Knowledge Repository of Ontology Learning Tools from Text
Author(s) -
Agnieszka Konys
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.09.332
Subject(s) - computer science , ontology , ontology learning , upper ontology , knowledge representation and reasoning , ontology based data integration , information retrieval , interoperability , semantics (computer science) , suggested upper merged ontology , information extraction , open biomedical ontologies , process ontology , semantic web , artificial intelligence , natural language processing , world wide web , programming language , philosophy , epistemology
Ontologies are one of the fundamental elements of the Semantic Web, and they have gained a lot of popularity and recognition because they are viewed as the answer to the need for interoperable semantics in modern information systems. The intermingling of techniques in areas such as natural language processing, information retrieval, machine learning, data mining, and knowledge representation provide a lot of possibilities for development of ontology learning approaches. A rise in focus on the ability to cope with the scale of Web data required for ontology learning forces the potential growth of cross-language research, emphasizing the automatic or semi-automatic generation of the tools dedicated to text mining and information extraction. This paper presents the integration of ontology learning tools from text in the knowledge repository to incorporate the applied techniques and outputs of an ontology learning algorithm into the one complex multifunctional solution. The proposed knowledge repository covers various applicability of existing techniques of learning ontologies from text, and offers competency question-based reasoning mechanism for individuals to specify their profiles of ontology learning tools. The validation stage is also provided in the form of applied reasoning.
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