Multi-domain knowledge graph completion (MKGC) seeks to predict missing triples in a target KG by leveraging triples from multiple KGs in different domains (e.g., languages or sources). Existing studies typically learn and fuse multi-domain KG representations solely with alignments or fusion modules, which can be affected by redundant information within KGs. This issue can conceal task-relevant information in representations, impeding further improvements when scaling to numerous KGs. To this end, we propose IMKGC, an information-theoretic MKGC framework to learn minimal sufficient representations. In particular, IMKGC learns entity representations by explicitly preserving endogenous contextual information within each KG, exogenous complementary information from other KGs, and consistent information of equivalent entities, while suppressing redundant information through variational constraints. Furthermore, we achieve compressed relation representations with a devised relation reasoning decoder that captures relatedness among relations, also improving triple prediction. Extensive experiments on 14 KGs in three benchmark datasets demonstrate that IMKGC significantly outperforms previous state-of-the-art methods, especially in redundant scenarios.

Attribute-specific fashion retrieval aims to enhance fine-grained image retrieval by emphasizing the similarity of specific attributes. Current methods primarily rely on attention mechanisms to extract attribute-related visual features but face two key challenges: the limitations of coarse-grained localization in achieving fine-grained accuracy, and an imbalance between global and local perception, where excessive focus on local features can undermine overall performance. To address these issues, we propose the fashion microscope ProFashion, which achieves pixel-level attribute awareness through optimal transport and neural semantic aggregation. The framework begins by employing optimal transport to align semantic attributes with visual patterns from a global perspective, generating an attribute-visual value map that highlights distinctive regions while reducing interference. This is followed by simulating the human brain's perception of attribute feature patterns through superpixel generation and aggregation, capturing attribute-related features at the pixel semantic level and forming key semantic clusters that preserve microstructures. Building on this, an attribute graph is constructed to facilitate feature clustering, significantly enhancing the framework's capability to handle overlapping features and cross-scale relationships. Comprehensive experiments on the FashionAI, DeepFashion, and DARN datasets demonstrate the framework's effectiveness, achieving overall MAP improvements of 3.11%, 3.70%, and 3.49%, respectively. Additionally, the framework delivers relative average throughput gains of 26.94%, 22.22%, and 24.78% on the FashionAI, DeepFashion, and DARN datasets, respectively.

Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang, Tingwen Liu, Li Guo, Yafeng Deng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Live-streaming services have attracted widespread popularity due to their real-time interactivity and entertainment value. Users can engage with live-streaming authors by participating in live chats, posting likes, or sending virtual gifts to convey their preferences and support. However, the live-streaming services faces serious data-sparsity problem, which can be attributed to the following two points: (1) User's valuable behaviors are usually sparse, e.g., like, comment and gift, which are easily overlooked by the model, making it difficult to describe user's personalized preference. (2) The main exposure content on our platform is short-video, which is 9 times higher than the exposed live-streaming, leading to the inability of live-streaming content to fully model user preference. To this end, we propose a Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation, termed as FARM. Specifically, we first present the intra-domain frequency aware module to enable our model to perceive user's sparse yet valuable behaviors, i.e., high-frequency information, supported by the Discrete Fourier Transform (DFT). To transfer user preference across the short-video and live-streaming domains, we propose a novel preference align before fuse strategy, which consists of two parts: the cross-domain preference align module to align user preference in both domains with contrastive learning, and the cross-domain preference fuse module to further fuse user preference in both domains using a serious of tailor-designed attention mechanisms. Extensive offline experiments and online A/B testing on Kuaishou live-streaming services demonstrate the effectiveness and superiority of FARM. Our FARM has been deployed in online live-streaming services and currently serves hundreds of millions of users on Kuaishou.

Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under target behavior for the next interacted item prediction. Unlike previous methods that adopt unidirectional modeling by mapping auxiliary behaviors to target behavior, recent concerns are shifting from behavior-fixed to behavior-specific recommendation. However, these methods still ignore the user's latent preference that underlying decision-making, leading to suboptimal solutions. Meanwhile, due to the asymmetric deterministic between items and behaviors, discriminative paradigm based on preference scoring is unsuitable to capture the uncertainty from low-entropy behaviors to high-entropy items, failing to provide efficient and diverse recommendation. To address these challenges, we propose FatsMB, a framework based diffusion model that guides preference generation From Behavior-Agnostic To Behavior-Specific in latent spaces, enabling diverse and accurate Multi-Behavior Sequential Recommendation. Specifically, we design a Multi-Behavior AutoEncoder (MBAE) to construct a unified user latent preference space, facilitating interaction and collaboration across Behaviors, within Behavior-aware RoPE (BaRoPE) employed for multiple information fusion. Subsequently, we conduct target behavior-specific preference transfer in the latent space, enriching with informative priors. A Multi-Condition Guided Layer Normalization (MCGLN) is introduced for the denoising. Extensive experiments on real-world datasets demonstrate the effectiveness of our model. We release our code at https://github.com/OrchidViolet/FatsMB.

