SPedia

D’Elia, Alfredo; Viola, Fabio; Roffia, Luca; Azzoni, Paolo; Cinotti, Tullio Salmon

doi:10.4018/ijswis.2017010108

Cited by 7 publications

(3 citation statements)

References 64 publications

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“…When SPedia is interlinked to LOD, it can be used for improved collaboration and knowledge sharing among scientific authors. It can act as a central hub for linked open scientific publication data by linking to the LOD of scientific work by other publishers ( Aslam & Aljohani, 2020 ). One major limitation of SPedia is that it does not consider altmetric data and is entirely dependent on bibliometric parameters.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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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Section: Related Workmentioning

confidence: 99%

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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“…As discussed above that the architecture and data extraction process of LOPDF framework is designed in such a way that it could be customized by doing small changes in the end point triggers of the framework based on the structure and templates of the data source. We have already customized and applied the LOPDF framework on the SpringerLink as source of data and have created a knowledge base (named as SPedia (Aslam & Aljohani, 2017;) of semantically enriched data about scientific publications published by Springer. In this section we give short introduction of SPedia as a product of LOPDF framework and provide the quantitative as well as qualitative analysis of datasets produced by using our framework.…”

Section: Analysis Of Extracted Rdf Datasetsmentioning

confidence: 99%

LOPDF: a framework for extracting and producing open data of scientific documents for smart digital libraries

Aslam

2021

PeerJ Computer Science

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Background Results of scientific experiments and research work, either conducted by individuals or organizations, are published and shared with scientific community in different types of scientific publications such as books, chapters, journals, articles, reference works and reference works entries. One aspect of these documents is their contents and the other is metadata. Metadata of scientific documents could be used to increase mutual cooperation, find people with common interest and research work, and to find scientific documents in the matching domains. The major issue in getting these benefits from metadata of scientific publications is availability of these data in unstructured (or semi-structured) format so that it can not be used to ask smart queries that can help in computing and performing different types of analysis on scientific publications data. Also, acquisition and smart processing of publications data is a complicated as well as time and resource consuming task. Methods To address this problem we have developed a generic framework named as Linked Open Publications Data Framework (LOPDF). The LOPDF framework can be used to crawl, process, extract and produce machine understandable data (i.e., LOD) about scientific publications from different publisher specific sources such as portals, XML export and websites. In this paper we present the architecture, process and algorithm that we developed to process textual publications data and to produce semantically enriched data as RDF datasets (i.e., open data). Results The resulting datasets can be used to make smart queries by making use of SPARQL protocol. We also present the quantitative as well as qualitative analysis of our resulting datasets which ultimately can be used to compute the research behavior of organizations in rapidly growing knowledge society. Finally, we present the potential usage of producing and processing such open data of scientific publications and how results of performing smart queries on resulting open datasets can be used to compute the impact and perform different types of analysis on scientific publications data.

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The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data

Färber

2019

Lecture Notes in Computer Science

104

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SPedia

Cited by 7 publications

References 64 publications

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

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

LOPDF: a framework for extracting and producing open data of scientific documents for smart digital libraries

The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data

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