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Tieke He

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3 papers

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AAAI Conference 2026 Conference Paper

EchoEdit: Consistent Multi-Hop Question Answering via Ripple Control in Knowledge Editing

Jinwei Shi
Wenxuan Huang
Yu Xing
Yunhui Liu
Tao Zheng
Bin Chong
Tieke He

Knowledge editing aims to update specific knowledge in Large Language Models (LLMs) without retraining the entire model. However, existing methods generally struggle to manage the ripple effects of knowledge updates, particularly in multi-hop reasoning tasks, where conflicts between old and new information often lead to shifts in reasoning chains and degraded consistency. To address this issue, a ripple-aware knowledge editing framework, namely EchoEdit, is proposed. EchoEdit introduces the RippleGraph to explicitly model potentially affected knowledge regions and employs a RippleRule generator to dynamically produce diffusion rules, precisely constraining knowledge propagation. Furthermore, we distill a Chain-of-Thought (CoT) planner from an external teacher model, which decouples complex reasoning chain planning into RippleGraph-guided reasoning, thereby alleviating the reasoning burden on low-resource LLMs in multi-hop tasks. Experimental results on the MQuAKE and RIPPLEEDITS multi-hop reasoning benchmarks demonstrate that EchoEdit significantly outperforms existing mainstream methods, effectively enhancing post-edit reasoning consistency and generalization capabilities.

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AAAI Conference 2026 Conference Paper

On Modality Weighting and Specificity for Multi-Modal Entity Alignment

Yu Xing
Qizhuo Xie
Yunhui Liu
Qing Gu
Tao Zheng
Bin Chong
Tieke He

Multi-modal entity alignment aims to identify equivalent entities across different multi-modal knowledge graphs (MMKGs). While prior work has achieved notable progress through improved multi-modal encoding and cross-modal fusion techniques, two critical challenges remain unresolved. First, due to the heterogeneous and often inconsistent sources from which MMKGs are constructed, the quality and informativeness of modalities vary significantly across entities, leading to the modality weighting problem. Second, existing cross-modal fusion mechanisms predominantly emphasize modality-shared information, often at the expense of modality-specific signals that are also essential for precise alignment. To address these issues, we propose HUMEA, a novel framework that integrates hierarchical Mixture-of-Experts (MoE) with unimodal distillation. HUMEA consists of: (1) A hierarchical MoE module comprising intra-modal and inter-modal experts, which adaptively modulates modality contributions by capturing entity representations at fine-to-coarse semantic granularities. In addition, we introduce a contrastive mutual information loss to enhance expert diversity and reduce redundancy. (2) A unimodal distillation strategy that preserves modality-specific information in the fused representations through single-modality alignment and distillation, achieving a balanced integration of shared and unique modality features. Extensive experiments on two benchmark datasets, FB15K-DB15K and FB15K-YAGO15K, demonstrate state-of-the-art performance, validating the effectiveness of our approach.

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TIST Journal 2016 Journal Article

A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs

Tieke He
Hongzhi Yin
Zhenyu Chen
Xiaofang Zhou
Shazia Sadiq
Bin Luo

Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones. In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.

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