User cold-start problem is a long-standing challenge in recommendation systems. Fortunately, cross-domain recommendation (CDR) has emerged as a highly effective remedy for the user cold-start challenge, with recently developed diffusion models (DMs) demonstrating exceptional performance. However, these DMs-based CDR methods focus on dealing with user-item interactions, overlooking correlations between items across the source and target domains. Meanwhile, the Gaussian noise added in the forward process of diffusion models would hurt user's personalized preference, leading to the difficulty in transferring user preference across domains. To this end, we propose a novel paradigm of Smoothing-Sharpening Process Model for CDR to cold-start users, termed as S2CDR which features a corruption-recovery architecture and is solved with respect to ordinary differential equations (ODEs). Specifically, the smoothing process gradually corrupts the original user-item/item-item interaction matrices derived from both domains into smoothed preference signals in a noise-free manner, and the sharpening process iteratively sharpens the preference signals to recover the unknown interactions for cold-start users. Wherein, for the smoothing process, we introduce the heat equation on the item-item similarity graph to better capture the correlations between items across domains, and further build the tailor-designed low-pass filter to filter out the high-frequency noise information for capturing user's intrinsic preference, in accordance with the graph signal processing (GSP) theory. Extensive experiments on three real-world CDR scenarios confirm that our S2CDR significantly outperforms previous SOTA methods in a training-free manner.

Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs. Existing methods typically enforce consistency constraints on equivalent entities across KGs to transfer knowledge, which risks suppressing domain-specific contextual information of entities. This design can also compromise entity representation information from all KG domains, impeding performance improvements, especially in low-resource data scenarios. To address this, we pioneer a generation-based paradigm for MKGC and propose DMKGC, a conditional diffusion-guided knowledge transfer framework. Our key insight is to treat each KG as a partial view of the entity entire information, and generate informative domain-general entity embeddings through diffusion models conditioned on support KGs. Particularly, we first initialize domain-agnostic entity embeddings as prior entity embeddings, and then encode them within individual KGs. Afterward, we fuse equivalent entities from support KGs as the conditional diffusion generation guidance. We leverage the prior entity embeddings as the proxy generation objective, which ensures this conditional generation to be unbiased towards any conditioned KGs. Simultaneously, we also train the generated embeddings to be predictive across KGs, thus preserving domain-specific information. Extensive experiments on 14 KGs in 3 benchmarks demonstrate a 4.3% average MRR improvement in tail entity prediction over state-of-the-art methods, with sustained gains in low-resource data settings.

Generalized Category Discovery (GCD) is a critical task in open-world computing scenarios, aiming to automatically classify partially labeled data by recognizing both known and novel categories. However, existing GCD methods usually suffer from inherent bias toward known categories due to the exclusive pre-training on them and the absence of labeled data of novel categories. This bias can lead to significant misclassification and clustering errors for novel categories. Although recent approaches leverage pseudo-label training and contrastive learning to address this, they still lack explicit supervision to disentangle novel and known categories, resulting in performance bottlenecks. To address these limitations, we propose an Expectation-Maximization-driven Contrastive Disentanglement (EMCD) framework designed to explicitly disentangle novel and known categories. We particularly formulate the identification of novel categories as a latent variable estimation problem. Specifically, it incorporates an EM-disentangling regularization to softly identify novel category samples and a consistency regularization to enhance generalization. In addition, we leverage dual contrastive constraints, including a cluster-sample contrastive constraint and a sample-sample contrastive contrastive, to pull close samples of novel categories while pushing apart ambiguous samples near decision boundaries. Empirical results on 3 commonly used datasets demonstrate that our model is effective and outperforms previous state-of-the-art methods. Our code is available at https://github.com/YWY-only/EMCD.

Yilong Chen, Junyuan Shang, Zhenyu Zhang, Yanxi Xie, Jiawei Sheng, Tingwen Liu, Shuohuan Wang, Yu Sun, Hua Wu, Haifeng Wang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Multimodal Large Language Models (MLLMs) excel in various tasks, yet often struggle with modality bias, where the model tends to rely heavily on a single modality and overlook critical information in other modalities, which leads to incorrect focus and generating irrelevant responses. In this paper, we propose using the paradigm of preference optimization to solve the modality bias problem, including RLAIF-V-Bias, a debiased preference optimization dataset, and a Noise-Aware Preference Optimization (NaPO) algorithm. Specifically, we first construct the dataset by introducing perturbations to reduce the informational content of certain modalities, compelling the model to rely on a specific modality when generating negative responses. To address the inevitable noise in automatically constructed data, we combine the noise-robust Mean Absolute Error (MAE) with the Binary Cross-Entropy (BCE) in Direct Preference Optimization (DPO) by a negative Box-Cox transformation, and dynamically adjust the algorithm’s noise robustness based on the evaluated noise levels in the data. Extensive experiments validate our approach, demonstrating not only its effectiveness in mitigating modality bias but also its significant role in minimizing hallucinations. The code and data is available at https://github.com/zhangzef/NaPO.

The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation.Current CoT distillation methods mainly focus on transferring teacher-generated rationales for complex reasoning to student models.However, they do not adequately explore teachers' dynamic attention toward critical information during reasoning.We find that language models exhibit progressive attention shifts towards key information during reasoning, which implies essential clues for drawing conclusions.Building on this observation and analysis, we introduce a novel CoT distillation framework that transfers the teacher's stepwise attention on key information to the student model.This establishes structured guidance for the student's progressive concentration on key information during reasoning.More importantly, we develop a Mixture of Layers module enabling dynamic alignment that adapts to different layers between the teacher and student.Our method achieves consistent performance improvements across multiple mathematical and commonsense reasoning datasets.To our knowledge, it is the first method to leverage stepwise attention within CoT distillation to improve small model reasoning. Input Question(a) A sample from the SVAMP dataset.The distilled student model fails to adequately utilize numerical information, leading to erroneous results, whereas the teacher model, during stepwise reasoning, effectively utilizes all numerical information to arrive at the correct final result.

