Research Trending Topic Prediction as Cognitive Enhancement

Charnine, Michael; Klokov, Alexey; Kochiev, Leon; Tishchenko, A. G.

doi:10.1109/cw52790.2021.00044

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Some effective prediction methods based on machine learning algorithms have been proposed in the field of research topic trend. Charnine et al employ the CatBoost method to realize long-term prediction of research trending topics [14]. To determine persistent and emerging research topics in scientific literature, Balili et al carefully design a framework that leverages SVM, XGBoost, and Logistic Regression [7].…”

Section: Analysis Of Research Topic Trendmentioning

confidence: 99%

“…Taheri et al [8] utilize fields of study from the Microsoft Academic to predict the upcoming years' computer science trends based on LSTM. However, most existing studies use title words to represent research topics [9,10,14,15,19], which is extremely limited and unconvincing. The reason is that the titles of many papers are very unconventional and even have little correlation with the research topic of the paper.…”

Section: Analysis Of Research Topic Trendmentioning

confidence: 99%

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When Research Topic Trend Prediction Meets Fact-Based Annotations

Wang

Chen

et al. 2022

Data Sci. Eng.

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The unprecedented growth of publications in many research domains brings the great convenience for tracing and analyzing the evolution and development of research topics. Despite the significant contributions made by existing studies, they usually extract topics from the titles of papers, instead of obtaining topics from the authoritative sessions provided by venues (e.g., AAAI, NeurIPS, and SIGMOD). To make up for the shortcoming of existing work, we develop a novel framework namely RTTP(Research Topic Trend Prediction). Specifically, the framework contains the following two components: (1) a topic alignment strategy called TAS is designed to obtain the detailed contents of research topics in each year, (2) an enhanced prediction network called EPN is designed to capture the research trend of known years for prediction. In addition, we construct two real-world datasets of specific research domains in computer science, i.e., database and data mining, computer architecture and parallel programming. The experimental results demonstrate that the problem is well solved and our solution outperforms the state-of-the-art methods.

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Section: Analysis Of Research Topic Trendmentioning

confidence: 99%

Section: Analysis Of Research Topic Trendmentioning

confidence: 99%

When Research Topic Trend Prediction Meets Fact-Based Annotations

Wang

Chen

et al. 2022

Data Sci. Eng.

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Natural Language Processing Tools for Predictive Modeling of Advanced Trends in Formal Ontologies in Biomedical Sciences

Charnine,

Kalinin

2024

SibScript

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Natural language processing methods can be used to predict advanced application trends in formal ontologies. Formal ontologies help to formalize the characteristics of objects in various domains. As a result, machine learning programs identify patterns and relationships between these characteristics. The article describes an experiment based on machine learning methods in combination with text search methods. It involves the CatBoost algorithm for predictive modeling and clustering of lexical items. The vector models of the corresponding items reflect a trend in a particular domain of knowledge; proximity between them was calculated based on the idea of semantic distance. The experiment revealed four advanced areas for formal ontologies, i.e., genotype – phenotype; personalization; clustering algorithms, and collaborative task management. Each area that represented the predictable trends of development in this particular domain was provided with keywords. The article also contains a review of most popular scientific articles on these trends.

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Research Trending Topic Prediction as Cognitive Enhancement

Cited by 2 publications

References 6 publications

When Research Topic Trend Prediction Meets Fact-Based Annotations

When Research Topic Trend Prediction Meets Fact-Based Annotations

Natural Language Processing Tools for Predictive Modeling of Advanced Trends in Formal Ontologies in Biomedical Sciences

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