BioKGrapher: Initial evaluation of automated knowledge graph construction from biomedical literature

Background The growth of biomedical literature presents challenges in extracting and structuring knowledge. Knowledge Graphs (KGs) offer a solution by representing relationships between biomedical entities. However, manual construction of KGs is labor-intensive and time-consuming, highlighting the need for automated methods. This work introduces BioKGrapher, a tool for automatic KG construction using large-scale publication data, with a focus on biomedical concepts related to specific medical conditions. BioKGrapher allows researchers to construct KGs from PubMed IDs. Methods The BioKGrapher pipeline begins with Named Entity Recognition and Linking (NER+NEL) to extract and normalize biomedical concepts from PubMed, mapping them to the Unified Medical Language System (UMLS). Extracted concepts are weighted and re-ranked using Kullback-Leibler divergence and local frequency balancing. These concepts are then integrated into hierarchical KGs, with relationships formed using terminologies like SNOMED CT and NCIt. Downstream applications include multi-label document classification using Adapter-infused Transformer models. Results BioKGrapher effectively aligns generated concepts with clinical practice guidelines from the German Guideline Program in Oncology (GGPO), achieving F1-Scores of up to 0.6. In multi-label classification, Adapter-infused models using a BioKGrapher cancer-specific KG improved micro F1-Scores by up to 0.89 percentage points over a non-specific KG and 2.16 points over base models across three BERT variants. The drug-disease extraction case study identified indications for Nivolumab and Rituximab. Conclusion BioKGrapher is a tool for automatic KG construction, aligning with the GGPO and enhancing downstream task performance. It offers a scalable solution for managing biomedical knowledge, with potential applications in literature recommendation, decision support, and drug repurposing. •Automated knowledge graph construction from PubMed abstracts for conditions.•Achieves up to 0.6 F1-Score alignment of concepts with oncology clinical practice guidelines.•Improves cancer document classification using Adapter-infused Transformer models.•Automatically retrieve Drug-Disease associations for potential drug repurposing applications.