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On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs

arXiv 2104.05866 ↗First posted 12 Apr 2021cs.SI cs.LGcited by 0

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Abstract (arXiv)

In the era of misinformation and information inflation, the credibility assessment of the produced news is of the essence. However, fact-checking can be challenging considering the limited references presented in the news. This challenge can be transcended by utilizing the knowledge graph that is related to the news articles. In this work, we present a methodology for creating scientific news article representations by modeling the directed graph between the scientific news articles and the cited scientific publications. The network used for the experiments is comprised of the scientific news articles, their topic, the cited research literature, and their corresponding authors. We implement and present three different approaches: 1) a baseline Relational Graph Convolutional Network (R-GCN), 2) a Heterogeneous Graph Neural Network (HetGNN) and 3) a Heterogeneous Graph Transformer (HGT). We test these models in the downstream task of link prediction on the: a) news article - paper links and b) news article - article topic links. The results show promising applications of graph neural network approaches in the domains of knowledge tracing and scientific news credibility assessment.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 12 Apr 2021.

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References · 15

  1. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  2. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  3. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  5. 2019Heterogeneous Graph Attention Networkcited by 732
  6. 2020Heterogeneous Graph Transformercited by 581
  7. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 197
  8. 2019Text Classification Algorithms: A Surveycited by 163
  9. 2017A Survey on Network Embeddingcited by 142
  10. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  11. 2016An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generationcited by 48
  12. 2017Attributed Network Embedding for Learning in a Dynamic Environmentcited by 39
3 more
  1. 2017Attributed Social Network Embeddingcited by 34
  2. 2019SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicatorscited by 4
  3. 2019A Dynamic Embedding Model of the Media Landscapecited by 1

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