Semi-supervised text classification (SSTC) aims to train text classification models with few labeled data and massive unlabeled data.Existing studies develop effective pseudo-labeling methods, but they can struggle with unlabeled data that have imbalanced classes mismatched with the labeled data, making the pseudolabeling biased towards majority classes, resulting in catastrophic error propagation.We believe it is crucial to explicitly estimate the overall class distribution, and use it to calibrate pseudo-labeling to constrain majority classes.To this end, we formulate the pseudo-labeling as an optimal transport (OT) problem, which transports the unlabeled sample distribution to the class distribution.With a memory bank, we dynamically collect both the high-confidence pseudo-labeled data and true labeled data, thus deriving reliable (pseudo-) labels for class distribution estimation.Empirical results on 3 commonly used benchmarks demonstrate that our model is effective and outperforms previous state-of-the-art methods.

Large language model (LLM) role-playing has gained widespread attention.Authentic character knowledge is crucial for constructing realistic LLM role-playing agents.However, existing works usually overlook the exploration of LLMs' ability to detect characters' known knowledge errors (KKE) and unknown knowledge errors (UKE) while playing roles, which would lead to low-quality automatic construction of character trainable corpus.In this paper, we propose RoleKE-Bench to evaluate LLMs' ability to detect errors in KKE and UKE.The results indicate that even the latest LLMs struggle to detect these two types of errors effectively, especially when it comes to familiar knowledge.We experimented with various reasoning strategies and propose an agent-based reasoning method, Self-Recollection and Self-Doubt (S 2 RD), to explore further the potential for improving error detection capabilities.Experiments show that our method effectively improves the LLMs' ability to detect error character knowledge, but it remains an issue that requires ongoing attention 1 .* indicates corresponding author. 1 The resource is accessible at https://github.com/ WYRipple/rp_kw_errors.2 In this paper, "character" also refers to "role".

Document retrieval aims to find documents related to the query from all candidate documents. Existing studies develop the Generative Retrieval approach, which assigns a unique DocID to each document, and then measures document-query relevance based on the probability of generating the expected DocID for the given query. However, the generated DocID can have expressive limitation of different semantic topics, leading to semantic gaps in document retrieval. Besides, existing GR models usually suffer from the catastrophic forgetting when memorizing new documents incrementally, which makes real-world application impractical. To overcome these issues, we propose Translational Generative Retrieval, which translates each document into potential queries reflecting different topics, specifically by a sequence of token probability distributions. To better model the semantics of potential queries and effectively decode the target query from the distributions, we propose DirEcted Acyclic Graph Retrieval (DEAR) model, which reforms the distributions from a non-autoregressive generative model into a Directed Acyclic Graph. Experimental results demonstrate that DEAR outperforms existing retrieval models, setting a new state-of-the-art in generative retrieval.

Cross-domain recommendation (CDR) has demon-strated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a richly informative source domain, CDR could improve the recommendation performance for cold-start users in the target domain. Previous CDR approaches mostly adhere the Embedding and Mapping (EMCDR) paradigm, which learns a user-shared mapping function to transfer users' preference from the source domain to the target domain, neglecting users' personalized preference. Recent CDR approaches further leverage the meta-learning paradigm, considering the CDR task for each user independently and learning user-specific mapping functions for each user. However, they mostly learn representations for each user individually, which ignores the common preference between different users, neglecting valuable information for CDR. In addition, all these approaches usually summarize the user's preference into an overall representation, which can hardly capture the user's multi-interest preference. To this end, we propose a personalized multi-interest modeling framework for CDR to cold-start users, termed as NF-NPCDR. Specifically, we propose a personalized preference encoder that enhances the neural process (NP) with the normalizing flow (NF) to convert the Gaussian (unimodal) distribution to a multimodal distribution, providing a novel way to capture the user's personalized multi-interest preference. Then, we propose a common preference encoder with a preference pool to capture the common preference between different users. Furthermore, we introduce a stochastic adaptive decoder to incorporate both the personalized and common preference for cold-start users, adaptively modulating both preference for better recommendation. Experimental evalu-ations demonstrate that NF-NPCDR outperforms previous SOTA approaches in five benchmark CDR scenarios.

Transformer models encounter inefficiency when scaling hidden dimensions due to the uniform expansion of parameters. When delving into the sparsity of hidden dimensions, we observe that only a small subset of dimensions are highly activated, where some dimensions are commonly activated across tokens, and some others uniquely activated for individual tokens. To leverage this, we propose MoHD (Mixture of Hidden Dimensions), a sparse architecture that combines shared sub-dimensions for common features and dynamically routes specialized sub-dimensions per token. To address the potential information loss from sparsity, we introduce activation scaling and group fusion mechanisms. MoHD efficiently expands hidden dimensions with minimal computational increases, outperforming vanilla Transformers in both parameter efficiency and task performance across 10 NLP tasks. MoHD achieves 1.7% higher performance with 50% fewer activatied parameters and 3.7% higher performance with 3$\times$ total parameters expansion at constant activated parameters cost. MoHD offers a new perspective for scaling the model, showcasing the potential of hidden dimension sparsity.

Graph neural networks (GNNs) leverage message passing mechanisms to learn the topological features of graph data. Traditional GNNs learns node features in a spatial domain unrelated to the topology, which can hardly ensure topological features. In this paper, we formulates message passing as a system of hyperbolic partial differential equations (hyperbolic PDEs), constituting a dynamical system that explicitly maps node representations into a particular solution space. This solution space is spanned by a set of eigenvectors describing the topological structure of graphs. Within this system, for any moment in time, a node features can be decomposed into a superposition of the basis of eigenvectors. This not only enhances the interpretability of message passing but also enables the explicit extraction of fundamental characteristics about the topological structure. Furthermore, by solving this system of hyperbolic partial differential equations, we establish a connection with spectral graph neural networks (spectral GNNs), serving as a message passing enhancement paradigm for spectral GNNs.We further introduce polynomials to approximate arbitrary filter functions. Extensive experiments demonstrate that the paradigm of hyperbolic PDEs not only exhibits strong flexibility but also significantly enhances the performance of various spectral GNNs across diverse graph tasks.

