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学者姓名：汪璟玢
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Abstract ：
Temporal Knowledge Graph Completion (TKGC) aims to address the incompleteness issue present in Temporal Knowledge Graph (TKG). Existing methods for TKGC mainly fall into two categories: one is the method that combines temporal information with entity and relation representations, which makes it difficult to deal with complex temporal patterns, and the other is the method that uses Graph Neural Network (GNN) to capture the neighborhood structure, which usually focuses on single timestamps and ignores the interactions between different timestamps. To address these limitations, we propose a novel method called Dynamic Periodicity Perception and Multi-Graph Integration (DPPMI). DPPMI introduces Temporal Category Sampling strategy and Relation-Aware Graph Transformer module to effectively capture contextual information across different time points. To handle complex temporal dynamics, we introduce a novel period embedding method based on the prime. Furthermore, we introduce a specialized attention mechanism to dynamically perceive the significance of various period embeddings, enabling the model to effectively identify and capture complex temporal patterns. Experimental results show that our model improves the Mean Reciprocal Rank (MRR) on the ICEWS14, YAGO11k, and Wikidata12k datasets by around 3.9%, 10%, and 3.7%, respectively, compared to the state-of-the-art baseline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Graph embeddings Graph embeddings Knowledge graph Knowledge graph
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| GB/T 7714 | Wu, Yuwei , Ke, Xifan , He, Haoran et al. Dynamic Period Perception and Multi-graph Integration for Temporal Knowledge Graph Completion [C] . 2025 : 412-426 . |
| MLA | Wu, Yuwei et al. "Dynamic Period Perception and Multi-graph Integration for Temporal Knowledge Graph Completion" . (2025) : 412-426 . |
| APA | Wu, Yuwei , Ke, Xifan , He, Haoran , Zha, Xian , Wang, Jingbin . Dynamic Period Perception and Multi-graph Integration for Temporal Knowledge Graph Completion . (2025) : 412-426 . |
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Multimodal Knowledge Graph Completion (MMKGC) involves integrating information from various modalities, such as text and images, into traditional knowledge graphs to enhance their completeness and accuracy. This approach leverages the complementary nature of multimodal data to strengthen the expressive power of knowledge graphs, thereby achieving better performance in tasks like knowledge reasoning and information retrieval. However, knowledge graph completion models designed for the structural information of triples directly applied to the multimodal domain have led to suboptimal model performance. In response to this challenge, this study introduces a novel model called the Multimodal Knowledge Graph Completion Model Based on Modal Hierarchical Fusion (MHF). The MHF model employs a phased fusion strategy that initially learns from structured, visual, and textual modalities independently. Then, it combines structural embeddings with text and image data using a specially designed neural network fusion layer to see how the different types of data interact with each other. Additionally, the MHF model incorporates a semantic constraint layer with a Factor Interaction Regularizer, which enhances the model’s generalization ability by exploiting the semantic equivalence between the head and tail entities of triples. Experimental results on three real-world multimodal benchmark datasets demonstrate that the MHF model achieves excellent performance in link prediction tasks, surpassing the current state-of-the-art baselines, the average performance gain of MRR, Hit@1, and Hit@10 is greater than 5.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Data fusion Data fusion Graph embeddings Graph embeddings Knowledge graph Knowledge graph Semantics Semantics
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| GB/T 7714 | Zhang, Sirui , Huang, Hao , Lin, Xinyang et al. Multimodal Knowledge Graph Completion Model Based on Modal Hierarchical Fusion [C] . 2025 : 381-395 . |
| MLA | Zhang, Sirui et al. "Multimodal Knowledge Graph Completion Model Based on Modal Hierarchical Fusion" . (2025) : 381-395 . |
| APA | Zhang, Sirui , Huang, Hao , Lin, Xinyang , Zheng, Cuichun , Zheng, Zhibo , Wang, Jingbin . Multimodal Knowledge Graph Completion Model Based on Modal Hierarchical Fusion . (2025) : 381-395 . |
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The extrapolation task in the temporal knowledge graph has received increasing attention from scholars due to its wide range of practical application scenarios. At present, recurrent neural networks are currently widely used in temporal knowledge graph completion techniques. These networks are employed to depict the sequential pattern of entities and relations. However, as the sequence lengthens, some critical early information may become diluted. Prediction errors ensue in the completion task as a result. Furthermore, it is observed that existing temporal knowledge graph completion methods fail to account for the topological structure of relations, which leads to relation representations with essentially little distinction across different timestamps. In order to tackle the previously mentioned concern, our research introduces a Temporal Knowledge Graph Completion Method utilizing Sequence-Focus Patterns Representation Learning (SFP). This method contains two patterns: the Focus pattern and the Sequential pattern. In the SFP model, we developed a novel graph attention network called ConvGAT. This network efficiently distinguishes and extracts complex relation information, thereby enhancing the accuracy of entity representations that are aggregated in the Focus pattern and Sequential pattern. Furthermore we proposed RelGAT, a graph attention network that simulates the topological structure of relations. This enhances the precision of relation representations and facilitates the differentiation between relation embeddings generated at various timestamps in the Focus pattern. Utilizing a time-aware attention mechanism, the Focus pattern extracts vital information at particular timestamps in order to amplify the data that the Sequential pattern dilutes. On five distinct benchmark datasets, SFP significantly outperforms the baseline, according to a comprehensive series of experiments.
