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        <title>T2Know: Analysis and Trend Platform Using the Knowledge Extracted from Scientific Texts</title>
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        <namePart type="given">Rafael</namePart>
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            <title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <namePart type="given">Marie</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>The T2Know project explores the application of natural language processing technologies to build a semantic platform for scientific documents using knowledge graphs. These graphs will interconnect meaningful sections from different documents, enabling both trend analysis and the generation of informed recommendations. The project’s objectives include the development of entity recognition systems, the definition of user and document profiles, and the linking of documents through transformer-based technologies. Consequently, the extracted relevant content will go beyond standard metadata such as titles and author affiliations, extending also to other key sections of scientific articles, including references, which are treated as integral components of the knowledge representation.</abstract>
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%0 Conference Proceedings
%T T2Know: Analysis and Trend Platform Using the Knowledge Extracted from Scientific Texts
%A Muñoz Guillena, Rafael
%A Palomar, Manuel
%A Gutiérrez, Yoan
%A Bonora, Mar
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F munoz-guillena-etal-2025-t2know
%X The T2Know project explores the application of natural language processing technologies to build a semantic platform for scientific documents using knowledge graphs. These graphs will interconnect meaningful sections from different documents, enabling both trend analysis and the generation of informed recommendations. The project’s objectives include the development of entity recognition systems, the definition of user and document profiles, and the linking of documents through transformer-based technologies. Consequently, the extracted relevant content will go beyond standard metadata such as titles and author affiliations, extending also to other key sections of scientific articles, including references, which are treated as integral components of the knowledge representation.
%U https://aclanthology.org/2025.ranlp-1.88/
%P 767-770

Markdown (Informal)

[T2Know: Analysis and Trend Platform Using the Knowledge Extracted from Scientific Texts](https://aclanthology.org/2025.ranlp-1.88/) (Muñoz Guillena et al., RANLP 2025)

• T2Know: Analysis and Trend Platform Using the Knowledge Extracted from Scientific Texts (Muñoz Guillena et al., RANLP 2025)

ACL

• Rafael Muñoz Guillena, Manuel Palomar, Yoan Gutiérrez, and Mar Bonora. 2025. T2Know: Analysis and Trend Platform Using the Knowledge Extracted from Scientific Texts. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 767–770, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.