Knowledge Graph Completion (KGC) is important in addressing the incompleteness of Knowledge Graphs (KGs) and supporting intelligent infrastructures. Recently, numerous methods have been developed for the utilization of Large Language Models (LLMs) to complete KGs in a textual generation manner. However, integrating KGs with LLMs presents multiple challenges. First, the output of LLMs is often unconstrained, and directly performing KGC with LLMs may generate entities beyond the scope of KGs and suffer from hallucination. Second, the inherent token length limitation of LLMs may hinder the integration of multi-aspect information, thereby restricting the effectiveness and efficiency in inference. Third, existing methods that integrate LLMs with KGs typically leverage partial aspects of KG contexts, overlooking the crucial role of multi-aspect information in prompting KGC. In fact, there exists crucial multi-aspect information of KG contexts that support the correctness of a factual triple, such as entity/relation descriptions, reasoning paths and entity neighbors. To this end, we propose a novel framework to explore the impact of multi-aspect information of KG contexts for KGC, termed as MAKGC. Particularly, given a target incomplete triple, MAKGC generates a list of candidate entities using an embedding model, incorporates the most relevant relation paths and entity descriptions as embeddings, and integrates them with structural embeddings into a set of instructions. In this way, the multi-aspect information facilitate LLMs in making accurate predictions. Extensive experiments demonstrate the effectiveness on benchmark datasets, and our model outperform previous competitive methods.

Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user representation in the target domain via the transfer of user preference existing in the source domain. Prior efforts mostly follow the embedding-and-mapping paradigm, which first integrate the preference into user representation in the source domain, and then perform a mapping function on this representation to the target domain. However, they focus on mapping features across domains, neglecting to explicitly model the preference integration process, which may lead to learning coarse user representation. Diffusion models (DMs), which contribute to more accurate user/item representations due to their explicit information injection capability, have achieved promising performance in recommendation systems. Nevertheless, these DMs-based methods cannot directly account for valuable user preference in other domains, leading to challenges in adapting to the transfer of preference for cold-start users. Consequently, the feasibility of DMs for CDR remains underexplored. To this end, we explore to utilize the explicit information injection capability of DMs for user preference integration and propose a Preference-Guided Diffusion Model for CDR to cold-start users, termed as DMCDR. Specifically, we leverage a preference encoder to establish the preference guidance signal with the user's interaction history in the source domain. Then, we explicitly inject the preference guidance signal into the user representation step by step to guide the reverse process, and ultimately generate the personalized user representation in the target domain, thus achieving the transfer of user preference across domains. Furthermore, we comprehensively explore the impact of six DMs-based variants on CDR. Extensive experiments on three real-world CDR scenarios demonstrate the superiority of our DMCDR over SOTA methods and six DMs-based variants.

Multi-Modal Entity Alignment (MMEA) aims to retrieve equivalent entities from different Multi-Modal Knowledge Graphs (MMKGs), a critical information retrieval task.Existing studies have explored various fusion paradigms and consistency constraints to improve the alignment of equivalent entities, while overlooking that the visual modality may not always contribute positively.Empirically, entities with low-similarity images usually generate unsatisfactory performance, highlighting the limitation of overly relying on visual features.We believe the model can be biased toward the visual modality, leading to a shortcut image-matching task.To address this, we propose a counterfactual debiasing framework for MMEA, termed CDMEA, which investigates visual modality bias from a causal perspective.Our approach aims to leverage both visual and graph modalities to enhance MMEA while suppressing the direct causal effect of the visual modality on model predictions.By estimating the Total Effect (TE) of both modalities and excluding the Natural Direct Effect (NDE) of the visual modality, we ensure that the model predicts based on the Total Indirect Effect (TIE), * Corresponding author.

Dynamics modeling has been introduced as a novel paradigm in message passing (MP) of graph neural networks (GNNs). Existing methods consider MP between nodes as a heat diffusion process, and leverage heat equation to model the temporal evolution of nodes in the embedding space. However, heat equation can hardly depict the wave nature of graph signals in graph signal processing. Besides, heat equation is essentially a partial differential equation (PDE) involving a first partial derivative of time, whose numerical solution usually has low stability, and leads to inefficient model training. In this paper, we would like to depict more wave details in MP, since graph signals are essentially wave signals that can be seen as a superposition of a series of waves in the form of eigenvector. This motivates us to consider MP as a wave propagation process to capture the temporal evolution of wave signals in the space. Based on wave equation in physics, we innovatively develop a graph wave equation to leverage the wave propagation on graphs. In details, we demonstrate that the graph wave equation can be connected to traditional spectral GNNs, facilitating the design of graph wave networks (GWNs) based on various Laplacians and enhancing the performance of the spectral GNNs. Besides, the graph wave equation is particularly a PDE involving a second partial derivative of time, which has stronger stability on graphs than the heat equation that involves a first partial derivative of time. Additionally, we theoretically prove that the numerical solution derived from the graph wave equation are constantly stable, enabling to significantly enhance model efficiency while ensuring its performance. Extensive experiments show that GWNs achieve state-of-the-art and efficient performance on benchmark datasets, and exhibit outstanding performance in addressing challenging graph problems, such as over-smoothing and heterophily. Our code is available at https://github.com/YueAWu/Graph-Wave-Networks.

