Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata

Turki, Houcemeddine; Taieb, Mohamed Ali Hadj; Aouicha, Mohamed Ben; Fraumann, Grischa; Hauschke, Christian; Heller, Lambert

doi:10.3389/frma.2021.694307

Cited by 12 publications

(3 citation statements)

References 47 publications

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“…As the knowledge graph's building blocks, schemas establish nodes' identity, and the setting establishes their location in relation to each other; terms that have multiple meanings benefit from the inclusion of these features. For example, Google's search engine system can distinguish among the color orange and the fruit orange [48].…”

Section: Knowledge Representationmentioning

confidence: 99%

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

et al. 2022

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The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy.

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Section: Knowledge Representationmentioning

confidence: 99%

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

et al. 2022

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“…Knowledge graphs, as effective tools of information retrieval (Reinanda et al, 2020 ), are applied in various domains (Zou, 2020 ; Abu-Salih, 2021 ), including physics (Say et al, 2020 ), healthcare (Steenwinckel et al, 2020 ; Zhang et al, 2020 ), business (Meier et al, 2021 ), and education (Chen et al, 2018 ; Chi et al, 2018 ; Rizun, 2019 ; Qin et al, 2020 ). Scholarly knowledge graphs operate with academic literature (Turki et al, 2021 ) and thus can be viewed in the frame of the scholarly knowledge ecosystem (Altman and Cohen, 2022 ). Implementation of such novel technologies in scholarly communication is beneficial for Open Science (Ignat et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation

Lezhnina

Kismihók

Prinz

et al. 2022

Front. Res. Metr. Anal.

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Scholarly knowledge graphs provide researchers with a novel modality of information retrieval, and their wider use in academia is beneficial for the digitalization of published works and the development of scholarly communication. To increase the acceptance of scholarly knowledge graphs, we present a dashboard, which visualizes the research contributions on an educational science topic in the frame of the Open Research Knowledge Graph (ORKG). As dashboards are created at the intersection of computer science, graphic design, and human-technology interaction, we used these three perspectives to develop a multi-relational visualization tool aimed at improving the user experience. According to preliminary results of the user evaluation survey, the dashboard was perceived as more appealing than the baseline ORKG-powered interface. Our findings can be used for the development of scholarly knowledge graph-powered dashboards in different domains, thus facilitating acceptance of these novel instruments by research communities and increasing versatility in scholarly communication.

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“…Knowledge graphs, as effective tools of information retrieval (Reinanda et al, 2020), are applied in various domains (Zou, 2020;Abu-Salih, 2021), including physics (Say et al, 2020), healthcare (Steenwinckel et al, 2020;Zhang et al, 2020), business (Meier et al, 2021), and education (Chen et al, 2018;Chi et al, 2018;Rizun, 2019;Qin et al, 2020). Scholarly knowledge graphs operate with academic literature (Turki et al, 2021) and thus can be viewed in the frame of the scholarly knowledge ecosystem (Altman and Cohen, 2022). Implementation of such novel technologies in scholarly communication is beneficial for Open Science (Ignat et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Knowledge Graph Extraction and Validation From Scholarly Publications Using Bibliographic Metadata

Cited by 12 publications

References 47 publications

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

A Scholarly Knowledge Graph-Powered Dashboard: Implementation and User Evaluation

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