Fact extraction from natural language texts with conceptual modeling

Abstract

The paper presents the application of Formal Concept Analysis paradigm to the fact extraction problem on natural language texts. Proposed technique combines the usage of two conceptual models: conceptual graphs and concept lattice. Conceptual graphs serve as semantic models of text sentences and the data source for concept lattice – the basic conceptual model in the Formal Concept Analysis. With the use of concept lattice it is possible to model relationships between words from different sentences from different texts. These relationships have been collected in formal concepts of concept lattice and provide interpreting formal concepts as possible facts. Facts can be extracted by using navigation in the lattice and interpretation its concepts and hierarchical links between them. Experimental investigation of the proposed technique is performed on the annotated textual corpus consisted of descriptions of biotopes of bacteria.