Using Publications Linked Open Data to Define Organizational Policies

Abstract

Researchers around the world are publishing their scientific research results in different forms such as books, journal articles, reference works and project reports. Publishers of these scientific documents usually describe them by using metadata for organizational purposes. This metadata provides a rich information about scientific documents that can be used for analysis purposes such as measuring the impact of researchers and research centers. It can also be used to find scientific documents published in domain of some ones interest, which ultimately can be used to raise the state of the art to the next level. Scientific publications metadata can also be used to analyze the quality and directions of common and highly cited individuals and organizations, and based on this analysis other individuals and organizations can define directions for their future work and research. However, the main limitation of this metadata is that it is available in different formats that might not facilitate the analysis of scientific documents. Therefore, in this paper we clarify that how our SPedia knowledge base (a semantic based knowledge base of scientific publications metadata which we extracted by using SpringerLink as information source) facilitates the analysis of scientific data for policy making. We discuss different kind of questions that can be answered through SPedia knowledge base and we show that how results of these questions can be used to analyze the performance of individuals as well as organizations. We also show that how results of such analysis can help in making organizational policies regarding future research directions.

AI-SPedia: a novel ontology to evaluate the impact of research in the field of artificial intelligence

“…The goal of SPedia is to facilitate a semantic-based search to allow users to pose smart and complex queries and overcome the limitations of traditional keyword-based searching by creating a SPARQL endpoint to query the dataset ( Ahtisham & Aljohani, 2016 ). The authors demonstrated how SPedia can be used to analyze trends in joint publications and research collaborations ( Ahtisham, 2018 ). The results retrieved from the SPedia knowledge base using SPedia SPARQL Endpoint enables the performance of both individual researchers and organizations to be analyzed.…”

“…The results retrieved from the SPedia knowledge base using SPedia SPARQL Endpoint enables the performance of both individual researchers and organizations to be analyzed. This process can help decision makers set future research directions ( Ahtisham, 2018 ). Moreover, SPedia is the first step toward achieving an LOD cloud of scholarly documents by well-known publishers such as Springer.…”

“…Several studies have been conducted to extract structured information from scientific documents to gather semantically enriched data from existing sources. For example, DBLP ( Aleman-Meza et al, 2007 ; DBLP, 2021 ), SPedia ( Ahtisham, 2018 ; Ahtisham & Aljohani, 2016 ; Aslam & Aljohani, 2020 ), VIVO ( Corson-Rikert & Cramer, 2010 ), CERIF ( Nogales, Sicilia & Jörg, 2014 ), Sapientia ( Daraio et al, 2016 ), PharmSci ( Say et al, 2020 ), and several other studies involving ontologies that translate data from existing data sources related to scientific research into a unified resource description framework (RDF). Consequently, end users can query the dataset to extract useful knowledge using SPARQL.…”

“…To enhance the data representation and overcome the limitations of bibliographical databases, we developed a semantically-enriched knowledge base, referred to as AI-SPedia, as an extension to SPedia ( Ahtisham, 2018 ; Ahtisham & Aljohani, 2016 ) to include information about scientific publications and researchers in the field of AI. AI-SPedia differs from SPedia in the following two ways: (i) it uses bibliometric and altmetric data of scientific publications to evaluate the research impact of a specific document or researcher; and (ii) the bibliometric and altmetric data of scientific publications are extracted from various sources, and they are presented as semantically enriched data ( i.e.…”