BIOS: An Algorithmically Generated Biomedical Knowledge Graph

by   Sheng Yu, et al.

Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for biomedical and healthcare big data and artificial intelligence (AI), facilitating natural language processing, model development, and data exchange. For many decades, these knowledge graphs have been built via expert curation, which can no longer catch up with the speed of today's AI development, and a transition to algorithmically generated BioMedKGs is necessary. In this work, we introduce the Biomedical Informatics Ontology System (BIOS), the first large scale publicly available BioMedKG that is fully generated by machine learning algorithms. BIOS currently contains 4.1 million concepts, 7.4 million terms in two languages, and 7.3 million relation triplets. We introduce the methodology for developing BIOS, which covers curation of raw biomedical terms, computationally identifying synonymous terms and aggregating them to create concept nodes, semantic type classification of the concepts, relation identification, and biomedical machine translation. We provide statistics about the current content of BIOS and perform preliminary assessment for term quality, synonym grouping, and relation extraction. Results suggest that machine learning-based BioMedKG development is a totally viable solution for replacing traditional expert curation.



page 4


Multimodal Learning on Graphs for Disease Relation Extraction

Objective: Disease knowledge graphs are a way to connect, organize, and ...

Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings

Much of biomedical and healthcare data is encoded in discrete, symbolic ...

Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations

Term clustering is important in biomedical knowledge graph construction....

Relationship extraction for knowledge graph creation from biomedical literature

Biomedical research is growing in such an exponential pace that scientis...

Epistemic AI platform accelerates innovation by connecting biomedical knowledge

Epistemic AI accelerates biomedical discovery by finding hidden connecti...

RDF Knowledge Graph Visualization From a Knowledge Extraction System

In this paper, we present a system to visualize RDF knowledge graphs. Th...

Prediction of concept lengths for fast concept learning in description logics

Concept learning approaches based on refinement operators explore partia...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.