HETNet: Historical Event Trend Prediction for Temporal Knowledge Graph Reasoning
Authors: Yunhao Xie, Desheng Wu


Reviewer paper summaries: This paper proposes HETNet, a novel approach for temporal knowledge graph reasoning that focuses on predicting non-historical events (events that have not occurred before the current timestamp). The method incorporates a historical trend predictor to estimate the probability distribution of entities and uses adversarial training to predict non-historical events. The authors evaluate their model on five public temporal knowledge graph datasets, claiming state-of-the-art performance with up to a 4.1% relative improvement in Hits@1 compared to previous methods.
Summary of strengths: - This paper especially focuses on forecasting "non-historical" events, where the same event has never occurred in the past. This problem itself is interesting and relevant to the field.
- The proposed use of a binary classifier for trend prediction to guide entity selection is a novel approach to incorporating historical information.
Summary of weaknesses: - The paper does not follow the *CL submission format. Please take note of the font and line number requirements.
- The main experimental setting is not clear, and the results are concerning. For example, the baseline models' performance on the public datasets reported in this paper differs significantly from what has been reported in other papers and the original publications.
- The relative performance of these models is also concerning. For instance, it is generally accepted that REGCN surpasses RENET, but this paper reports completely different results. This leads to concerns about the experiment and evaluation settings in this work and casts doubt on the reliability of the proposed method's reported performance.
- More detailed analysis could be conducted. Since this paper focuses on "non-historical" events, it could discuss the difference between "non-historical" events due to the first appearance of an entity versus the first interaction between two entities. This would provide more insight into the method's effectiveness and limitations. Intuitively, the second case might be easier to predict, since each entity has rich historical information.
- The authors do not provide experimental results or discussions on the separate performance of the proposed method for repeated events versus non-historical events.
Comments, suggestions and typos: Please refer to the comments and questions raised in Weakness section.
Overall assessment scores: 2