INTEGRATE-KG: A Workflow For Unifying Heterogeneous Data Driven by Shared Languages

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.