@inproceedings{liu-etal-2026-simrule,
title = "{S}im{R}ule: Collaborative Similarity-Driven Rule Learning for Interpretable Temporal Knowledge Graph Forecasting",
author = "Liu, Wenshuo and
Hao, Shufeng and
Li, Ziyu and
Shi, Chongyang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1824/",
doi = "10.18653/2026.findings-emnlp.1824",
pages = "38960--38974",
ISBN = "979-8-89176-462-0",
abstract = "Rule-based methods for temporal knowledge graph (TKG) forecasting are valued for the interpretability of their explicit logic chains. However, they suffer from a severe cold-start coverage gap: exact-match closed walks terminate at sparse entities, leaving many queries without any rule evidence. Existing similarity-based remedies are unreliable because their similarity is derived from static structural co-occurrence or shallow embeddings with no temporal calibration and hand-tuned thresholds. To address these limitations, we propose SimRule, a collaborative recommendation framework for TKG rule mining. SimRule first mines both exact rules and collaborative-surrogate rules through a temporally calibrated dual-view entity similarity index. An adaptive dual-branch fusion then ranks candidate objects from their aggregated evidence, and each forecast remains fully auditable as an explicit rule chain with quantified time gaps. On ICEWS14, ICEWS18, and ICEWS05-15, SimRule achieves state-of-the-art MRR and Hits@$K$ ($K \ge 3$) among rule-based methods."
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<abstract>Rule-based methods for temporal knowledge graph (TKG) forecasting are valued for the interpretability of their explicit logic chains. However, they suffer from a severe cold-start coverage gap: exact-match closed walks terminate at sparse entities, leaving many queries without any rule evidence. Existing similarity-based remedies are unreliable because their similarity is derived from static structural co-occurrence or shallow embeddings with no temporal calibration and hand-tuned thresholds. To address these limitations, we propose SimRule, a collaborative recommendation framework for TKG rule mining. SimRule first mines both exact rules and collaborative-surrogate rules through a temporally calibrated dual-view entity similarity index. An adaptive dual-branch fusion then ranks candidate objects from their aggregated evidence, and each forecast remains fully auditable as an explicit rule chain with quantified time gaps. On ICEWS14, ICEWS18, and ICEWS05-15, SimRule achieves state-of-the-art MRR and Hits@K (K \ge 3) among rule-based methods.</abstract>
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%0 Conference Proceedings
%T SimRule: Collaborative Similarity-Driven Rule Learning for Interpretable Temporal Knowledge Graph Forecasting
%A Liu, Wenshuo
%A Hao, Shufeng
%A Li, Ziyu
%A Shi, Chongyang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-simrule
%X Rule-based methods for temporal knowledge graph (TKG) forecasting are valued for the interpretability of their explicit logic chains. However, they suffer from a severe cold-start coverage gap: exact-match closed walks terminate at sparse entities, leaving many queries without any rule evidence. Existing similarity-based remedies are unreliable because their similarity is derived from static structural co-occurrence or shallow embeddings with no temporal calibration and hand-tuned thresholds. To address these limitations, we propose SimRule, a collaborative recommendation framework for TKG rule mining. SimRule first mines both exact rules and collaborative-surrogate rules through a temporally calibrated dual-view entity similarity index. An adaptive dual-branch fusion then ranks candidate objects from their aggregated evidence, and each forecast remains fully auditable as an explicit rule chain with quantified time gaps. On ICEWS14, ICEWS18, and ICEWS05-15, SimRule achieves state-of-the-art MRR and Hits@K (K \ge 3) among rule-based methods.
%R 10.18653/2026.findings-emnlp.1824
%U https://aclanthology.org/2026.findings-emnlp.1824/
%U https://doi.org/10.18653/2026.findings-emnlp.1824
%P 38960-38974
Markdown (Informal)
[SimRule: Collaborative Similarity-Driven Rule Learning for Interpretable Temporal Knowledge Graph Forecasting](https://aclanthology.org/2026.findings-emnlp.1824/) (Liu et al., Findings 2026)
ACL