{"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/neuro-symbolic-representation-learning-on","title":"Neuro-symbolic representation learning on biological knowledge graphs","arxiv_id":"1612.04256","date":"2016-12-13","proceeding":null,"authors":["Mona Alshahrani","Mohammed Asif Khan","Omar Maddouri","Akira R Kinjo","Núria Queralt-Rosinach","Robert Hoehndorf"],"abstract":"Motivation: Biological data and knowledge bases increasingly rely on Semantic\nWeb technologies and the use of knowledge graphs for data integration,\nretrieval and federated queries. In the past years, feature learning methods\nthat are applicable to graph-structured data are becoming available, but have\nnot yet widely been applied and evaluated on structured biological knowledge.\nResults: We develop a novel method for feature learning on biological knowledge\ngraphs. Our method combines symbolic methods, in particular knowledge\nrepresentation using symbolic logic and automated reasoning, with neural\nnetworks to generate embeddings of nodes that encode for related information\nwithin knowledge graphs. Through the use of symbolic logic, these embeddings\ncontain both explicit and implicit information. We apply these embeddings to\nthe prediction of edges in the knowledge graph representing problems of\nfunction prediction, finding candidate genes of diseases, protein-protein\ninteractions, or drug target relations, and demonstrate performance that\nmatches and sometimes outperforms traditional approaches based on manually\ncrafted features. Our method can be applied to any biological knowledge graph,\nand will thereby open up the increasing amount of Semantic Web based knowledge\nbases in biology to use in machine learning and data analytics. Availability\nand Implementation:\nhttps://github.com/bio-ontology-research-group/walking-rdf-and-owl Contact:\nrobert.hoehndorf@kaust.edu.sa","url_abs":"http://arxiv.org/abs/1612.04256v1","url_pdf":"http://arxiv.org/pdf/1612.04256v1.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":"neuro-symbolic-representation-learning-on","repo_url":"https://github.com/bio-ontology-research-group/walking-rdf-and-owl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}