{"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/el-embeddings-geometric-construction-of","title":"EL Embeddings: Geometric construction of models for the Description Logic EL ++","arxiv_id":"1902.10499","date":"2019-02-27","proceeding":null,"authors":["Maxat Kulmanov","Wang Liu-Wei","Yuan Yan","Robert Hoehndorf"],"abstract":"An embedding is a function that maps entities from one algebraic structure\ninto another while preserving certain characteristics. Embeddings are being\nused successfully for mapping relational data or text into vector spaces where\nthey can be used for machine learning, similarity search, or similar tasks. We\naddress the problem of finding vector space embeddings for theories in the\nDescription Logic $\\mathcal{EL}^{++}$ that are also models of the TBox. To find\nsuch embeddings, we define an optimization problem that characterizes the\nmodel-theoretic semantics of the operators in $\\mathcal{EL}^{++}$ within\n$\\Re^n$, thereby solving the problem of finding an interpretation function for\nan $\\mathcal{EL}^{++}$ theory given a particular domain $\\Delta$. Our approach\nis mainly relevant to large $\\mathcal{EL}^{++}$ theories and knowledge bases\nsuch as the ontologies and knowledge graphs used in the life sciences. We\ndemonstrate that our method can be used for improved prediction of\nprotein--protein interactions when compared to semantic similarity measures or\nknowledge graph embedding","url_abs":"http://arxiv.org/abs/1902.10499v1","url_pdf":"http://arxiv.org/pdf/1902.10499v1.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":"el-embeddings-geometric-construction-of","repo_url":"https://github.com/bio-ontology-research-group/EL2Box_embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"knowledge-graph-embedding","task_name":"Knowledge Graph Embedding"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.10499","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}