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bibtex [2019/03/22 13:51]
anton
bibtex [2019/03/22 13:53] (current)
anton
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  title = {Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research},  title = {Ontology-based approach to decision-making support of conceptual domain models creating and using in learning and scientific research},
  journal = {{IOP} Conference Series: Materials Science and Engineering},​  journal = {{IOP} Conference Series: Materials Science and Engineering},​
- thanks = {18-07-00032,​ 18-47-340014},​+ thanks = {rfbr-18-07-00032, ​rfbr-18-47-340014},​
  abstract = {The paper presents an approach to knowledge management in learning and scientific research that allows increasing the availability of expert knowledge and reducing semantic noise during knowledge transfer. The availability of expert knowledge is ensured by transforming them from implicit into explicit form (in the form of Semantic Link Network). The reduction of semantic noise is achieved through the integration of knowledge in different forms and their personalization for different groups of knowledge recipients. In the paper, the tasks of decision-making support are formulated which should be performed by the knowledge management system on the base of the proposed approach.}  abstract = {The paper presents an approach to knowledge management in learning and scientific research that allows increasing the availability of expert knowledge and reducing semantic noise during knowledge transfer. The availability of expert knowledge is ensured by transforming them from implicit into explicit form (in the form of Semantic Link Network). The reduction of semantic noise is achieved through the integration of knowledge in different forms and their personalization for different groups of knowledge recipients. In the paper, the tasks of decision-making support are formulated which should be performed by the knowledge management system on the base of the proposed approach.}
 } }
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  title = {Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task},  title = {Using online update of distributional semantics models for decision-making support for concepts extraction in the domain ontology learning task},
  journal = {{IOP} Conference Series: Materials Science and Engineering},​  journal = {{IOP} Conference Series: Materials Science and Engineering},​
- thanks = {18-07-00032},​+ thanks = {rfbr-18-07-00032},​
  abstract = {Most of the information processed by computer systems is presented in the form of text corpuses. The number of such texts (as well as the corpus as a whole) only increases with time, and therefore the word processing tasks remain relevant to this day. Ontology allows to describe semantics using domain concepts and relations between them [1, 2]. In the ontology learning task, the ontology is dependent on quality of corpus which may not be readily available. There are different approaches to creating ontologies (including the use of different tools and frameworks). This paper discusses the use of word2vec (group of related models that are used to produce word embeddings) using online vocabulary update and extension of the original data corpus with additional training for the domain concepts extraction to automate the domain ontology creation.}  abstract = {Most of the information processed by computer systems is presented in the form of text corpuses. The number of such texts (as well as the corpus as a whole) only increases with time, and therefore the word processing tasks remain relevant to this day. Ontology allows to describe semantics using domain concepts and relations between them [1, 2]. In the ontology learning task, the ontology is dependent on quality of corpus which may not be readily available. There are different approaches to creating ontologies (including the use of different tools and frameworks). This paper discusses the use of word2vec (group of related models that are used to produce word embeddings) using online vocabulary update and extension of the original data corpus with additional training for the domain concepts extraction to automate the domain ontology creation.}
 } }