types.
The work presented describes a combined approach to the partial extraction of the argumentative structure of a text that can be employed in the absence of sufficient annotated data to apply efficiently the machine learning methods for the direct detection of arguments and their relations. In this case, argument identification is performed by using the patterns of argumentation indicators created by a linguist and automatically expanded. These patterns enable the recognition of specific argument types with fine precision. In this study, arguments “from expert opinion” serve as such a pivot type. Besides, potential relations between recognized arguments are analyzed by dividing the text into superphrasal units (fragments united by one topic). The criterion for connecting arguments in an argumentative structure is their inclusion in the same superphrasal unit. An experiment for identifying potentially related arguments is conducted on a set of popular science texts with a minimum size of 1000 words.
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