@inproceedings{ma-etal-2026-queries,
title = "Not All Queries Are Equal: Query-Type-Aware Mixture-of-Experts for Temporal Knowledge Graph Forecasting",
author = "Ma, Rong and
Ma, Bo and
Yang, Yating and
Zhou, Xi and
Wang, Lei and
Dong, Rui and
Ahmat, Ahtamjan and
Wang, Zhen",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1274/",
pages = "29057--29078",
ISBN = "979-8-89176-459-0",
abstract = "Temporal knowledge graph forecasting predicts future facts from historical snapshots, and its accuracy hinges on how well a model exploits the historical evidence available to each query. Existing methods route every query through a single reasoning pipeline, yet queries with the same predicate form can rest on very different histories and call for different inductive biases. We observe that query histories fall into three regimes: sparse histories with few prior events, recurrent histories dominated by stable repetition, and ambiguous histories with many competing candidates. Building on this observation, we propose QTI-MoE, a Query-Type-aware Inductive-bias Mixture-of-Experts framework. QTI-MoE characterises each query with a soft type label derived from its historical prefix and forwards it to one of three experts whose memory pathways carry complementary biases: recency-weighted aggregation for sparse queries, count-based repetition for recurrent queries, and diversity-normalised scoring for ambiguous queries. QTI-MoE achieves state-of-the-art performance on four standard benchmarks, and a type-stratified analysis confirms that each expert peaks on its target regime."
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<abstract>Temporal knowledge graph forecasting predicts future facts from historical snapshots, and its accuracy hinges on how well a model exploits the historical evidence available to each query. Existing methods route every query through a single reasoning pipeline, yet queries with the same predicate form can rest on very different histories and call for different inductive biases. We observe that query histories fall into three regimes: sparse histories with few prior events, recurrent histories dominated by stable repetition, and ambiguous histories with many competing candidates. Building on this observation, we propose QTI-MoE, a Query-Type-aware Inductive-bias Mixture-of-Experts framework. QTI-MoE characterises each query with a soft type label derived from its historical prefix and forwards it to one of three experts whose memory pathways carry complementary biases: recency-weighted aggregation for sparse queries, count-based repetition for recurrent queries, and diversity-normalised scoring for ambiguous queries. QTI-MoE achieves state-of-the-art performance on four standard benchmarks, and a type-stratified analysis confirms that each expert peaks on its target regime.</abstract>
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%0 Conference Proceedings
%T Not All Queries Are Equal: Query-Type-Aware Mixture-of-Experts for Temporal Knowledge Graph Forecasting
%A Ma, Rong
%A Ma, Bo
%A Yang, Yating
%A Zhou, Xi
%A Wang, Lei
%A Dong, Rui
%A Ahmat, Ahtamjan
%A Wang, Zhen
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ma-etal-2026-queries
%X Temporal knowledge graph forecasting predicts future facts from historical snapshots, and its accuracy hinges on how well a model exploits the historical evidence available to each query. Existing methods route every query through a single reasoning pipeline, yet queries with the same predicate form can rest on very different histories and call for different inductive biases. We observe that query histories fall into three regimes: sparse histories with few prior events, recurrent histories dominated by stable repetition, and ambiguous histories with many competing candidates. Building on this observation, we propose QTI-MoE, a Query-Type-aware Inductive-bias Mixture-of-Experts framework. QTI-MoE characterises each query with a soft type label derived from its historical prefix and forwards it to one of three experts whose memory pathways carry complementary biases: recency-weighted aggregation for sparse queries, count-based repetition for recurrent queries, and diversity-normalised scoring for ambiguous queries. QTI-MoE achieves state-of-the-art performance on four standard benchmarks, and a type-stratified analysis confirms that each expert peaks on its target regime.
%U https://aclanthology.org/2026.emnlp-main.1274/
%P 29057-29078
Markdown (Informal)
[Not All Queries Are Equal: Query-Type-Aware Mixture-of-Experts for Temporal Knowledge Graph Forecasting](https://aclanthology.org/2026.emnlp-main.1274/) (Ma et al., EMNLP 2026)
ACL
- Rong Ma, Bo Ma, Yating Yang, Xi Zhou, Lei Wang, Rui Dong, Ahtamjan Ahmat, and Zhen Wang. 2026. Not All Queries Are Equal: Query-Type-Aware Mixture-of-Experts for Temporal Knowledge Graph Forecasting. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29057–29078, Budapest, Hungary. Association for Computational Linguistics.