Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

Abstract

Knowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an increasing 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 automatically annotate textual data. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This makes populating a knowledge graph with multiple nodes and edge types practically infeasible. We sought to accelerate the label function creation process by evaluating the extent to which label functions could be re-used across multiple edge types. We used a subset of an existing knowledge graph centered on disease, compound, and gene entities to evaluate label function re-use. We determined the best label function combination by comparing a baseline database-only model with the same model but added edge-specific or edge-mismatch label functions. We confirmed that adding additional edge-specific rather than edge-mismatch label functions often improves text annotation and shows that this approach can incorporate novel edges into our source knowledge graph. We expect that continued development of this strategy has the potential to swiftly populate knowledge graphs with new discoveries, ensuring that these resources include cutting-edge results.

Topological network measures for drug repositioning

“…Most methods that were used so far had been for graphs homogenous edge types, however, recent efforts have been focused on analyzing multi-edged graphs, or ‘multi-graphs.’ For example, a multi-edged graph arose from imaging and cognitive data in a study of autism and schizophrenia, on which unsupervised clustering was performed to obtain subpopulations [ 30 ]. Extraction of data from biomedical literature yields multi-edged graphs or ‘Knowledge graphs’ [ 31 , 32 ]. Increasing attention to these multigraphs will bring in better insights into the interplay between various interacting layers we encounter in biology.…”

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

“…There are numerous studies in the literature that aimed to integrate the available biomedical data [1][2][3][4][5][6][7][8][9][10] . These studies provided useful tools and methods to the life-sciences research community; however, many of them miss important functionalities that prevent them from becoming widely adopted tools/services (Supplementary Information section 1).…”