Temporal Knowledge Graph Completion (TKGC) aims to predict missing entities or relations based on historical facts, thereby facilitating the understanding of dynamic system evolution and supporting downstream reasoning tasks. However, existing methods predominantly focus on modeling sequential and structural dependencies, often overlooking the rich semantic information embedded in entities and relations, as well as the higher-order interactions among them, which limits their ability to handle complex, evolving scenarios effectively. To address these limitations, we propose DSTAG, a novel TKGC approach based on a semantic tag-enhanced dual-graph convolutional network. Our method leverages large language models to generate contextualized semantic multi-tags for both entities and relations (e.g., ''political event,'' ''economic activity''), thereby enriching their semantic representations. Furthermore, we introduce a semantic tag representation mechanism that captures higher-order dependencies during the aggregation and propagation of semantic tag information across graphs. DSTAG adopts a dual-graph convolutional network architecture, where the relation graph convolution extracts semantic features between temporal relationships and injects this information into the entity graph convolution, enabling joint modeling of entities and relations. We evaluate DSTAG on three widely used TKG benchmarks: ICEWS14, ICEWS18, and ICEWS05-15. Experimental results show that DSTAG achieves substantial MRR improvements over state-of-the-art baselines by 8.64%, 9.81% and 4.56%, respectively.

Temporal knowledge graphs (TKGs) effectively capture the dynamic evolution of events over time, emerging as a critical driving force in the advancement of artificial intelligence. In recent years, temporal knowledge graph reasoning (TKGR) has garnered significant attention for its ability to address the intrinsic incompleteness of TKGs. Among various TKGR methods, reinforcement learning (RL)-based multi-hop reasoning stands out due to the decision-making capabilities and interpretability. However, existing multi-hop reasoning methods are predominantly designed for the transductive setting where test entities are observed during training, and they exhibit limited performance in the fully-inductive setting where training and test entities are entirely disjoint. Moreover, the sparse links of newly emerged unseen entities in TKGs hinder multi-hop reasoning methods from utilizing sufficient actions to construct multihop relational paths, ultimately impairing reasoning accuracy. To address these challenges, we propose ARLIE (Adaptive Reinforcement Learning with Inductive Embeddings), a novel method capable of conducting multi-hop reasoning in both fully-inductive and transductive settings over TKGs. Specifically, ARLIE consists of the following two key components. (1) A context-based inductive representation method generates finegrained embeddings for unseen entities by exploiting queryrelated contextual information. (2) After obtaining temporal evolution and semantic dependencies of unseen entities, an actionaugmented adaptive RL framework leverages diverse actions to infer missing elements step-by-step over TKGs. Finally, experimental results show that ARLIE surpasses state-of-the-art TKGR methods across both fully-inductive and transductive settings.

Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can predict user preferences and demands, they fall short in generating explicit recommendation paths and lack explainability. As a step beyond the above methods, recent advancements utilize reinforcement learning (RL) to find suitable items for a given user via explainable recommendation paths. However, the performance of these solutions is still limited by the following two points. (1) Lack of ability to capture contextual dependencies from neighboring information. (2) The excessive reliance on short recommendation paths due to efficiency concerns. To surmount these challenges, we propose a category-aware dual-agent reinforcement learning (CADRL) model for explainable recommendations over KGs. Specifically, our model comprises two components: (1) a category-aware gated graph neural network that jointly captures context-aware item representations from neighboring entities and categories, and (2) a dual-agent RL framework where two agents efficiently traverse long paths to search for suitable items. Finally, experimental results show that CADRL outperforms state-of-the-art models in terms of both effectiveness and efficiency on large-scale datasets.

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge-such as the progression of social network interactions and the unfolding of news events-is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity's relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks.

Abstract Recommender systems utilize data analysis and predictive algorithms to suggest relevant items to users, enhancing their experiences and engagements across various digital platforms, particularly in e-commerce. To obtain satisfactory representations of items and user preferences, many existing studies (multi-modal recommendation approaches) integrate diverse data (e.g., text and images) into the recommendation process to enhance item embeddings. However, the capability of these methods is restricted due to the following problems: (1) insufficient utilization of multi-modal information; (2) lack of deeper and more adequate insights from user-item interactions after multi-modal fusion, as well as the inability to uncover more intricate or hidden knowledge in the users’ modality preference. To address these problems, we propose HUMP, which Highlights Users’ Modality Preference for multi-modal recommender systems, featuring two key components: (1) a users’ modality preference guided data fusion module for integrating users’ modality preference into user and item representations which is more appropriate for recommendation scenarios; (2) a global representation enhancement module, designed to learn the deeper relationships of fused information and enhance the representations through a user-item layered heterogeneous graph. Experiments on real-world datasets demonstrate the superiority of our model over state-of-the-art baselines.

