Mathematical knowledge ontologies and recommender systems for collections of documents in physics and mathematics

Елизаров, А. М.; Жижченко, Алексей Борисович; Zhiltsov, Nikita; Kirillovich, Alexander; Lipachev, Evgeny K.

doi:10.1134/s1064562416020174

Cited by 28 publications

(11 citation statements)

References 10 publications

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“…In fact, the CDSSK consists of a set of subspaces related to various areas of science, built according to common principles. Despite the fact that there are some examples of formalization of knowledge in different subject areas [7,[8][9][10][11][12][13][14][15], there is no generalized approach to defining the digital space of scientific knowledge. An analysis of examples of the formalization of the knowledge space in various fields indicates that the main components of the DSSK in general and each of its subspaces in particular are ontology and its content.…”

Section: Components Of Cdsskmentioning

confidence: 99%

Ontological Approach to the Description of a Common Digital Space of Scientific Knowledge

Ataeva

Каленов

Serebryakov

2020

Preprint

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Despite the development of technical means, the processes associated with the search for complete and accurate scientific information in a huge number of data sources are becoming more complicated. To reach a new level in the use of information processing technologies, first of all, a transition to a semantically meaningful representation is necessary for scientific knowledge extracted from information in a digital environment. In modern conditions, characterized by multidisciplinary research, the desired effect can be achieved by developing universal approaches to the storage and presentation of scientific knowledge. These approaches are reflected in the concept of the Common Digital Space of Scientific Knowledge. The paper presents an overview of the basic concepts in this area, which are used both to represent the elements of space and to provide access to them not only for humans, but also for software agents. Semantic libraries are considered as tools for constructing the knowledge space.

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Section: Components Of Cdsskmentioning

confidence: 99%

Ontological Approach to the Description of a Common Digital Space of Scientific Knowledge

Ataeva

Каленов

Serebryakov

2020

Preprint

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“…Существует два основных типа рекомендательных систем: контент-ориентированные и социальные (коллаборативной фильтрации). В [29] приведен онтологический подход в рекомендательных системах для физико-математического контента.…”

Section: рекомендательные системы в научной работеunclassified

Recommender System in the Process of Scientific Peer Review in Mathematical Journal

Елизаров¹,

Lipachev²,

Khaydarov³

2020

RDLJ

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An approach is proposed for organizing expert evaluation of a scientific document submitted to a mathematical journal. Domain restriction is associated with the use of the Mathematical Sciences Classification System – MSC. A recommendation system is presented that allows you to create a list of possible experts for conducting scientific peer-reviewing on a mathematical article. The recommender system uses the MSC codes presented by the author of the article on the MSC2020 classifiers. If the codes MSC2000 or MSC2010 are indicated in the article, they are automatically converted to codes MSC2020. For each expert, the system supports a personal profile that contains a set of codes MSC2020, supplemented by numerical characteristics – weights calculated for each code in accordance with the system of accounting for competencies, preferences or refusals to participate in the review procedure. This set is automatically edited if the expert is included in the list of possible reviewers – the weights of several codes increase or decrease, as well as new codes are added. The recommendation system is implemented as an integrated tool (plug-in) of the Open Journal Systems (OJS) platform. The developed method has been tested in the information system of the Lobachevskii Journal of Mathematics (https://ljm.kpfu.ru).

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“…This contrast the main limitation of the adoption of knowledge-based measures, namely their strong dependence on the availability of an ontology [1]. The development of recommender systems [2] and adaptive learning systems [3], based on semantic relations defined in domain ontologies, highlights the necessity of making mathematics more computable and its representation more robust and standardized. Nevertheless, the community of experts in semantic language design and markup for mathematics doesn't agree on how to create a semantic language for mathematics, despite the common target of achieving a Global Digital Mathematics Library [4].…”

Section: State Of the Artmentioning

confidence: 99%

Towards Adaptive Systems for Automatic Formative Assessment in Virtual Learning Communities

Marchisio

Fioravera

et al. 2018

2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)

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This paper presents a model for structuring shared resources, proposed to activate learners' formative and proactive assessment processes by enhancing the sharing-workflow among instructors. The model is based on the features of a Virtual Learning Community (VLC), and provides for the development and reuse of ontologies. The adoption of the model for the implementation of an adaptive system for the dispatching of materials is discussed on the basis of the results from clustering analyses. Semantic-similarity measures are compared by their application for clustering a collection of mathematical questions for automatic assessment, created by instructors within a national-wide VLC for Secondary Schools.

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Mathematical knowledge ontologies and recommender systems for collections of documents in physics and mathematics

Cited by 28 publications

References 10 publications

Ontological Approach to the Description of a Common Digital Space of Scientific Knowledge

Ontological Approach to the Description of a Common Digital Space of Scientific Knowledge

Recommender System in the Process of Scientific Peer Review in Mathematical Journal

Towards Adaptive Systems for Automatic Formative Assessment in Virtual Learning Communities

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