Keyword ：
Graph Attention Network Graph Attention Network Knowledge graph completion Knowledge graph completion Link prediction Link prediction Temporal knowledge graphs Temporal knowledge graphs
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| GB/T 7714 | Wang, Jingbin , Ke, Xifan , Zhang, Fuyuan et al. SFP: temporal knowledge graph completion based on sequence-focus patterns representation learning [J]. | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
| MLA | Wang, Jingbin et al. "SFP: temporal knowledge graph completion based on sequence-focus patterns representation learning" . | APPLIED INTELLIGENCE 55 . 6 (2025) . |
| APA | Wang, Jingbin , Ke, Xifan , Zhang, Fuyuan , Wu, Yuwei , Zhang, Sirui , Guo, Kun . SFP: temporal knowledge graph completion based on sequence-focus patterns representation learning . | APPLIED INTELLIGENCE , 2025 , 55 (6) . |
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Knowledge graph completion (KGC) aims to enhance the completeness and utility of knowledge graphs (KGs) by predicting and filling missing information. Existing methods primarily focus on structured representation learning, extracting low-dimensional embeddings of entities and relations to uncover and predict missing information in knowledge graphs. However, these methods often overlook entity type information and lack deep feature extraction capabilities. Inability to recognize the type information of entities may lead to poor embedding expression effects of entities, while insufficient deep feature extraction limits the model’s ability to understand complex relationships. To address these issues, this paper proposes a Knowledge Graph Completion with Entity Type-Aware and Deep Feature Extraction (TAFE). The model employs a Type-Aware Graph Attention Encoder (TA-GAT) to identify equivalence relations and model entity type information during graph context entity aggregation. Additionally, it incorporates a Deep Feature Extraction 3D Convolution Decoder (FE-Conv3D), using Gaussian function mapping techniques to capture deep feature information of entities and relations. The 3D convolutional kernels extract interaction and local features among embeddings, enhancing the model’s ability to capture details and understand complex relationships. Extensive experimental analysis demonstrates the effectiveness of TAFE in knowledge graph completion tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Keyword ：
Graph embeddings Graph embeddings Graph neural networks Graph neural networks Knowledge graph Knowledge graph
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| GB/T 7714 | Zhang, Fuyuan , You, Changkai , Lin, Xinyang et al. Knowledge Graph Completion with Entity Type-Aware and Deep Feature Extraction [C] . 2025 : 396-411 . |
| MLA | Zhang, Fuyuan et al. "Knowledge Graph Completion with Entity Type-Aware and Deep Feature Extraction" . (2025) : 396-411 . |
| APA | Zhang, Fuyuan , You, Changkai , Lin, Xinyang , Zheng, Cuichun , Zhang, Yumeng , Wang, Jingbin . Knowledge Graph Completion with Entity Type-Aware and Deep Feature Extraction . (2025) : 396-411 . |
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Temporal knowledge graphs completion (TKGC) is a critical task that aims to forecast facts that will occur in future timestamps. It has attracted increasing research interest in recent years. Among the many approaches, reinforcement learning-based methods have gained attention due to their efficient performance and interpretability. However, these methods still face two challenges in the prediction task. First, a single policy network lacks the capability to capture the dynamic and static features of entities and relationships separately. Consequently, it fails to evaluate candidate actions comprehensively from multiple perspectives. Secondly, the composition of the action space is incomplete, often guiding the agent towards distant historical events and missing the answers in recent history. To address these challenges, this paper proposes a Temporal Knowledge Graph Completion Based on a Multi-Policy Network(MPNet). It constructs three policies from the aspects of static entity-relation, dynamic relationships, and dynamic entities, respectively, to evaluate candidate actions comprehensively. In addition, this paper creates a more diverse action space that guides the agent in investigating answers within historical subgraphs more effectively. The effectiveness of MPNet is validated through an extrapolation setting, and extensive experiments conducted on three benchmark datasets demonstrate the superior performance of MPNet compared to existing state-of-the-art methods.
