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

• URL: http://arxiv.org/abs/2602.04630v1
• Date: Wed, 04 Feb 2026 15:02:32 GMT
• Title: Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings
• Authors: Tim Kunt, Annika Buchholz, Imene Khebouri, Thorsten Koch, Ida Litzel, Thi Huong Vu,
• Abstract summary: Large text data sets inherit two types of features: the text itself, and its information conveyed through semantics, and its relationship to other texts through links, references, or shared attributes.<n>We propose a new embedding method for the Web of Science dataset, containing 56 million scientific publications through the lens of our proposed embedding method.
• Score: 0.722741581069214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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