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A plethora of scholarly knowledge is being published on distributed scholarly infrastructures. Querying a single infrastructure is no longer sufficient for researchers to satisfy information needs. We present a GraphQL-based federated query service for executing distributed queries on numerous, heterogeneous scholarly infrastructures (currently, ORKG, DataCite and GeoNames), thus enabling the integrated retrieval of scholarly content from these infrastructures. Furthermore, we present the methods that enable cross-walks between artefact metadata and artefact content across scholarly infrastructures, specifically DOI-based persistent identification of ORKG artefacts (e.g., ORKG comparisons) and linking ORKG content to third-party semantic resources (e.g., taxonomies, thesauri, ontologies). This type of linking increases interoperability, facilitates the reuse of scholarly knowledge, and enables finding machine actionable scholarly knowledge published by ORKG in global scholarly infrastructures. In summary, we suggest applying the established linked data principles to scholarly knowledge to improve its findability, interoperability, and ultimately reusability, i.e., improve scholarly knowledge FAIR-ness.

Efficient source selection is one of the most important optimization steps in federated SPARQL query processing as it leads to more efficient query execution plan generation. An over-estimation of the data sources will generate extra network traffic by retrieving irrelevant intermediate results. Such intermediate results will be excluded after performing joins between triple patterns. Consequently an over-estimation of sources may result in increased query execution time. Devising triple patterns join-aware source selection approaches has shown to yield great improvement potential. In this work, we present UPSP, a new source selection approach for SPARQL query federation over multiple SPARQL endpoints. UPSP makes use of the subject-subject, subject-object, object-subject, and object-object joins information stored in an index structure to perform efficient triple patterns join-aware source selection. Our evaluation results on FedBench shows that UPSP outperforms state-of-the-art source selection approaches by selecting smaller number of sources (without losing recall) and reducing the query execution times.

Efficient source selection is one of the most important optimization steps in federated SPARQL query processing as it leads to more efficient query execution plan generation. An over-estimation of the data sources will generate extra network traffic by retrieving irrelevant intermediate results. Such intermediate results will be excluded after performing joins between triple patterns. Consequently an over-estimation of sources may result in increased query execution time. Devising triple patterns join-aware source selection approaches has shown to yield great improvement potential. In this work, we present UPSP, a new source selection approach for SPARQL query federation over multiple SPARQL endpoints. UPSP makes use of the subject-subject, subject-object, object-subject, and object-object joins information stored in an index structure to perform efficient triple patterns join-aware source selection. Our evaluation results on FedBench shows that UPSP outperforms state-of-the-art source selection approaches by selecting smaller number of sources (without losing recall) and reducing the query execution times.

Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no “one model fits all”), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.

Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains. Causes for this challenge are context-specific requirements (i.e. no “one model fits all”), different data modelling decisions, domain-specific purposes, and technical constraints. Moreover, even if the problem of semantic heterogeneity among different RDF publishers and knowledge domains is solved, querying and accessing the data of distributed RDF datasets on the Web is not straightforward. This is because of the complex and fastidious process needed to understand how these datasets can be related or linked, and consequently, queried. To address this issue, we propose to extend the existing Vocabulary of Interlinked Datasets (VoID) by introducing new terms such as the Virtual Link Set concept and data model patterns. A virtual link is a connection between resources such as literals and IRIs (Internationalized Resource Identifier) with some commonality where each of these resources is from a different RDF dataset. The links are required in order to understand how to semantically relate datasets. In addition, we describe several benefits of using virtual links to improve interoperability between heterogenous and independent datasets. Finally, we exemplify and apply our approach to multiple world-wide used RDF datasets.