Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases

Scientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying. Existing databases, on the other hand, are well structured for automated analysis, but do not contain comprehensive biological knowledge. We devised an approach for constructing comprehensive knowledge graphs from these two types of resources and applied it to investigate relationships between pre-/probiotics and microbiota-gut-brain axis diseases. To this end, we created (i) a knowledge base, dubbed ppstatement, containing manually curated detailed annotations, and (ii) a knowledge base, called ppconcept, containing automatically annotated concepts. The resulting Pre-/Probiotics Knowledge Graph (PPKG) combines these two knowledge bases with three other public databases (i.e. MeSH, UMLS and SNOMED CT). To validate the performance of PPKG and to demonstrate the added value of integrating two knowledge bases, we created four biological query cases. The query cases demonstrate that we can retrieve co-occurring concepts of interest, and also that combining the two knowledge bases leads to more comprehensive query results than utilizing them separately. The PPKG enables users to pose research queries such as which pre-/probiotics combinations may benefit depression?, potentially leading to novel biological insights.

Automated summary

Scientific publications provide biological relationships, but are not easy to integrate and query. Existing databases are well-structured for analysis, but lack comprehensive biological knowledge. We developed a method to construct a comprehensive knowledge graph by combining these two resources, and applied it to study the relationships between pre-/probiotics and microbiota-gut-brain axis diseases.