SPedia: A Semantics Based Repository of Scientific Publications Data

Aslam, Muhammad Ahtisham; Aljohani, Naif Radi

doi:10.1007/978-3-319-39937-9_37

Cited by 3 publications

(7 citation statements)

References 8 publications

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“…Better represented publications data will produce better analytical results based on reasoning of existing data and results of such analysis can definitely play role in defining better policies. In this paper we also focus on analyzing scientific publications data by making use of SPedia knowledge base [7], [8] (a semantically enriched repository of scientific publications data) to facilitate organizational policy making for STI in a simpler way like Sapientia [16].…”

Section: Related Workmentioning

confidence: 99%

“…SPedia [7], [8] is the semantic Web based knowledge repository that we extracted by using SpringerLink as information source. Figure 1 shows the process that we used to parse metadata of scientific documents and produce RDF datasets.…”

Section: Spedia Knowledge Basementioning

confidence: 99%

“…To address these limitations, we make use of SPedia [7], [8] RDF datasets. SPedia is a semantically enriched knowledge base which we extracted by taking SpringerLink as source.…”

Section: Introductionmentioning

confidence: 99%

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Using Publications Linked Open Data to Define Organizational Policies

Aslam¹

2018

IJIEEB

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Spedia Knowledge Basementioning

confidence: 99%

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Using Publications Linked Open Data to Define Organizational Policies

Aslam¹

2018

IJIEEB

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Maatouk

2022

PeerJ Computer Science

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Background Sharing knowledge such as resources, research results, and scholarly documents, is of key importance to improving collaboration between researchers worldwide. Research results from the field of artificial intelligence (AI) are vital to share because of the extensive applicability of AI to several other fields of research. This has led to a significant increase in the number of AI publications over the past decade. The metadata of AI publications, including bibliometrics and altmetrics indicators, can be accessed by searching familiar bibliographical databases such as Web of Science (WoS), which enables the impact of research to be evaluated and identify rising researchers and trending topics in the field of AI. Problem description In general, bibliographical databases have two limitations in terms of the type and form of metadata we aim to improve. First, most bibliographical databases, such as WoS, are more concerned with bibliometric indicators and do not offer a wide range of altmetric indicators to complement traditional bibliometric indicators. Second, the traditional format in which data is downloaded from bibliographical databases limits users to keyword-based searches without considering the semantics of the data. Proposed solution To overcome these limitations, we developed a repository, named AI-SPedia. The repository contains semantic knowledge of scientific publications concerned with AI and considers both the bibliometric and altmetric indicators. Moreover, it uses semantic web technology to produce and store data to enable semantic-based searches. Furthermore, we devised related competency questions to be answered by posing smart queries against the AI-SPedia datasets. Results The results revealed that AI-SPedia can evaluate the impact of AI research by exploiting knowledge that is not explicitly mentioned but extracted using the power of semantics. Moreover, a simple analysis was performed based on the answered questions to help make research policy decisions in the AI domain. The end product, AI-SPedia, is considered the first attempt to evaluate the impacts of AI scientific publications using both bibliometric and altmetric indicators and the power of semantic web technology.

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