Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery

Toby Reed, Vassilis Cutsuridis


Abstract
A novel literature-based discovery system based on UMLS Ontologies, Semantic Filters, Statistics, and Word Embed-dings was developed and validated against the well-established Raynaud’s disease – Fish Oil discovery by min-ing different size and specificity corpora of Pubmed titles and abstracts. Results show an ‘inverse effect’ between open ver-sus closed discovery search modes. In open discovery, a more general and bigger corpus (Vascular disease or Peri-vascular disease) produces better results than a more specific and smaller in size corpus (Raynaud disease), whereas in closed discovery, the exact opposite is true.
Anthology ID:
2020.icon-demos.1
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON): System Demonstrations
Month:
DECEMBER
Year:
2020
Address:
Patna, India
Editors:
Vishal Goyal, Asif Ekbal
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
1–3
Language:
URL:
https://aclanthology.org/2020.icon-demos.1
DOI:
Bibkey:
Cite (ACL):
Toby Reed and Vassilis Cutsuridis. 2020. Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery. In Proceedings of the 17th International Conference on Natural Language Processing (ICON): System Demonstrations, pages 1–3, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Demonstration of a Literature Based Discovery System based on Ontologies, Semantic Filters and Word Embeddings for the Raynaud Disease-Fish Oil Rediscovery (Reed & Cutsuridis, ICON 2020)
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PDF:
https://aclanthology.org/2020.icon-demos.1.pdf