Aim: The study aims to explore the synergistic effects between different herbal pairs and the original traditional Chinese medicine prescriptions by developing a model for analyzing compatibility correlation and synergy in prescriptions. Method: We have applied TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. Liuwei Dihuang prescription(LDP), formulated following the principle of "Three Tonics and Three Purgatives," was used as an example. An analysis model built via association network was applied to evaluate the relationship and synergistic impacts between the original prescription and three distinct drug pairs related to specific diseases. Results: Node similarities measured by the nearest-neighbor expansion network and shortest path expansion network between LDP and kidney yin deficiency disease network were 0.873 and 0.685, respectively. The average random walk correlation score between LDP and diseases showed a significant difference, suggesting a strong correlation with diseases. Among these, the herbal pair of Corni FructusMoutan Cortex(SZY-MDP) demonstrated node similarities of 0.742 and 0.433 with kidney yin deficiency on the nearest-neighbor expansion network and shortest path expansion network, respectively. Also, a significant difference was noted in the average random walk correlation score of this pair with diseases, signifying that this herb pair is most closely related to the original prescription and diseases. Simultaneously, within LDP, the SZY-MDP herbal pair showed the closest association with the original prescription concerning shared targets, active compounds, and disease targets. Notably, the most prominent KEGG signaling pathway for LDP intervention in hyperthyroidism was the regulation of adipocyte lipolysis, indicating its regulatory effect on body energy metabolism and lipid metabolism. Conclusion: Based on the analysis model focused on compatibility correlation and synergy, the "tonifying and purgative" drugs in LDP exhibit specific "correlation-synergy" effects on kidney yin deficiency.

Despite the notable progress achieved by large-scale vision-language pre-training models in a wide range of multi-modal tasks, their performance often falls short in image-text matching challenges that require an in-depth understanding of structured representations. For instance, when distinguishing between texts or images that are generally similar but have distinct structured knowledge (such as entities and relationships in text, or objects and object attributes in images), the model's capabilities are limited. In this paper, we propose a advancing Multi-modal Structured Knowledge Representation with synergistic hard negative samples (MSKR), thereby significantly improving the model's matching capability for such data. Specifically, our model comprises a structured knowledge-enhanced encoder designed to bolster the structured knowledge inherent in textual data, such as entities, their attributes, and the relationships among these entities as well as structured knowledge within images, focusing on elements like objects and their attributes. To further refine the model's learning process, we produce both image and text challenging negative samples. Extensive experimental evaluations on the Winoground, InpaintCOCO, and MSCOCO benchmark reveal that MSKR significantly outperforms the baseline model, showcasing marked improvements 2.66% on average in structured representation learning compared to the baseline. Moreover, general representation results illustrate that our model not only excels in structured representation learning but also maintains its proficiency in general representation learning.

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs (MMKGs), whose entities can be associated with relational triples and related images. Most previous studies treat the graph structure as a special modality, and fuse different modality information with separate uni-modal encoders, neglecting valuable relational associations in modalities. Other studies refine each uni-modal information with graph structures, but may introduce unnecessary relations in specific modalities. To this end, we propose a novel local-to-global interaction network for MMEA, termed as LoginMEA. Particularly, we first fuse local multi-modal interactions to generate holistic entity semantics and then refine them with global relational interactions of entity neighbors. In this design, the uni-modal information is fused adaptively, and can be refined with relations accordingly. To enrich local interactions of multi-modal entity information, we devise modality weights and low-rank interactive fusion, allowing diverse impacts and element-level feature interactions among modalities. To capture global interactions of graph structures, we adopt relation reflection graph attention networks, which fully capture relational associations between entities. Extensive experiments demonstrate superior results of our method over 5 cross-KG or bilingual benchmark datasets, indicating the effectiveness of capturing local and global interactions.

Web 3.0 is viewed as the next generation of the Internet, with the aim of establishing a decentralized network where users can control their digital identities and data. Due to its decentralization feature, blockchain has become a promising solution for secure data storage and retrieval for abundant de-centralized applications in Web 3.0. In this context, decentralized identifiers (DIDs) are rapidly emerging as a key infrastructure for blockchain-based Web 3.0. However, with more and more DIDs generated and stored on the blockchain, it is challenging to support efficient retrieval of DIDs with data integrity assurance. In this paper, we propose a novel authenticated DID retrieval system for blockchain-based Web 3.0. Specifically, a new authenticated data structure (ADS) with the corresponding data verification algorithm is designed to enable efficient retrieval and verification for both DID records and their historical updates. Theoretical analysis has been performed to prove the security and efficiency of our proposed system. We implement our system and conduct experiments to evaluate the performance. Experimental results demonstrate that our proposed scheme exhibits higher system efficiency compared to the baseline solution.

Graph contrastive learning aims to learn a representative model by maximizing the agreement between different views of the same graph. Existing studies usually allow multifarious noise in data augmentation, and suffer from trivial and inconsistent generation of graph views. Moreover, they mostly impose contrastive constraints on pairwise representations, limiting the structural correlations among multiple nodes. Both problems may hinder graph contrastive learning, leading to suboptimal node representations. To this end, we propose a novel graph contrastive learning framework, namely GCL-LS, via low-rank and sparse subspace decomposition. In particular, it decomposes node representations into low-rank and sparse components, preserving structural correlations and compressed features in the low-rank and sparse subspace, respectively. By contrasting the representations in the subspaces, it naturally disentangles low-quality noise in data augmentation, and captures structural correlations and substantial features of nodes in contrastive learning. Experimental results show that our method significantly improves downstream node classification accuracy, and further analysis demonstrates the effectiveness of the subspace decomposition in graph contrastive learning. The code is released at https://github.com/zhanggehang/GCL_LS.

