Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks

Zhu, Yifan; Lin, Qika; Lü, Hao; Shi, Kaize; Qiu, Ping; Niu, Zhendong

doi:10.1016/j.knosys.2021.106744

Cited by 51 publications

(13 citation statements)

References 27 publications

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“…According to different usages, citation recommendation can be divided into global recommendation and inline recommendation. The global citation recommendation ( Ayala-Gomez et al, 2018 , Dai et al, 2021 , Zhu et al, 2021 ) returns the reference that are relevant to the entire query paper. In contrast, inline citation recommendation, also called context-aware citation recommendation, only provides references for a citation placeholder according to its local context.…”

Section: Related Workmentioning

confidence: 99%

“…Comparing with common scientific literature data, the information among COVID-19 papers, such as text content, authors and venues, have more character of heterogeneous. For paper and citation recommendation task, the heterogeneous character will bring more obstacles for finding desired citations ( Zhu et al, 2021 ). Therefore, how to fully mine the association between heterogeneous nodes, and distinguish the importance of them to further refine optimal node representation, is a key issue for improving the performance of COVID-19 citation recommendation.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

Dai

Zhao

et al. 2023

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

Dai

Zhao

et al. 2023

Expert Systems with Applications

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“…The first vector represents the meaning of the entity or relationship, and the second vector is used to construct the mapping matrix. Inspired by the overwhelming performance to present complex KG in recent research [ 46 ], we also incorporate TransD in our research.…”

Section: Related Workmentioning

confidence: 99%

Leverage knowledge graph and GCN for fine-grained-level clickbait detection

Zhou

Zhang

et al. 2022

World Wide Web

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Clickbait is the use of an enticing title as bait to deceive users to click. However, the corresponding content is often disappointing, infuriating or even deceitful. This practice has brought serious damage to our social trust, especially to online media, which is one of the most important channels for information acquisition in our daily life. Currently, clickbait is spreading on the internet and causing serious damage to society. However, research on clickbait detection has not yet been well performed. Almost all existing research treats clickbait detection as a binary classification task and only uses the title as the input. This shallow usage of information and detection technology not only suffers from low performance in real detection (e.g., it is easy to bypass) but is also difficult to use in further research (e.g., potential empirical studies). In this work, we proposed a novel clickbait detection model that incorporated a knowledge graph, a graph convolutional network and a graph attention network to conduct fine-grained-level clickbait detection. According to experiments using a real dataset, our novel proposed model outperformed classical and state-of-the-art baselines. In addition, certain explainability can also be achieved in our model through the graph attention network. Our fine-grained-level results can provide a measurement foundation for future empirical study. To the best of our knowledge, this is the first attempt to incorporate a knowledge graph and deep learning technique to detect clickbait and achieve explainability.

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“…Such conditions costs computationally high and unable to distinguish well among embedding vector which is handled by applying transformations. Similarly, in [ 99 ] authors have applied various translational methods and TransD outperformed in the constructed heterogeneous bibliographic network. TransD creates mapping matrices based on entities and relations, in order to capture the heterogeneity of both entities and relationships at the same time.…”

Section: Knowledge Graph Refinementmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

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The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

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Recommending scientific paper via heterogeneous knowledge embedding based attentive recurrent neural networks

Cited by 51 publications

References 27 publications

Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

Heterogeneous deep graph convolutional network with citation relational BERT for COVID-19 inline citation recommendation

Leverage knowledge graph and GCN for fine-grained-level clickbait detection

Scholarly knowledge graphs through structuring scholarly communication: a review

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