{"url":"/dataset/umls","name":"UMLS","full_name":"Unified Medical Language System","description_markdown":"The **Unified Medical Language System (UMLS)** is a comprehensive resource that integrates and disseminates essential terminology, classification standards, and coding systems. Its purpose is to foster the creation of more effective and interoperable biomedical information systems and services, including electronic health records. Here are the key aspects of the UMLS:\r\n\r\n1. **Integration of Vocabularies**: The UMLS brings together various health and biomedical vocabularies and standards. It acts as a bridge, enabling interoperability between different computer systems by harmonizing terminology.\r\n\r\n2. **Components**:\r\n    - **Metathesaurus**: This component contains terms and codes from multiple vocabularies, including CPT, ICD-10-CM, LOINC, MeSH, RxNorm, and SNOMED CT. It provides hierarchies, definitions, relationships, and other attributes.\r\n    - **Semantic Network**: The Semantic Network defines broad categories (semantic types) and their relationships (semantic relations).\r\n    - **SPECIALIST Lexicon and Lexical Tools**: These tools include a large syntactic lexicon for biomedical and general English. They assist in normalizing strings, generating lexical variants, and creating indexes.\r\n\r\n3. **Use Cases**:\r\n    - **Clinical Practice**: Link terms and codes across healthcare entities (doctors, pharmacies, insurance companies).\r\n    - **Patient Care Coordination**: Facilitate communication among hospital departments.\r\n    - **Text Processing**: Extract concepts, relationships, or knowledge from medical texts.\r\n    - **Terminology Mapping**: Map between different terminologies.\r\n    - **Local Terminology Development**: Create and maintain local terminologies.\r\n    - **Research**: Investigate terminologies or ontologies.\r\n\r\n4. **Accessing the UMLS**:\r\n    - **Web Browsers**: You can search and explore UMLS data using UTS applications like the Metathesaurus Browser (retrieve concept information) and the Semantic Network Browser (view semantic types and relations).\r\n    - **Local Installation**: Download UMLS files and use the MetamorphoSys tool to customize the UMLS for your specific needs. Load the customized data into your own database system or browse it using the MetamorphoSys RRF browser.\r\n\r\nSource: Conversation with Bing, 3/18/2024\r\n(1) Unified Medical Language System (UMLS) - National Library of Medicine. https://www.nlm.nih.gov/research/umls/index.html.\r\n(2) UMLS Metathesaurus Browser. https://uts.nlm.nih.gov/uts/umls/home.\r\n(3) GitHub - dongwookim-ml/kg-data: knowledge-graph datasets. https://github.com/dongwookim-ml/kg-data.\r\n(4) UMLS Dataset | Papers With Code. https://paperswithcode.com/dataset/umls.","description_withheld":null,"homepage":"https://www.nlm.nih.gov/research/umls/quickstart.html","introduced_date":"2013-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/umlssimilarity-measuring-the-relatedness-and","title":"UMLS::Similarity: Measuring the Relatedness and Similarity of Biomedical Concepts","first_author":"Bridget McInnes","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["UMLS"],"data_loaders":[{"repo":"https://github.com/pykeen/pykeen","url":"https://pykeen.readthedocs.io/en/stable/api/pykeen.datasets.UMLS.html","frameworks":["pytorch"]}],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/link-prediction-on-umls","task":"Link Prediction","dataset_variant":"UMLS","rows":10,"metrics":["Hits@10","MR"],"first_row_in_archive_order":{"model":"LP-BERT","paper":"/paper/lp-bert-multi-task-pre-training-knowledge","metrics":{"Hits@10":"1.000","MR":"1.18"},"code_links":[{"title":"763337092/LP-BERT","url":"https://github.com/763337092/LP-BERT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/kglm-integrating-knowledge-graph-structure-in","title":"KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction","date":"2022-11-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/palt-parameter-lite-transfer-of-language","title":"PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion","date":"2022-10-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-language-semantic-and-structure","title":"Joint Language Semantic and Structure Embedding for Knowledge Graph Completion","date":"2022-09-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lp-bert-multi-task-pre-training-knowledge","title":"Multi-task Pre-training Language Model for Semantic Network Completion","date":"2022-01-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semantic-triple-encoder-for-fast-open-set","title":"Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion","date":"2020-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kg-bert-bert-for-knowledge-graph-completion","title":"KG-BERT: BERT for Knowledge Graph Completion","date":"2019-09-07","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/convolutional-2d-knowledge-graph-embeddings","title":"Convolutional 2D Knowledge Graph Embeddings","date":"2017-07-05","rows_on_this_dataset":1,"code_links":8,"syntology":null},{"paper":"/paper/complex-embeddings-for-simple-link-prediction","title":"Complex Embeddings for Simple Link Prediction","date":"2016-06-20","rows_on_this_dataset":1,"code_links":9,"syntology":null},{"paper":"/paper/embedding-entities-and-relations-for-learning","title":"Embedding Entities and Relations for Learning and Inference in Knowledge Bases","date":"2014-12-20","rows_on_this_dataset":1,"code_links":10,"syntology":null},{"paper":"/paper/translating-embeddings-for-modeling-multi","title":"Translating Embeddings for Modeling Multi-relational Data","date":"2013-12-01","rows_on_this_dataset":1,"code_links":8,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}