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs), where entities can be associated with related images.Most existing studies rely heavily on the automatically learned multi-modal fusion modules, which may allow redundant information such as misleading clues in the generated entity representations, impeding the feature consistency of equivalent entities.To this end, we propose a variational framework for MMEA via information bottleneck, termed as IBMEA, by emphasizing alignment-relevant information while suppressing alignment-irrelevant information in entity representations.Specifically, we first develop multi-modal variational encoders that represent modal-specific features as probability distributions.Then, we propose four modal-specific information bottleneck regularizers to limit the misleading clues in the modal-specific entity representations.Finally, we propose a modal-hybrid information contrastive regularizer to integrate modal-specific representations and ensure the similarity of equivalent entities between MMKGs to achieve MMEA.We conduct extensive experiments on 2 cross-KG and 3 bilingual MMEA datasets.Experimental results demonstrate that our model consistently outperforms previous state-of-the-art methods, and also shows promising and robust performance especially in the low-resource and high-noise data scenarios.

Low-resource Complex Named Entity Recognition aims to detect entities with the form of any linguistic constituent under scenarios with limited manually annotated data. Existing studies augment the text through the substitution of same type entities or language modeling, but suffer from the lower quality and the limited entity context patterns within low-resource corpora. In this paper, we propose a novel data augmentation method E2DA from both exogenous and endogenous perspectives. As for exogenous augmentation, we treat the limited manually annotated data as anchors, and leverage the powerful instruction-following capabilities of Large Language Models (LLMs) to expand the anchors by generating data that are highly dissimilar from the original anchor texts in terms of entity mentions and contexts. As regards the endogenous augmentation, we explore diverse semantic directions in the implicit feature space of the original and expanded anchors for effective data augmentation. Our complementary augmentation method from two perspectives not only continuously expands the global text-level space, but also fully explores the local semantic space for more diverse data augmentation. Extensive experiments on 10 diverse datasets across various low-resource settings demonstrate that the proposed method excels significantly over prior state-of-the-art data augmentation methods.

Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain. Traditional CDR studies follow the embedding and mapping (EMCDR) paradigm, which transfers user representations from the source to target domain by learning a user-shared mapping function, neglecting the user-specific preference. Recent CDR studies attempt to learn user-specific mapping functions in meta-learning paradigm, which regards each user's CDR as an individual task, but neglects the preference correlations among users, limiting the beneficial information for user representations. Moreover, both of the paradigms neglect the explicit user-item interactions from both domains during the mapping process. To address the above issues, this paper proposes a novel CDR framework with neural process (NP), termed as CDRNP. Particularly, it develops the meta-learning paradigm to leverage user-specific preference, and further introduces a stochastic process by NP to capture the preference correlations among the overlapping and cold-start users, thus generating more powerful mapping functions by mapping the user-specific preference and common preference correlations to a predictive probability distribution. In addition, we also introduce a preference remainer to enhance the common preference from the overlapping users, and finally devises an adaptive conditional decoder with preference modulation to make prediction for cold-start users with items in the target domain. Experimental results demonstrate that CDRNP outperforms previous SOTA methods in three real-world CDR scenarios.

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for content analysis and various applications. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE byMultiModal representation learning withInformationBottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks.

Event extraction (EE) is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, EE based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This article fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain EE based on deep learning models. We introduce a new literature classification of current general domain EE research according to the task definition. Afterward, we summarize the paradigm and models of EE approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.

Graph contrastive learning (GCL) has recently achieved substantial advancements. Existing GCL approaches compare two different “views“ of the same graph in order to learn node/graph representations. The underlying assumption of these studies is that the graph augmentation strategy is capable of generating several different graph views such that the graph views are structurally different but semantically similar to the original graphs, and thus the ground-truth labels of the original and augmented graph/nodes can be regarded identical in contrastive learning. However, we observe that this assumption does not always hold. For instance, the deletion of a supernode within a social network can exert a substantial influence on the partitioning of communities for other nodes. Similarly, any perturbation to nodes or edges in a molecular graph will change the labels of the graph. Therefore, we believe that augmenting the graph, accompanied by an adaptation of the labels used for the contrastive loss, will facilitate the encoder to learn a better representation. Based on this idea, we propose ID-MixGCL, which allows the simultaneous interpolation of input nodes and corresponding identity labels to obtain soft-confidence samples, with a controllable degree of change, leading to the capture of fine-grained representations from self-supervised training on unlabeled graphs. Experimental results demonstrate that ID-MixGCL improves performance on graph classification and node classification tasks, as demonstrated by significant improvements on the Cora, IMDB-B, IMDB-M, and PROTEINS datasets compared to state-of-the-art techniques, by 3-29parcent absolute points. The dataset and source code can be found at: https://github.com/zhanggehang/ID-MIXGCL.

Document-level event factuality identification (DocEFI) is an important task in event knowledge acquisition, which aims to detect whether an event actually occurs or not from the perspective of the document. Unlike the sentence-level task, a document can have multiple sentences with different event factualities, leading to event factuality conflicts in DocEFI. Existing studies attempt to aggregate local event factuality by exploiting document structures, but they mostly consider textual components in the document separately, degrading complicated correlations therein. To address the above issues, this paper proposes a novel approach, namely UR-HAT, to improve DocEFI with uncertain relational hypergraph attention networks. Particularly, we reframe a document graph as a hypergraph, and establish beneficial n-ary correlations among textual nodes with relational hyperedges, which helps to globally consider local factuality features to resolve event factuality conflicts. To better discern the importance of event factuality features, we further represent textual nodes with uncertain Gaussian distributions, and propose novel uncertain relational hypergraph attention networks to refine textual nodes with the document hypergraph. In addition, we select factuality-related keywords as nodes to enrich event factuality features. Experimental results demonstrate the effectiveness of our proposed method, and outperforms previous methods on two widely used benchmark datasets.

