Building AIPedia Ontology to Evaluate Research Impact in Artificial Intelligence Area

Maatouk, Yasser

doi:10.20935/al2781

Cited by 3 publications

(5 citation statements)

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“…Organizations follow the government and the international archival policies to store and manage their documents [6][7][8][9]. e existing studies show many techniques and frameworks for managing documents and IR [9][10][11][12][13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e existing studies focus on the specific domains, and there are no studies on the DM and IR [10][11][12][13][14][15]. ere is a lack of bilingual ontological framework for the organizations in the KSA.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To compete e ectively, organizations should analyze and leverage a wide range of data, information, and expertise in order to make e ective decisions. Decision Support Systems (DSS) are interactive computer-based systems designed to assist decision-makers in identifying and solving problems, completing decision process tasks, and making decisions [12][13][14][15][16][17][18][19]. ese systems are becoming increasingly popular among managers due to this trend.…”

Section: Introductionmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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“…Consequently, machines can interpret and understand these data using RDF. RDF is used to add semantic meaning and provide a hierarchy of classes and properties ( Gandhi & Madia, 2016 ; Maatouk, 2021 ).…”

Section: Related Workmentioning

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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“…Os preceitos da Web Semântica (BERNERS-LEE; HENDLER; LASSILA, 2001;W3C, 2022) entrelaçados com as teorias sociais dos sistemas sociotécnicos e da Teoria Ator-Rede (BURNS, 2006;CALLON, 1989;CALLON, 1986;WALSHAM, 1997) quando aplicados na estruturação de portais de acesso à informação pode torná-los um artefato sociotécnico dinâmico e colaborativo com a modalidade de pesquisa semântica, atendendo os interesses de todos os atores envolvidos, especialmente os da comunidade usuária (IYAMU; MGUDLWA, 2018;MAATOUK, 2021).…”

Section: Introductionunclassified

Indicadores qualitativos para avaliação do potencial semântico de portais do conhecimento de universidades

Guedes,

Strauhs

2023

Inf.Inf.

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Objetivo: Este estudo teve por objetivo propor indicadores qualitativos para a avaliação do potencial semântico de portais do conhecimento de universidades. Metodologia: A pesquisa incluiu levantamento bibliométrico, análise sistemática e de conteúdo e pesquisa de campo com envio de questionário para coordenadores de TI das 69 universidades federais brasileiras, a fim de identificar as tecnologias e os recursos humanos disponíveis no portal de cada instituição. O tratamento dos dados foi efetuado mediante análise estatística descritiva e os indicadores foram validados por um painel de especialistas. Resultados: O mapeamento da rede de portais universitários semânticos evidenciou os atores humanos e não humanos e suas inter-relações marcadas por laços fortes e fracos. Foi proposto um conjunto de 18 indicadores qualitativos construídos a partir de preceitos semânticos e sociotécnicos. Conclusões: O conjunto de indicadores pode ser usado como instrumento de gestão, a fim de disponibilizar pesquisa semântica que facilita o acesso à informação relevante, além de favorecer a interoperabilidade e as ações de comunicação e de colaboração, contribuindo para a evolução dos portais do conhecimento de universidades como uma aplicação sociotécnica.

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Building AIPedia Ontology to Evaluate Research Impact in Artificial Intelligence Area

Cited by 3 publications

References 0 publications

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

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

Indicadores qualitativos para avaliação do potencial semântico de portais do conhecimento de universidades

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