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.