In the context of using big data to improve healthcare and life sciences and with a specific focus on drug discovery, we consider the problem of formulating biomedical mechanism-of-action (MOA) hypotheses that can explain how specific drugs treat specific diseases. Our aim is to enable scalable mining and interpretation of MOA hypotheses enabling drug discovery and repurposing on large-scale biomedical knowledge graphs (KGs).The approach that we introduce to address this problem centers on expert-guided generation of candidate MOA hypotheses in the form of regular-path KG patterns between the KG nodes for the entities of interest, such as drugs and diseases. We call those patterns that represent promising candidate MOAs functional path patterns (FPPs), and call the proposed approach FPP-Hunter. The results of a drug-disease case study that we have conducted with the biomedical KG ROBOKOP suggest that the proposed approach has the potential to address scalability challenges in forming promising MOA hypotheses using large-scale KGs, in drug repurposing and potentially beyond.

Recently, knowledge graphs have seen a significant increase in popularity in a wide variety of domains, as they provide a basis for many data-analytics and knowledge-discovery approaches. At the same time, many knowledge graphs are not immediately usable due to their format, unreadable or missing data, and inaccessibility. These issues present barriers to the exploration and use of knowledge graphs for big data analytics and knowledge discovery. In this paper we present a workflow for domain- and task-sensitive curation of large-scale knowledge graphs, and detail our experience with implementing this workflow with the biomedical knowledge graph called Drug Repurposing Knowledge Graph (DRKG). The workflow aims to address usability-related issues of real-life knowledge graphs, by performing data setup and curation that align with the needs of specific tasks and domains. Recognizing that domain experts and anticipated users of a knowledge graph provide invaluable expertise regarding the desired graph format, the proposed workflow involves them as humans-in-the-loop. We present the processes required to execute the workflow, detail our experience in the biomedical domain with the use case of DRKG, and discuss the challenges and lessons learned throughout the experience. We anticipate that the proposed workflow and experiences will be applicable to other domains, and that our workflow will enable and encourage exploration and wider use of large-scale knowledge graphs, thereby improving big data analytics.

Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes.