Expanding a database-derived biomedical knowledge graph via multi-relation extraction from biomedical abstracts.

ABSTRACT:

Background

Knowledge graphs support biomedical research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via manual curation, which is challenging to scale with an exponentially rising publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to annotate textual data automatically. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This bottleneck makes populating a knowledge graph with multiple nodes and edg

SUBMITTER: Nicholson DN 

PROVIDER: S-EPMC9578183 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature