{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/embedding-biomedical-ontologies-by-jointly","title":"Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors","arxiv_id":"1906.05939","date":"2019-06-13","proceeding":"WS 2019 8","authors":["Sotiris Kotitsas","Dimitris Pappas","Ion Androutsopoulos","Ryan Mcdonald","Marianna Apidianaki"],"abstract":"Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.","url_abs":"https://arxiv.org/abs/1906.05939v2","url_pdf":"https://arxiv.org/pdf/1906.05939v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"embedding-biomedical-ontologies-by-jointly","repo_url":"https://github.com/SotirisKot/Content-Aware-N2V","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"network-embedding","task_name":"Network Embedding"}],"methods":[],"datasets_introduced":[{"slug":"is-a","name":"IS-A","full_name":null},{"slug":"part-of","name":"PART-OF","full_name":null}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1906.05939","atlas_url":"https://app.syntology.ai/?focus=1906.05939","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}