VectorSage: enhancing PubMed article retrieval with advanced semantic search

The exponential growth of academic literature has presented unprecedented opportunities. However, it also underscores the need for advanced search methodologies to support efficient knowledge discovery. While effective for structured queries, traditional keyword-based search engines often struggle with the inherent variability of language, where the same concept can be expressed in many ways, leading to incomplete or imprecise retrieval of relevant research. Another issue that must be considered is that of lexical ambiguity, such as polysemy or homonymy, whereby several words and abbreviations can have multiple meanings. This results in items placed in the results list that are irrelevant to the search context. Recent advances in natural language processing have enabled semantic similarity techniques that move beyond basic text matching toward context-aware search.

We developed VectorSage (https://vectorsage.nube.uni-greifswald.de), an advanced biomedical search system for retrieving PubMed abstracts using a hybrid approach that combines term relevance scoring with embedding-based semantic similarity. VectorSage employs a global ranking mechanism to enhance further search relevance by sorting the retrieved documents, ensuring a balance between semantic relevance and keyword specificity. This method enables efficient literature exploration and knowledge discovery.