From the 4,300 abstracts, there were 1,188, 1,309, 822, 322, and 40 unique entities (concepts) for Disease, Chemical, Gene, Species, and SNP&Mutation, respectively. These biomedical concepts form 21,521, 8,048, 5,042, and 161 unique relationships: Disease-Chemical, Disease-Gene, Disease-species, and Disease-SNP&Mutation respectively. The most frequent Disease-Concept pairs can be seen in Table 1. We noticed that polyphenols, which are usually found in fruits and vegetables, have high co-existence with multiple neurodegenerative diseases. Polyphenols are well known for their function to reduce the risk of neurodegenerative disease [8].

To date, there are no effective treatments for most neurodegenerative diseases. However, certain foods may be associated with these diseases and bring an opportunity to prevent or delay neurodegenerative progression. Our objective is to construct a knowledge graph for neurodegenerative diseases using literature mining to study their relations with diet. We collected biomedical annotations (Disease, Chemical, Gene, Species, SNP&Mutation) in the abstracts from 4,300 publications relevant to both neurodegenerative diseases and diet using PubTator, an NIH-supported tool that can extract biomedical concepts from literature. A knowledge graph was created from these annotations. Graph embeddings were then trained with the node2vec algorithm to support potential concept clustering and similar concept identification. We found several food-related species and chemicals that might come from diet and have an impact on neurodegenerative diseases.