Temporal knowledge graph (TKG) has been proved to be an effective way for modeling dynamic facts in real world. Many efforts have been devoted into predicting future events i.e. extrapolation, on TKGs. Recently, rule-based knowledge graph completion methods which are considered to be more interpretable than embedding-based methods, have been transferred to temporal knowledge graph extrapolation. However, rule-based models suffer from temporal redundancy when leveraged under dynamic settings, which results in inaccurate rule confidence calculation. In this paper, we define the problem of temporal redundancy and propose TR-Rules which solves the temporal redundancy issues through a simple but effective strategy. Besides, to capture more information lurking in TKGs, apart from cyclic rules, TR-Rules also mines and properly leverages acyclic rules, which has not been explored by existing models. Experimental results on three benchmarks show that TR-Rules achieves state-of-the-art performance. Ablation study shows the impact of temporal redundancy and demonstrates the performance of acyclic rules is much more promising due to its higher sensitivity to the number of sampled walks during learning stage.

TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy

**Workshop Organizers:** Mihai Surdeanu, Laura Chiticariu, Dayne Freitag, Gus Hahn-Powell, Clayton Morrison, Enrique Noriega, Ellen Riloff, Becky Sharp and Marco A. Valenzuela Escárcega
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*Coffee Break 3:30 pm* [**Workshop Website**](https://pan-dl.github.io/2023/about) This workshop will focus on all aspects of pattern-based approaches, including their application, representation, and interpretability, as well as their strengths and weaknesses relative to state-of-the-art machine learning approaches. It will also explore ways of combining the strengths of pattern-based, deep learning and other statistical methods. We wish the workshop to be the first step in building a community of researchers from different areas of NLP, both applied and theoretical, who are interested in pattern-based approaches and who use them in their work (e.g., industry practitioners and domain experts). As such, beyond the standard keynote talks and user-contributed presentations, we plan for the workshop to include a panel discussion, breakout sessions on different sub-topics, and a meeting to discuss a potential shared task for future events.
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W08: The Pattern-based Approaches to NLP in the Age of Deep Learning Workshop (Pan-DL)

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**Welcome to EMNLP 2023!**

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TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy

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