FACT EXTRACTION FROM NATURAL LANGUAGE TEXTS WTH CONCEPTUAL GRAPH MODELS
Applications of methods of conceptual modeling in the fact extraction technology from textual data are considered. Textual data is presented as non-structured natural language texts, forming a text corpus. Conceptual graphs and concept lattices are used as conceptual models. The use of conceptual graphs allows to solve effectively the problems of named entity recognition and relations extraction. These solutions then applied for building concept lattices which serve as a data source in the fact extraction system. Extraction of facts is doing by processing user requests and finding the corresponding concepts in the concept lattice.