Keyword ：
Knowledge graph completion Knowledge graph completion Link prediction Link prediction Reinforcement learning Reinforcement learning Temporal knowledge graphs Temporal knowledge graphs
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| GB/T 7714 | Wang, Jingbin , Wu, Renfei , Wu, Yuwei et al. MPNet: temporal knowledge graph completion based on a multi-policy network [J]. | APPLIED INTELLIGENCE , 2024 , 54 (3) : 2491-2507 . |
| MLA | Wang, Jingbin et al. "MPNet: temporal knowledge graph completion based on a multi-policy network" . | APPLIED INTELLIGENCE 54 . 3 (2024) : 2491-2507 . |
| APA | Wang, Jingbin , Wu, Renfei , Wu, Yuwei , Zhang, Fuyuan , Zhang, Sirui , Guo, Kun . MPNet: temporal knowledge graph completion based on a multi-policy network . | APPLIED INTELLIGENCE , 2024 , 54 (3) , 2491-2507 . |
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Accurate monitoring of urban waterlogging contributes to the city's normal operation and the safety of residents' daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city's global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity.
Keyword ：
active learning active learning graph convolutional network graph convolutional network route selection route selection sparse crowdsensing sparse crowdsensing waterlogging prediction waterlogging prediction
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| GB/T 7714 | Wang, Jingbin , Zhang, Weijie , Yu, Zhiyong et al. Route selection for opportunity-sensing and prediction of waterlogging [J]. | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (4) . |
| MLA | Wang, Jingbin et al. "Route selection for opportunity-sensing and prediction of waterlogging" . | FRONTIERS OF COMPUTER SCIENCE 18 . 4 (2024) . |
| APA | Wang, Jingbin , Zhang, Weijie , Yu, Zhiyong , Huang, Fangwan , Zhu, Weiping , Chen, Longbiao . Route selection for opportunity-sensing and prediction of waterlogging . | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (4) . |
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Link prediction in open knowledge graphs (OpenKGs) is crucial for applications like question answering and recommendation systems. Existing OpenKG models leverage the semantic information of noun phrases (NPs) to enhance the performance in the link prediction task. However, these models only extract superficial semantic information from NPs, ignoring the fact that an NP possesses diverse semantics. Furthermore, these models have not fully exploited the semantic information of the relation phrases (RPs). To address these issues, we propose a model for link prediction called Open Knowledge Graph Link Prediction with Semantic -Aware Embedding (SeAE). First, we develop an adaptive disentanglement embedding (ADE) mechanism to learn the intrinsically abundant semantics of NPs. The ADE mechanism can adaptively calculate the embedding segmentation number according to the dataset and has an ingenious method for updating embeddings. Second, we integrate the attention mechanism into the GRU encoder to obtain the distribution of importance inside RP, facilitating a more comprehensive capture of the RP's semantic information and enhancing the model's interpretability. Finally, we design a relation gate, which extracts the RP semantic features of tail NP from the shared edge. This gate realizes the relation constraints on entities while enhancing the interaction between entities and relations. Extensive experiments on four benchmarks demonstrate that SeAE outperforms the state-of-the-art models, resulting in improvements of approximately 5.4% and 7.4% in MRR on ReVerb45K and ReVerb45KF datasets respectively.
