Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease

Computer Science > Artificial Intelligence [Submitted on 16 Mar 2026] Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease Giang Pham, Rebecca Finetti, Caterina Graziani, Bianca Roncaglia, Asma Bendjeddou, Linda Brodo, Sara Brunetti, Moreno Falaschi, Stefano Forti, Silvia Giulia Galfré, Paolo Milazzo, Corrado Priami, Annalisa Santucci, Ottavia Spiga, Alina Sîrbu Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders. Subjects: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Quantitative Methods (q-bio.QM) Cite as: arXiv:2603.15711 [cs.AI]   (or arXiv:2603.15711v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.15711 Focus to learn more Submission history From: Alina Sîrbu [view email] [v1] Mon, 16 Mar 2026 14:09:09 UTC (34,566 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.IR q-bio q-bio.QM References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)