A multi-modal knowledge graph (MKG) includes triplets that consist of entities and relations and multi-modal auxiliary data. In recent years, multi-hop multi-modal knowledge graph reasoning (MMKGR) based on reinforcement learning (RL) has received extensive attention because it addresses the intrinsic incompleteness of MKG in an interpretable manner. However, its performance is limited by empirically designed rewards and sparse relations. In addition, this method has been designed for the transductive setting where test entities have been seen during training, and it works poorly in the inductive setting where test entities do not appear in the training set. To overcome these issues, we proposeTMR(Topology-awareMulti-hopReasoning), which can conduct MKG reasoning under inductive and transductive settings. Specifically, TMR mainly consists of two components. (1) The topology-aware inductive representation captures information from the directed relations of unseen entities, and aggregates query-related topology features in an attentive manner to generate the fine-grained entity-independent features. (2) After completing multi-modal feature fusion, the relation-augmented adaptive RL conducts multi-hop reasoning by eliminating manual rewards and dynamically adding actions. Finally, we construct new MKG datasets with different scales for inductive reasoning evaluation. Experimental results demonstrate that TMP outperforms state-of-the-art MKGR methods under both inductive and transductive settings.

Reinforcement learning (RL)-based multi-hop reasoning has become an interpretable way for knowledge graph reasoning owing to its persuasive explanations for the predicted results, but the reasoning performance of these methods drops significantly over few-shot relations (only contain few triplets). To address this problem, recent studies introduce meta-learning into RL-based reasoning methods. However, the performance of these studies is still limited due to the following points: (1) the overall reasoning accuracy is impaired due to the low reasoning accuracies over some hard relations; (2) the reasoning process becomes laborious and ineffective owing to the existence of noisy data; (3) the generalizability is negatively affected due to the lack of knowledge-sharing. To tackle these challenges, we propose a novel modelHMLSconsisting of two modulesHHML(HierarchicalHardness-awareMeta-reinforcementLearning) andHHS(HierarchicalHardness-awareSampling). Specifically,HHMLcontains the following two components: (1) a hardness-aware RL conducts multi-hop reasoning by training hardness-aware batches and reducing noise; (2) a knowledge-sharing meta-learning adapts to few-shot relations by exploiting common features in the hierarchical relation structure. The other moduleHHSgenerates hardness-aware batches from relation and relation-cluster levels. The experimental results demonstrate that this work notably outperforms the state-of-the-art approaches in few-shot scenarios.

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features; (2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction; (2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task.

Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary to reason out missing elements. Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability. As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning. However, the performance of RL-based TKG reasoning methods is limited due to: (1) lack of ability to capture temporal evolution and semantic dependence jointly; (2) excessive reliance on manually designed rewards. To overcome these challenges, we propose an adaptive reinforcement learning model based on attention mechanism (DREAM) to predict missing elements in the future. Specifically, the model contains two components: (1) a multi-faceted attention representation learning method that captures semantic dependence and temporal evolution jointly; (2) an adaptive RL framework that conducts multi-hop reasoning by adaptively learning the reward functions. Experimental results demonstrate DREAM outperforms state-of-the-art models on public datasets.

Knowledge graph (KG) reasoning is a significant method for KG completion. To enhance the explainability of KG reasoning, some studies adopt reinforcement learning (RL) to complete the multi-hop reasoning. However, RL-based reasoning methods are severely limited by few-shot relations (only contain few triplets). To tackle the problem, recent studies introduce meta-learning into RL-based methods to improve reasoning performance. However, the generalization abilities of their models are limited due to the problem of low reasoning accuracies over hard relations (e.g., language and title). To overcome this problem, we propose a novel model called THML (Two-level Hardness-aware Meta-reinforcement Learning). Specifically, the model contains the following two components: (1) A hardness-aware meta-reinforcement learning method is proposed to predict the missing element by training hardness-aware batches. (2) A two-level hardness-aware sampling is proposed to effectively generate new hardness-aware batches from relation level and relation-cluster level. The generalization ability of our model is significantly improved by repeating the process of these two components in an alternate way. The experimental results demonstrate that THML notably outperforms the state-of-the-art approaches in few-shot scenarios.