Book chapter · Routledge eBooks · April 22, 2025

Existing problems of knowledge extraction from semi-structured and unstructured text

Orken J. Mamyrbayev, Nina Khairova, Waldemar Wójcik

Abstract

The efficiency of information extraction, processing, and storage largely depends on the degree of formalization and automation of information processing systems. Information can be categorized into structured, semi-structured, and unstructured types, with most of the data in global and corporate networks being semi-structured. Semi-structured text information, often found in Web pages and emails, presents significant challenges for analytical processing. Text Mining techniques, drawing from artificial intelligence and natural language processing (NLP), play a crucial role in extracting knowledge from such semi-structured texts by identifying syntactic and semantic relationships. This chapter examines the challenges associated with knowledge identification in natural language texts. In the context of semi-structured text information, knowledge identification refers to the extraction of information about entities and their relationships from natural language texts and its comparison with a predefined standard. This standard serves as an intellectual framework for understanding, enabling the identification of essential, general, and specific features and properties of the extracted knowledge, and aligning it with a known system class or object model.

Advanced Text Analysis TechniquesTopic ModelingNatural Language Processing Techniquesknowledge extractionsemi-structured textunstructured text

Existing problems of knowledge extraction from semi-structured and unstructured text

Abstract

More by Orken Mamyrbayev