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Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings

Please always quote using this URN: urn:nbn:de:0297-zib-100646
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  • Large text data sets, such as publications, websites, and other text-based media, inherit two distinct types of features: (1) the text itself, its information conveyed through semantics, and (2) its relationship to other texts through links, references, or shared attributes. While the latter can be described as a graph structure and can be handled by a range of established algorithms for classification and prediction, the former has recently gained new potential through the use of LLM embedding models. Demonstrating these possibilities and their practicability, we investigate the Web of Science dataset, containing ~56 million scientific publications through the lens of our proposed embedding method, revealing a self-structured landscape of texts.

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Author:Tim KuntORCiD, Annika BuchholzORCiD, Imene KhebouriORCiD, Thorsten KochORCiD, Ida LitzelORCiD, Thi Huong VuORCiD
Document Type:In Proceedings
Parent Title (German):Operations Research Proceedings 2025. OR 2025
Series:Lecture Notes in Operations Research
Date of first Publication:2025/07/22
Series (Serial Number):ZIB-Report (25-11)
ISSN:1438-0064
DOI:https://doi.org/10.12752/10064
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