Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery
A novel literature-based discovery system based on UMLS Ontologies, Semantic Filters, Statistics, and Word Embeddings was developed and validated against the well-established Raynaud’s disease – Fish Oil discovery by mining different size and specificity corpora of Pubmed titles and abstracts. Results show an ‘inverse effect’ between open versus closed discovery search modes. In open discovery, a more general and bigger corpus (Vascular disease or Perivascular disease) produces better results than a more specific and smaller in size corpus (Raynaud disease), whereas in closed discovery, the exact opposite is true
