Download PDFOpen PDF in browserAgentic Knowledge Graph Traversal in Protein-Protein Relation Grounding7 pages•Published: April 19, 2026AbstractAutomated semantic knowledge extraction from scientific literature promises to open vast quantities of scientific knowledge to formal analysis and computationally-driven discovery. In this work we investigate the promise of Large Language Model (LLM) agents in extracting structured knowledge from biomedical texts, specifically for grounding protein-protein interaction (PPI) relations to terms in the PSI-MI ontology of molecular interactions. While LLMs excel at summarization, they struggle to interface with structured knowledge representations. We equipped agents with various knowledge graph interaction strategies and measured their PPI grounding performance. Our central finding is that PageRank-guided traversal, a method rooted in graph topology, consistently outperforms embedding-based approaches such as retrieval augmented generation (RAG) and top-down traversal strategies including breadth-first search (BFS), depth-first search (DFS), and local greedy search in extracting knowledge previously missed by human curators. Our initial results indicate that the structure of a well-curated knowledge base is itself a powerful source of information, an underutilized principle in current agentic knowledge base interaction methods.Keyphrases: agentic ai, ai for science, biology, knowledge representation, natural language processing, ontologies, relation extraction, research process automation, semantic web In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 50-56.
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