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.