TY - CHAP A1 - Kunt, Tim A1 - Buchholz, Annika A1 - Khebouri, Imene A1 - Koch, Thorsten A1 - Litzel, Ida A1 - Vu, Thi Huong T1 - Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings T2 - Operations Research Proceedings 2025. OR 2025 N2 - 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. T3 - ZIB-Report - 25-11 Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-100646 SN - 1438-0064 ER -