Keyword ：
Attention mechanism Attention mechanism Knowledge graph embedding Knowledge graph embedding Link prediction Link prediction Open knowledge graph Open knowledge graph Semantic-aware Semantic-aware
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| GB/T 7714 | Wang, Jingbin , Huang, Hao , Wu, Yuwei et al. Open Knowledge Graph Link Prediction with Semantic-Aware Embedding [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
| MLA | Wang, Jingbin et al. "Open Knowledge Graph Link Prediction with Semantic-Aware Embedding" . | EXPERT SYSTEMS WITH APPLICATIONS 249 (2024) . |
| APA | Wang, Jingbin , Huang, Hao , Wu, Yuwei , Zhang, Fuyuan , Zhang, Sirui , Guo, Kun . Open Knowledge Graph Link Prediction with Semantic-Aware Embedding . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 249 . |
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Temporal Knowledge Graphs (TKGs), represented by quadruples, describe facts with temporal relevance. Temporal Knowledge Graph Completion (TKGC) aims to address the incompleteness issue of TKGs and has received extensive attention in recent years. Previous approaches treated timestamps as a single node, resulting in incomplete parsing of temporal information and the inability to perceive temporal hierarchies and periodicity. To tackle this problem, we propose a novel model called Time Split Network (TSN). Specifically, we employed a unique approach to handle temporal information by splitting timestamps. This allows the model to perceive temporal hierarchies and periodicity, while reducing the number of model parameters. Additionally, we combined convolutional neural networks with stepwise fusion of temporal features to simulate the hierarchical order of time and obtain comprehensive temporal information. The experimental results of entity link prediction on the four benchmark datasets demonstrate the superiority of the TSN model. Specifically, compared to the state-of-the-art baseline, TSN improves the MRR by approximately 2.6% and 1.3% on the ICEWS14 and ICEWS05-15 datasets, and improves the MRR by approximately 33.5% and 34.6% on YAGO11k and Wikidata12k, respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Knowledge graph Knowledge graph
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| GB/T 7714 | You, Changkai , Lin, Xinyu , Wu, Yuwei et al. Time Split Network for Temporal Knowledge Graph Completion [C] . 2024 : 333-347 . |
| MLA | You, Changkai et al. "Time Split Network for Temporal Knowledge Graph Completion" . (2024) : 333-347 . |
| APA | You, Changkai , Lin, Xinyu , Wu, Yuwei , Zhang, Sirui , Zhang, Fuyuan , Wang, Jingbin . Time Split Network for Temporal Knowledge Graph Completion . (2024) : 333-347 . |
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Current research on knowledge graphs focuses mostly on static knowledge graphs while ignoring temporal information. Recently, people have begun to study the temporal knowledge graph, which integrates temporal information into KGC, so that the modeling is constantly changing with the knowledge that evolves over time. In this survey, we summarize the existing temporal knowledge graph research, which is divided into extrapolation tasks and interpolation tasks according to time. The extrapolation task is mainly used to predict future facts and consists of three models: Temporal Point Process, Time Series, and other models. The interpolation task extends the existing KGC models to complement the lack of past temporal information, including five models: Translational Distance, Semantic Matching, Neural, Relational Rotation, and Hyperbolic Geometric models.
Keyword ：
Knowledge completion Knowledge completion Knowledge embedding Knowledge embedding Temporal knowledge graph Temporal knowledge graph
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| GB/T 7714 | Chen, Sulin , Wang, Jingbin . A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks [J]. | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 , 2023 , 153 : 1002-1014 . |
| MLA | Chen, Sulin et al. "A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks" . | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 153 (2023) : 1002-1014 . |
| APA | Chen, Sulin , Wang, Jingbin . A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks . | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 , 2023 , 153 , 1002-1014 . |
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Knowledge graph completion (KGC) has been widely explored, but the task of temporal knowledge graph completion (TKGC) for predicting future events is far from perfection. Some embedding-based approaches have achieved significant results on the TKGC task by modeling the structural information of each temporal snapshot and the evolution between temporal snapshots. However, due to the uneven distribution of data in knowledge graphs (KGs), models that only utilize local structure and time series information suffer from information sparsity, resulting in some entities failing to obtain a better embedding representation due to less available information. Moreover, existing methods usually do not distinguish between the time span and frequency of historical information, which reduces the performance of link prediction. For this reason, we propose the G lobal and L ocal Information-A ware Net work (GL-ANet) to capture both global and local information. In particular, to model global information, we capture global structural information of entities across time using a global neighborhood aggregator to enrich the representation of entities; global historical information is obtained based on the frequency and time span of historical facts, focusing on recent and frequent events rather than all historical events to suggest the performance of link prediction; to model local information, we propose a two-layer attention network to capture local structural information at each timestamp, using a gating mechanism and GRU to capture local evolution information. Extensive experiments demonstrate the effectiveness of our model, achieving significant improvements and outperforming state-of-the-art models on five benchmark datasets.
Keyword ：
Global information Global information Link prediction Link prediction Local information Local information Neighborhood aggregator Neighborhood aggregator Temporal knowledge graph Temporal knowledge graph
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| GB/T 7714 | Wang, Jingbin , Lin, Xinyu , Huang, Hao et al. GLANet: temporal knowledge graph completion based on global and local information-aware network [J]. | APPLIED INTELLIGENCE , 2023 , 53 (16) : 19285-19301 . |
| MLA | Wang, Jingbin et al. "GLANet: temporal knowledge graph completion based on global and local information-aware network" . | APPLIED INTELLIGENCE 53 . 16 (2023) : 19285-19301 . |
| APA | Wang, Jingbin , Lin, Xinyu , Huang, Hao , Ke, Xifan , Wu, Renfei , You, Changkai et al. GLANet: temporal knowledge graph completion based on global and local information-aware network . | APPLIED INTELLIGENCE , 2023 , 53 (16) , 19285-19301 . |
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