Dynamics of topic formation and quantitative analysis of hot trends in physical science

Chumachenko, A. V.; Kreminskyi, Borys; Mosenkis, Iu. L.; Yakimenko, A. I.

doi:10.1007/s11192-020-03610-6

Cited by 4 publications

(3 citation statements)

References 57 publications

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“…The evolution of TF distributions proved to be a rich source of information on scientific knowledge underlying structure. Together with the detection of basic concepts presented in this article, it opens vast possibilities to explore the related topic history, clustering and hierarchy detection that we have already addressed in Chumachenko et al’s [23] study. One of the challenges that lay ahead is to find a method to predict the dynamics of a topic and future values of the entropy for specific concepts.…”

Section: Discussionmentioning

confidence: 93%

“…Another problem affected by basic concepts is related to topic detection based on the co-occurrence of concepts in different documents [23]. For example, such widely used in high-energy physics concepts like ‘Mass’, ‘Energy’ or ‘Field’ that appeared almost in every document will eventually identify high-energy physics as a single topic.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical entropic analysis of scientific concepts

Chumachenko

Kreminskyi

Mosenkis

et al. 2020

Journal of Information Science

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In the present era of information, the problem of effective knowledge retrieval from a collection of scientific documents becomes especially important for continuous scientific progress. The information available in scientific publications traditionally consists of bibliometric metadata and its semantic component such as title, abstract and text. While the former having a machine-readable format usually used for knowledge mapping and pattern recognition, the latter designed for human interpretation and analysis. Only a few studies use full-text analysis, based on carefully selected scientific ontology, to map the actual structure of the scientific knowledge or uncover similarities between documents. Unfortunately, the presence of common (basic) concepts across semantically unrelated documents creates spurious connections between different topics. We revise the known method based on the entropic information-theoretic measure used for selecting basic concepts and propose to analyse the dynamics of Shannon entropy for more rigorous sorting of concepts by their generality.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Dynamical entropic analysis of scientific concepts

Chumachenko

Kreminskyi

Mosenkis

et al. 2020

Journal of Information Science

Self Cite

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“…2 The second strategy makes direct use of expert validation (as e.g. in Chumachenko et al, 2020;Haunschild et al, 2018), and the third strategy compares the results to other, 'trustful' classification systems such as PubMed's Medical Subject Headings, which are created by experts (e.g. Ahlgren et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Challenges to the validity of topic reconstruction

2021

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In this paper we utilize an opportunity to construct ground truths for topics in the field of atomic, molecular and optical physics. Our research questions in this paper focus on (i) how to construct a ground truth for topics and (ii) the suitability of common algorithms applied to bibliometric networks to reconstruct these topics. We use the ground truths to test two data models (direct citation and bibliographic coupling) with two algorithms (the Leiden algorithm and the Infomap algorithm). Our results are discomforting: none of the four combinations leads to a consistent reconstruction of the ground truths. No combination of data model and algorithm simultaneously reconstructs all micro-level topics at any resolution level. Meso-level topics are not reconstructed at all. This suggests (a) that we are currently unable to predict which combination of data model, algorithm and parameter setting will adequately reconstruct which (types of) topics, and (b) that a combination of several data models, algorithms and parameter settings appears to be necessary to reconstruct all or most topics in a set of papers.

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Thermodynamics of Scientific Ontology

Chumachenko,

Buttliere

2024

Preprint

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In this work, we propose a thermodynamic framework to analyze the creative potential of scientific fields by examining statistical data on the usage frequencies of scientific concepts within a corpus of publications from ArXiv. Using statistical mechanics and thermodynamics, we model the system of physical concepts that form the ontology of scientific field. We explore the relationship between Clausius entropy and Shannon entropy in this context, assuming the interaction of concepts through their pairwise mutual information. Our approach enables us to leverage methods from statistical physics to analyze information systems during knowledge production and transfer. We demonstrate that the coarse-grained frequencies of scientific concepts follow a generalized Boltzmann distribution, allowing for a thermodynamic description. This study calculates internal energy, Helmholtz free energy, temperature, and heat capacity for scientific concepts as closed thermodynamic systems, and maps the state space of the concepts-based knowledge network using data-driven thermodynamic diagrams. This framework advances the methods of computational theory of discovery by providing insights into the dynamics of scientific knowledge and the emergence of innovation.

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Dynamics of topic formation and quantitative analysis of hot trends in physical science

Cited by 4 publications

References 57 publications

Dynamical entropic analysis of scientific concepts

Dynamical entropic analysis of scientific concepts

Challenges to the validity of topic reconstruction

Thermodynamics of Scientific Ontology

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