Complex Knowledge Base Question Answering aims to answer a complex question over a Knowledge Base. A mainstream solution is based on information retrieval, which usually extracts a pivotal subgraph from entire Knowledge Base to locate candidate answers, and then determines the plausible answers with semantic matching between candidate answers and the question. However, such a paradigm can have two critical problems: 1) Complex Knowledge Base Question Answering can be sensitive to the subgraph, since a small subgraph may exclude the answers, while a large one may introduce a lot of noise; 2) directly deriving answers with semantic matching neglects the global topology in the Knowledge Base, which may limit the capability in answer reasoning. To tackle above challenges, we propose the Relation-Aware Subgraph Retrieval and Reasoning Network, where relations are emphasized to construct subgraphs and answer reasoning. Specifically, we present a Relation-Aware Subgraph Retrieval (RASR) method to initialize and prune subgraphs with the guidance of relation semantics. To compre-hensively understand the complex correlations between the question and candidate answers, we put forward a Relation-Aware Reasoning Network (RARN), which contains a text reasoning module focusing on the semantics understanding of the question and a graph reasoning module focusing on mining the topology between the topic entities and the answers. Experiments on two classical benchmark datasets show that our reasoning model outperforms the state-of-the-art results of Information Retrieval models. What's more, data statistical analysis on the subgraphs demonstrates the effectiveness of our proposed subgraph retrieval method.

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors.

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level event graph to associate sentence nodes of different types and adopting a document-awared graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a new schema of argument roles, with a type-awared parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks, especially at least 2.51% and 1.12% improvement of the event trigger identification and argument role classification sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods.

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from language models pre-trained on general-domain texts. Traditional event detection approaches primarily focus on the general domain and ignore these two problems in the power system domain. To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED , leveraging a semantic channel and a topological channel to enrich information interaction from short texts. Concretely, the semantic channel refines textual representations with semantic similarity, building the semantic information interaction among potential event-related words. The topological channel generates a relation-type-aware graph modeling word dependencies, and a word-type-aware graph integrating part-of-speech tags. To further reduce errors worsened by professional terminologies in type analysis, a type learning mechanism is designed for updating the representations of both the word type and relation type in the topological channel. In this way, the information sparsity and professional term occurrence problems can be alleviated by enabling interaction between topological and semantic information. Furthermore, to address the lack of labeled data in power systems, we built a Chinese event detection dataset based on electrical Power Event texts, named PoE . In experiments, our model achieves compelling results not only on the PoE dataset, but on general-domain event detection datasets including ACE 2005 and MAVEN.

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships.

Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it struggle for downstream applications. In this paper, we introduce a novel task, namely Event Causality Extraction (ECE), aiming to extract the cause-effect event causality pairs with their structured event information from plain texts. The ECE task is more challenging since each event can contain multiple event arguments, posing fine-grained correlations between events to decide the cause-effect event pair. Hence, we propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE. Further, we devise a event type-enhanced model architecture to realize the dual grid tagging scheme. Experiments demonstrate the effectiveness of our method, and extensive analyses point out several future directions for ECE.

Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of inter-est, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain. Previous CDR approaches mostly achieve the goal by following the Embedding and Mapping (EMCDR) idea which attempts to learn a mapping function to transfer the pre-trained user repre-sentations (embeddings) from the source domain into the target domain. However, they pre-train the user/item representations independently for each domain, ignoring to consider both domain interactions simultaneously. Therefore, the biased pre-trained representations inevitably involve the domain-specific information which may lead to negative impact to transfer information across domains. In this work, we consider a key point of the CDR task: what information needs to be shared across domains? To achieve the above idea, this paper utilizes the information bottleneck (IB) principle, and proposes a novel approach termed as CDRIB to enforce the representations encoding the domain-shared information. To derive the unbiased representations, we devise two IB regularizers to model the cross-domain/in-domain user-item interactions simultaneously and thereby CDRIB could consider both domain interactions jointly for de-biasing. With an additional contrastive information regularizer, CDRIB can also capture cross-domain user-user correlations. In this way, those regularizers encourage the representations to encode the domain-shared information, which has the capability to make recommendations in both domains directly. To the best of our knowledge, this paper is the first work to capture the domain-shared information for cold-start users via variational information bottleneck. Empirical experiments illustrate that CDRIB outperforms the state-of-the-art approaches on four real-world cross-domain datasets, demonstrating the effectiveness of adopting the information bottleneck for CDR.

Keyphrase extraction (KPE) aims to obtain a set of phrases from a document that can summarize the main content of the document.Recently, pre-trained language models (LMs), especially BERT and ELMo, have achieved remarkable success, presenting new state-of-the-art results in unsupervised KPE.However, current pre-trained LMs focus on building language modeling objectives to learn a general representation, ignoring the keyphrase-related knowledge.Intuitively, the joint embedding of the keyphrase set should tend to be close to that of the extracted document, and far from those of other documents.In this work, we propose a contrastive learning-based semantic representation task to further improve BERT for unsupervised KPE.Particularly, we design a doc-phrase attention module to generate joint semantic embedding of the keyphrase set as a positive sample and select other semantically similar documents as hard negative samples.In the prediction layer, we further add an accumulated self-attention module to calculate the final scores of candidate phrases.We compare with eight strong baselines, and evaluate our model on three publicly available datasets.Experimental results show that our model is effective and robust on both long and short documents.

