Information for "Integration of Scholarly Communication Metadata using Knowledge Graphs"

In this paper, we presented the concept of Scholarly Communication Metadata Knowledge Graph (SCM-KG), which integrates heterogeneous, distributed schemas, data and

metadata from a variety of scholarly communication data sources. As a proof-of-concept, we developed an SCM-KG pipeline to create a knowledge graph by integrating data collected from heterogeneous data sources. We showed the capability of parallelization in rule-based data mappings, and we also presented how semantic similarity measures are applied to determine the relatedness of concepts in two resources in terms of the relatedness of their RDF interlinking structure. Results of the empirical evaluation suggest that the integration approach pursued by the SCM-KG pipeline is able to effectively integrate pieces of information spread across different data sources. The experiments suggest that the rule based mapping together with semantic structure based instance matching technique implemented in the SCM-KG pipeline integrates data in a knowledge graph with high accuracy. Although our initial use case addresses the scientific metadata domain, we generated billions of triples with high accuracy in mapping and linking, and we regard it capable at an industrial scale and in use cases demanding high

precision.