Abstract
Immense numbers of textual documents are available in a digital form. Research activities are focused on methods of how to speed up their processing to avoid information overloading or to provide formal structures for the problem solving or decision making of intelligent agents. Ontology learning is one of the directions which contributes to all of these activities. The main aim of the ontology learning is to semi-automatically, or fully automatically, extract ontologies—formal structures able to express information or knowledge. The primary motivation behind this paper is to facilitate the processing of a large collection of papers focused on disaster management, especially on tsunami research, using the ontology learning. Various tools of ontology learning are mentioned in the literature at present. The main aim of the paper is to uncover these tools, i.e., to find out which of these tools can be practically used for ontology learning in the tsunami application domain. Specific criteria are predefined for their evaluation, with respect to the “Ontology learning layer cake”, which introduces the fundamental phases of ontology learning. ScienceDirect and Web of Science scientific databases are explored, and various solutions for semantics extraction are manually “mined” from the journal articles. ProgrammableWeb site is used for exploration of the tools, frameworks, or APIs applied for the same purpose. Statistics answer the question of which tools are mostly mentioned in these journal articles and on the website. These tools are then investigated more thoroughly, and conclusions about their usage are made with respect to the tsunami domain, for which the tools are tested. Results are not satisfactory because only a limited number of tools can be practically used for ontology learning at present.
Highlights
IntroductionValuable information and knowledge are spread across this extensive collection of texts
These three groups can be subdivided into more specific categories: research articles where a concrete tool is used in solving a specific problem, research articles where a concrete method or technique is used, articles providing a review of tools or review of methods for semantics extraction, comparative studies, and other(s) articles mainly presenting challenges or future perspectives related with the processing of big data
This paper aims to answer the question of which tool, applicable to ontology learning or knowledge extraction, can be practically used at present with respect to the predefined requirements
Summary
Valuable information and knowledge are spread across this extensive collection of texts. In a general point of view, techniques of linguistics, statistics, and artificial intelligence are used for semi-automatic or fully automatic extraction of information and knowledge from structured (spreadsheets-like), semi-structured (markup-like, JSON-like), or unstructured (plain texts, PDF or Word files) collections of data. These techniques are cited in connection with the multidisciplinary research area called text mining. As a subarea of the artificial intelligence, combines techniques and methods of data mining, machine learning, library and information sciences,
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