In few-shot relational triple extraction (FS-RTE), one seeks to extract relational triples from plain texts by utilizing only few annotated samples. Recent work first extracts all entities and then classifies their relations. Such an entity-then-relation paradigm ignores the entity discrepancy between relations. To address it, we propose a novel task decomposition strategy, Relation-then-Entity, for FS-RTE. It first detects relations occurred in a sentence and then extracts the corresponding head/tail entities of the detected relations. To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relation-relevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities. Experimental results show that our model outperforms previous work by an absolute gain (18.98%, 28.85% in F1 in two few-shot settings).

Event detection (ED) is a pivotal task for information retrieval, which aims at identifying event triggers and classifying them into pre-defined event types. In real-world applications, events are usually annotated with numerous fine-grained types, which often arises long-tail type nature and co-occurrence event nature. Existing studies explore the event correlations without full utilization, which may limit the capability of event detection. This paper simultaneously incorporates both the type-level and instance-level event correlations, and proposes a novel framework, termed as CorED. Specifically, we devise an adaptive graph-based type encoder to capture instance-level correlations, learning type representations not only from their training data but also from their relevant types, thus leading to more informative type representations especially for the low-resource types. Besides, we devise an instance interactive decoder to capture instance-level correlations, which predicts event instance types conditioned on the contextual typed event instances, leveraging co-occurrence events as remarkable evidence in prediction. We conduct experiments on two public benchmarks, MAVEN and ACE-2005 dataset. Empirical results demonstrate the unity of both type-level and instance-level correlations, and the model achieves effectiveness performance on both benchmarks.

The cross-device user matching task is to identify the behavior-logs (i.e., behavior sequences) on multiple devices that belong to one real person. Due to its anonymous and long-term properties, most previous methods of learning behavior embeddings cannot effectively capture two important features in the sequences, namely high-order connections and long-range dependencies. To this end, we propose a novel framework called Two-tier Graph Contextual Embedding (TGCE) to solve the above problems simultaneously. In the first tier, we construct behavior evolutionary graphs (BEGs) for behavior sequences and design an order-preserving neighbor aggregation network to collectively model transitions of behaviors with their neighbors. As repeated behaviors can be grouped into single nodes, our model joints neighboring environments around behaviors in a collective way, and behavior embeddings can be enriched. In the second tier, we further build scaled shortcut graphs (SSGs) by refining BEGs with random walk-based edge addition, then a position-aware graph attention network is further imposed on SSGs to facilitate fast information propagation. As distant graph nodes can be directly connected by shortcut edges, we can further capture long-range dependencies. By stacking two graph tiers, our approach can obtain graph contextual embeddings for behaviors to further improve user matching. Experimental results on the benchmark dataset show that our model outperforms various baselines in the user matching task. Our code is released on https://github.com/13061051/TGCE_2021.

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.Typically, the wrongly labeled instances comprise numbers of incomplete and inaccurate annotation noise, while most prior denoising works are only concerned with one kind of noise and fail to fully explore useful information in the whole training set.To address this issue, we propose a robust learning paradigm named Self-Collaborative Denoising Learning (SCDL), which jointly trains two teacher-student networks in a mutuallybeneficial manner to iteratively perform noisy label refinery.Each network is designed to exploit reliable labels via self denoising, and two networks communicate with each other to explore unreliable annotations by collaborative denoising.Extensive experimental results on five real-world datasets demonstrate that SCDL is superior to state-of-the-art DS-NER denoising methods 1 .

Script event prediction (SEP) aims to choose a correct subsequent event from a candidate list, given a chain of ordered context events. Event representation learning has been proposed and successfully applied to this task. Most previous methods learning representations mainly focus on coarse-grained connections at event or chain level, while ignoring more fine-grained connections between events. Here we propose a novel framework which can enhance the representation learning of events by mining their connections at multiple granularity levels, including argument level, event level and chain level. In our method, we first employ a masked self-attention mechanism to model the relations between the components of events (i.e. arguments). Then, a directed graph convolutional network is further utilized to model the temporal or causal relations between events in the chain. Finally, we introduce an attention module to the context event chain, so as to dynamically aggregate context events with respect to the current candidate event. By fusing threefold connections in a unified framework, our approach can learn more accurate argument/event/chain representations, and thus leads to better prediction performance. Comprehensive experiment results on public New York Times corpus demonstrate that our model outperforms other state-of-the-art baselines. Our code is available in https://github.com/YueAWu/MCer.

Open Information Extraction (OIE), the task aimed at discovering all textual facts organized in the form of (subject, predicate, object) found within a sentence, has gained much attention recently. However, in some knowledge-driven applications such as question answering, we often have a target entity and hope to obtain its structured factual knowledge for better understanding, instead of extracting all possible facts aimlessly from the corpus. In this paper, we define a new task, namely Semi-Open Information Extraction (SOIE), to address this need. The goal of SOIE is to discover domain-independent facts towards a particular entity from general and diverse web text. To facilitate research on this new task, we propose a large-scale human-annotated benchmark called SOIED, consisting of 61,984 facts for 8,013 subject entities annotated on 24,000 Chinese sentences collected from the web search engine.

Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs.Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations.Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions.Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations.This will be more predictive for knowledge acquisition in the few-shot scenario.Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-theart results with different few-shot sizes.The source code is available at https://github.