Systematic Construction of Knowledge Graphs for Research-Performing Organizations

Abstract

Research-Performing Organizations (e.g., research centers, universities) usually accumulate a wealth of data related to their researchers, the generated scientific results and research outputs, and publicly and privately-funded projects that support their activities, etc. Even though the types of data handled may look similar across organizations, it is common to see that each institution has developed its own data model to provide support for many of their administrative activities (project reporting, curriculum management, personnel management, etc.). This creates obstacles to the integration and linking of knowledge across organizations, as well as difficulties when researchers move from one institution to another. In this paper, we take advantage of the ontology network created by the Spanish HERCULES initiative to facilitate the construction of knowledge graphs from existing information systems, such as the one managed by the company Universitas XXI, which provides support to more than 100 Spanish-speaking research-performing organizations worldwide. Our effort is not just focused on following the modeling choices from that ontology, but also on demonstrating how the use of standard declarative mapping rules (i.e., R2RML) guarantees a systematic and sustainable workflow for constructing and maintaining a KG. We also present several real-world use cases in which the proposed workflow is adopted together with a set of lessons learned and general recommendations that may also apply to other domains. The next steps include researching in the automation of the creation of the mapping rules, the enrichment of the KG with external sources, and its exploitation though distributed environments.

Keywords

• knowledge graph
• research-performing organizations
• declarative mapping rules