WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
Table 1: The total number, average number of alias names, and supported languages per concept in UMLS, Wikipedia and WikiUMLS.
['Dataset', '#concepts', '#aliases', '#langs']
The major shortcoming of BERT and BioBERT is that they don’t encode multilingual aliases effectively, particularly if the scripts are different. This multilingual data can be utilised for triangulation of the matching pair. For example, GERD is a term used in UMLS for Gastroesophageal reflux disease, and is also a given name in many Germanic languages. If it is used for retrieval, many Wikipedia pages related to people with that name will also rank high. However, if alias names of GERD in other languages (e.g. Japanese) are used in the query and documents, the disambiguation becomes easier. To encode multilingual alias concept names we require a model that embeds tokens of different languages into the same embedding space. To this end, we also experiment with multilingual BERT (BERTmulti). We split the matching pairs into roughly 10k, 2k, and 5.8k for training, validation, and test set, respectively. There are about 3 million remaining UMLS concepts from various vocabularies that are not aligned to Wikipedia, which we hope to align in this work. Statistics of UMLS, Wikipedia, and the WikiUMLS