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学者姓名：汪璟玢
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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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近年来利用图结构来解决知识图补全(KGC)问题取得了不错的进展，其中图神经网络(GNNs)通过聚合实体的局部邻域信息来不断更新中心实体的表示，图注意力网络(GATs)使用注意力机制有侧重地聚合邻居，以获得更准确的中心实体表示。这些模型虽然在KGC中取得了不错的性能，但它们都忽略了中心实体的类型信息，仅仅使用邻域信息来计算注意力，将导致计算出来的注意力不够精准。针对这些问题，文中提出了一种类型匹配约束的图注意力网络(TMGAT),该方法通过计算中心实体类型对每个邻域关系的注意力，来得到实体类型-关系级别的注意力，以进一步计算出中心实体与各邻域关系的类型匹配度，再通过邻域关系及对应的邻居实体，结合类型匹配度计算实体-关系级别的注意力，得到邻域节点对中心实体的最终注意力。使用类型匹配度来约束传统的注意力机制，提升注意力机制的准确性，得到更加精准的中心实体嵌入，进而提升知识图补全的准确性。截至目前，文中提出的TMGAT是第一个在GATs中结合显式类型进行知识图补全任务的模型。文中加工了两个现有的数据集，使数据集中每个实体都拥有若干个类型，以验证TMGAT模型的性能。最后，实验部分展现了TMGAT在知识补全任务中优秀的竞争力，并研究了类型个数对模型性能的影响。
Keyword ：
图注意力机制 图注意力机制 图结构 图结构 知识图补全 知识图补全 知识图谱 知识图谱 类型信息 类型信息
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| GB/T 7714 | 孙首男 , 汪璟玢 , 吴仁飞 et al. TMGAT:类型匹配约束的图注意力网络 [J]. | 计算机科学 , 2024 , 51 (03) : 235-243 . |
| MLA | 孙首男 et al. "TMGAT:类型匹配约束的图注意力网络" . | 计算机科学 51 . 03 (2024) : 235-243 . |
| APA | 孙首男 , 汪璟玢 , 吴仁飞 , 游常凯 , 柯禧帆 , 黄皓 . TMGAT:类型匹配约束的图注意力网络 . | 计算机科学 , 2024 , 51 (03) , 235-243 . |
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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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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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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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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 (KG) is a structural way to represent knowledge. Many applications in the industry rely on KG, such as recommendation systems, relationship extraction, and question answering. However, most existing knowledge graphs are incomplete, so tackling KG completion becomes a crucial problem. Knowledge Graph Embedding (KGE) is an effective method for KG completion. Based on the literature published in recent years, we review existing KGE methods, including traditional approaches and approaches exploiting external information. Traditional methods only utilize triplet information and ignore the more informative external information. Therefore, our work mainly focuses on the methods that utilize external information, including textual description, relation paths, neighborhood information, entity types, and temporal information. Experimental results show that methods exploiting external information generally outperform traditional methods.
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| GB/T 7714 | Chen, Yuxuan , Wang, Jingbin . A Review Focusing on Knowledge Graph Embedding Methods Exploiting External Information [J]. | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 , 2023 , 153 : 869-887 . |
| MLA | Chen, Yuxuan et al. "A Review Focusing on Knowledge Graph Embedding Methods Exploiting External Information" . | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 153 (2023) : 869-887 . |
| APA | Chen, Yuxuan , Wang, Jingbin . A Review Focusing on Knowledge Graph Embedding Methods Exploiting External Information . | ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022 , 2023 , 153 , 869-887 . |
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In this paper, we study the learning representation of entities and relationships in the link prediction task of knowledge graph. The knowledge graph is a collection of factual triples, but most of them are incomplete. At present, some models use complex rotation to model triples, and obtain more effective results. However, such models generally use specific structures to learn the representation of entities or relationships, and do not make full use of the context information of entities and relations. In addition, in order to achieve high performance, models often need larger embedding dimensions and more epoches, which will cause large time and space cost. To systematically tackle these problems, we develop a novel knowledge graph embedding method, named CAQuatE. We propose two concepts to select valuable context information, then design a context information encoder to enhance the original embedding, and finally use quaternion multiplication to model triples. The experiment and results on two common benchmark datasets show that CAQuatE can significantly outperform the existing state-of-the-art model in the knowledge graph completion task by obtaining lower dimensional representation vectors with fewer epoches and no additional parameters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
knowledge graph knowledge graph knowledge graph completion knowledge graph completion link prediction link prediction quaternion quaternion
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| GB/T 7714 | Wang, J. , Yang, X. , Ke, X. et al. Context-Aware Quaternion Embedding for Knowledge Graph Completion [未知]. |
| MLA | Wang, J. et al. "Context-Aware Quaternion Embedding for Knowledge Graph Completion" [未知]. |
| APA | Wang, J. , Yang, X. , Ke, X. , Wu, R. , Guo, K. . Context-Aware Quaternion Embedding for Knowledge Graph Completion [未知]. |
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现有的时间知识图谱补全模型仅考虑四元组自身的结构信息,忽略了实体隐含的邻居信息和关系对实体的约束,导致模型在时态知识图谱补全任务上表现不佳.此外,一些数据集在时间上呈现不均衡的分布,导致模型训练难以达到一个较好的平衡点.针对这些问题,提出了一个基于关系约束的上下文感知模型(CARC).CARC通过自适应时间粒度聚合模块来解决数据集在时间上分布不均衡的问题,并使用邻居聚合器将上下文信息集成到实体嵌入中,以增强实体的嵌入表示.此外,设计了四元组关系约束模块,使具有相同关系约束的实体嵌入彼此相近,不同关系约束的实体嵌入彼此远离,以进一步增强实体的嵌入表示.在多个公开的时间数据集上进行了大量实验,实验结果证明了所提模型的优越性.
Keyword ：
关系约束 关系约束 时间区间预测 时间区间预测 时间知识图谱 时间知识图谱 时间粒度 时间粒度 邻居信息 邻居信息 链路预测 链路预测
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| GB/T 7714 | 汪璟玢 , 赖晓连 , 林新宇 et al. 基于关系约束的上下文感知时态知识图谱补全 [J]. | 计算机科学 , 2023 , 50 (3) : 23-33 . |
| MLA | 汪璟玢 et al. "基于关系约束的上下文感知时态知识图谱补全" . | 计算机科学 50 . 3 (2023) : 23-33 . |
| APA | 汪璟玢 , 赖晓连 , 林新宇 , 杨心逸 . 基于关系约束的上下文感知时态知识图谱补全 . | 计算机科学 , 2023 , 50 (3) , 23-33 . |
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