WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking

Proceedings of the 28th International Conference on Computational Linguistics, pages 5957–5962 Barcelona, Spain (Online), December 8-13, 2020 5957 WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking Afshin Rahimi School of ITEE The University of Queensland Brisbane, Australia a.rahimi@uq.edu.au Timothy Baldwin Karin Verspoor School of Computing and Information Systems The University of Melbourne Melbourne, Australia tbaldwin@unimelb.edu.au karin.verspoor@unimelb.edu.au Abstract We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1 of 72%, a substantial improvement of 20% over word- and char-level BM25, enabling manual alignment with minimal effort. We release our resources, including ranked Wikipedia pages for 700k UMLS concepts, and WikiUMLS, a dataset for training and evaluation of alignment models between UMLS and Wikipedia collected from Wikidata. This will provide easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings.1 1 Introduction The Unified Medical Language System (UMLS)2 is a controlled vocabulary resource, enabling standard- isation of biomedical terminology and interoperability of electronic health systems across the world (Bo- denreider, 2004). UMLS has good coverage in only a handful languages such as English, impeding its uptake in health systems in different language settings (Mark´o et al., 2006). In addition, concept defini- tions in UMLS are either missing or very short, limiting its use as a medical encyclopedia. Wikipedia, on the other hand, is a crowd-sourced encyclopedia that is a primary source of online medical knowledge for practitioners, students, and the general public (Heilman et al., 2011; Heilman and West, 2015; Shafee et al., 2017; Murray, 2019). Wikipedia hosts an increasing number of health-related articles (Matheson- Monnet and Matheson, 2017). Wikipedia articles exist in 100s of languages, far exceeding the multilin- gual support in UMLS. Together, these characteristics point to the value of Wikipedia for patient-facing health information. Our goal in this work is to align UMLS concepts to their corresponding Wikidata and through that to their available Wikipedia concepts, to expand the language support for UMLS terminology with little effort. This will have a direct impact on patients worldwide by facilitating adoption of UMLS (includ- ing clinical terminology SNOMED-CT (Donnelly, 2006) or Medical Subject Headings, MeSH (Aronson and Lang, 2010)) in international healthcare systems, and also supporting medical information seeking needs of patients with varying linguistic backgrounds. For example, a patient whose native language is not English might receive a discharge summary in English with mentions of symptoms, diagnoses, or medications in formal clinical language. This language may not match Wikipedia titles exactly, making search hard (and noisy). Aligning UMLS and Wikipedia facilitates such information seeking, makes medical information accessible in a patient’s native language, and supports the need for patient access to information in consumer-friendlier terms (Zeng et al., 2001; Smith and Stavri, 2005). Most common medical terms are covered in Wikipedia; for instance, it is estimated that 80% of SNOMED-CT terms have …

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