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.

Knowledge graphs (KGs), with their flexible and expressive data model, are frequently used for management of large-scale data and knowledge in data-intensive domains, including business, healthcare, and biomedicine. In particular, the KG data representation enables extraction from KGs of various knowledge and insights, with a number of applications to date. One type of knowledge that can be extracted from KGs is relational knowledge, which is expressed as paths between pairs of KG nodes and can provide real-world explanations for domain connections between the entities or concepts of interest. In this paper we focus on the problem of ranking path-based explanations for KG queries in ways that would rank higher the paths that make more sense in the user-provided semantic context, effectively and efficiently on large-scale KGs.Toward addressing the problem, we introduce an approach called Semantics-Aware Path Ranking Algorithm (SAPRA). The SAPRA approach is designed to scale to very large KGs. It is broadly applicable to KG data and queries in a range of domains, leveraging the properties of entities and relationships within the given KG to recommend paths that most closely align with the user-provided semantic context. To further enhance the accuracy of the semantic interpretation of the user queries, SAPRA can adapt its behavior based on feedback from domain experts. SAPRA accepts as inputs KG queries and the associated semantic context in their purely syntactic form, which makes the approach domain agnostic. We report the results of an experimental evaluation of our implementation of SAPRA on the biomedical KGs ROBOKOP and DRKG. The results show promise for better path-ranking effectiveness and efficiency of the proposed approach against the state of the art on large-scale KGs, in the biomedical domain and potentially beyond.

In large-scale multidisciplinary consortia endeavors that address problems of research, industry, and public-good significance, it is typically a priority to integrate the heterogeneous data contributed by the consortia participants into a unified data representation. Knowledge graphs (KGs) are a typical choice for the data model of the resulting data repositories. To overcome potential issues with terminology misalignment, consortia commonly dedicate resources to the development of shared languages (vocabularies), with the intent of enabling diverse participants to understand and build on each other’s work. Our research focus in this paper is on the challenge of automating integration into unified KGs of diverse data that potentially use different terminology, with the help of the available shared languages to resolve terminology clashes.To address the challenge, we introduce a data-integration workflow called INTEGRATE-KG that is domain agnostic, yet domain aware through opportunities for the involvement of humans-in-the-loop. A key feature of the approach is in its use of the synonyms available for the shared languages to automate semantics-level terminology alignment across the individual data contributions after they have been submitted for integration. INTEGRATE-KG also includes a module for automatically enriching the available shared languages, with opportunities for domain experts to provide semantic corrections and feedback. We present the workflow, report on our experiences with applying it to experimental, survey, and shared-language data on phosphorus sustainability, and provide suggestions for involving domain experts in INTEGRATE-KG as humans-in-the-loop.

Knowledge graphs (KGs), with their flexible encoding of heterogeneous data, have been increasingly used in a variety of applications. At the same time, domain data are routinely stored in formats such as spreadsheets, text, or figures. Storing such data in KGs can open the door to more complex types of analytics, which might not be supported by the data sources taken in isolation. Giving domain experts the option to use a predefined automated workflow for integrating heterogeneous data from multiple sources into a single unified KG could significantly alleviate their data-integration time and resource burden, while potentially resulting in higher-quality KG data capable of enabling meaningful rule mining and machine learning.In this paper we introduce a domain-agnostic workflow called BUILD-KG for integrating heterogeneous scientific and experimental data from multiple sources into a single unified KG potentially enabling richer analytics. BUILD-KG is broadly applicable, accepting input data in popular structured and unstructured formats. BUILD-KG is also designed to be carried out with end users as humans-in-the-loop, which makes it domain aware. We present the workflow, report on our experiences with applying it to scientific and experimental data in the materials science domain, and provide suggestions for involving domain scientists in BUILD-KG as humans-in-the-loop.

Knowledge graphs have been growing in popularity in recent years, due to their successful use in many data-analytics and knowledge-discovery tasks. As the sizes of domain graphs continue to grow, they can become too large to be processed efficiently by downstream applications. It is thus indispensable in many cases to aim to address the efficiency issue by reducing knowledge-graph sizes via summarization. At the same time, to provide quality results, that is, to be effective, on the graph summaries, many downstream analytics and knowledge-discovery tasks require the summaries to retain as much information from the original graph as possible. It turns out that state-of-the-art data-reduction approaches may generate task-specific or lossy summaries, which may present challenges for the effectiveness of downstream analytics in some cases. As a result, it is important to study trade-offs between the performance and accuracy of graph summarizations for analytics and knowledge discovery.In this paper, in the scope of knowledge-graph size reduction via summarization, we consider the trade-off between the performance and accuracy of one type of knowledge-graph analytics, inference-rule mining, on the summarized graphs as compared with the original graphs. Toward addressing the tradeoff challenge, we introduce a domain- and task-independent knowledge-graph summarization approach called GAME for generating reduced abstract knowledge graphs from a given graph. Our preliminary experimental results suggest that our GAME approach can preserve as much information from the given (original) graph as possible for the purpose of rule mining, while potentially significantly improving the rule-mining efficiency. We anticipate that the knowledge-graph reduction provided by the proposed GAME approach can enable efficient, yet effective, results for diverse applications and use cases.

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.

Drug discovery is an important process used by biomedical experts to identify potential treatments for diseases. In its traditional form, the process requires significant expert time and manual effort. By encoding a wealth of information about relationships between drugs and diseases, modern large-scale biomedical knowledge graphs provide excellent opportunities to accelerate drug discovery, by automating aspects of the process. One opportunity is to use explainable fact-checking tools to generate explanations for hypothesized drug-disease treatment relationships in a given knowledge graph, with a reliability score assigned to each explanation. The explanations and their scores can then be used by experts to determine which drug-disease pairs to consider for clinical trials.In our collaboration with a biomedical team, we have found that existing explainable fact-checking tools are not necessarily helpful in drug discovery, as their explanation formats and evaluation metrics do not match well the requirements of scientific discovery in the biomedical domain. To address these challenges in using fact-checking tools in drug discovery, we introduce a scalable automated approach for generating explanations that are modeled after existing biomedical concepts and supplemented with data-supported evaluation metrics. Our explanations are based on knowledge-graph patterns, which are readily understood by biomedical experts. Our experimental results suggest that our proposed metrics are accurate and useful on largescale biomedical knowledge graphs, and our explanations are understandable and reasonable to experts doing drug discovery.