74 citations · 145 across the 8 of their papers we have counts for
8 papers
Separating Semantic Expansion from Linear Geometry for PubMed-Scale Vector Search
Rob Koopman
We describe a PubMed scale retrieval framework that separates semantic interpretation from metric geometry. A large language model expands a natural language query into concise bio…
Contextualization of topics: Browsing through the universe of bibliographic information
Rob Koopman, Shenghui Wang, Andrea Scharnhorst
This paper describes how semantic indexing can help to generate a contextual overview of topics and visually compare clusters of articles. The method was originally developed for a…
Mutual Information based labelling and comparing clusters
Rob Koopman, Shenghui Wang
After a clustering solution is generated automatically, labelling these clusters becomes important to help understanding the results. In this paper, we propose to use a Mutual Info…
Clustering articles based on semantic similarity
Shenghui Wang, Rob Koopman
Document clustering is generally the first step for topic identification. Since many clustering methods operate on the similarities between documents, it is important to build repr…
Bibliometrics and Information Retrieval: Creating Knowledge through Research Synergies
Judit Bar-Ilan, Rob Koopman, Shenghui Wang +4
This panel brings together experts in bibliometrics and information retrieval to discuss how each of these two important areas of information science can help to inform the researc…
Contextualization of topics - browsing through terms, authors, journals and cluster allocations
Rob Koopman, Shenghui Wang, Andrea Scharnhorst
This paper builds on an innovative Information Retrieval tool, Ariadne. The tool has been developed as an interactive network visualization and browsing tool for large-scale biblio…