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Название: Open Information Extraction as Additional Source for Kazakh Ontology Generation
Авторы: Khairova, N. F.
Petrasova, S. V.
Mamyrbayev, Orken
Mukhsina, Kuralay
Ключевые слова: open information extraction; RDF-triplets; unstructured text; logical-linguistic equations; Kazakh bilingual news websites
Дата публикации: 2020
Библиографическое описание: Open Information Extraction as Additional Source for Kazakh Ontology Generation [Electronic resource] / N. Khairova [et al.] // Intelligent Information and Database Systems (ACIIDS 2020) : proc. of the 12th Asian Conf., Phuket, Thailand, March 23-26, 2020. Pt. 1 / ed.: N. T. Nguyen [et al.]. – Electronic text data. – Cham, 2020. – P. 86-96. – (Ser. : Lecture Notes in Computer Science. Vol. 12033). – URL: https://link.springer.com/chapter/10.1007/978-3-030-41964-6_8, paid access (accessed 10.09.2022).
Краткий осмотр (реферат): Nowadays, structured information that obtains from unstructured texts and Web context can be applied as an additional source of knowledge to create ontologies. In order to extract information from a text and represent it in the RDF-triplets format, we suggest using the Open Information Extraction model. Then we consider the adaptation of the model to fact extraction from unstructured texts in the Kazakh language. In our approach, we identify lexical units that name the participants of the action (the Subject and Object) and semantic relations between them based on words characteristics in a sentence. The model provides semantic functions of the action participants via logical-linguistic equations that express the relations of the grammatical and semantic characteristics of the words in a Kazakh sentence. Using the tag names and some syntactic characteristics of words in the Kazakh sentences as the values of the predicate variables in corresponding equations allows us to extract Subjects, Objects and Predicates of facts from texts of Web content. The experimental research dataset includes texts extracted from Kazakh bilingual news websites. The experiment shows that we can achieve the precision of facts extraction over 71% for Kazakh corpus.
DOI: doi.org/10.1007/978-3-030-41964-6_8
URI (Унифицированный идентификатор ресурса): http://repository.kpi.kharkov.ua/handle/KhPI-Press/57930
Располагается в коллекциях:Кафедра "Інтелектуальні комп'ютерні системи"

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