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- research-articleJuly 2024
CL&CD: Contrastive Learning and Cluster Description for Zero-Shot Relation Extraction
Knowledge-Based Systems (KNBS), Volume 293, Issue CJun 2024https://doi.org/10.1016/j.knosys.2024.111652AbstractZero-shot Relation Extraction (ZRE) is designed to identify new relations when the model is adapted to a new environment in a new domain. The majority of existing ZRE methods employ distant supervision for data labeling, which inevitably leads ...
- research-articleJuly 2024
MRSLN: A Multimodal Residual Speaker-LSTM Network to alleviate the over-smoothing issue for Emotion Recognition in Conversation
AbstractMultimodal Emotion Recognition in Conversation (ERC) plays a significant role in the field of human–computer intelligent interaction since it enables computers to perceive and infer the emotions expressed by the individuals, thereby intelligently ...
- research-articleJune 2024
Machine Learning-based Composition Analysis of Ancient Glass Objects
AIEE '24: Proceedings of the 2024 5th International Conference on Artificial Intelligence in Electronics EngineeringJanuary 2024, Pages 9–19https://doi.org/10.1145/3658835.3658838Ancient glass is one of the objects studied in archaeology, and the study of ancient Chinese glass has attracted much attention from scholars at home and abroad in recent years. Ancient glass is similar in appearance to exotic glass objects, but the ...
Thought Graph: Generating Thought Process for Biological Reasoning
- Chi-Yang Hsu,
- Kyle Cox,
- Jiawei Xu,
- Zhen Tan,
- Tianhua Zhai,
- Mengzhou Hu,
- Dexter Pratt,
- Tianlong Chen,
- Ziniu Hu,
- Ying Ding
WWW '24: Companion Proceedings of the ACM on Web Conference 2024May 2024, Pages 537–540https://doi.org/10.1145/3589335.3651572We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes. Our framework stands out for its ability to provide a deeper ...
- ArticleMay 2024
Continual Few-Shot Relation Extraction with Prompt-Based Contrastive Learning
AbstractContinual relation extraction (CRE) aims to continually learn new relations while maintaining knowledge of previous relations in the data streams. Recently, continual few-shot relation extraction (CFRE) is introduced, in which only the first step ...
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- research-articleApril 2024
Pure kernel graph fusion tensor subspace clustering under non-negative matrix factorization framework
Information Processing and Management: an International Journal (IPRM), Volume 61, Issue 2Mar 2024https://doi.org/10.1016/j.ipm.2023.103603AbstractCurrently, graph-based multi-kernel subspace clustering methods have achieved rich research results in dealing with nonlinear data structures. However, most of the methods still have the following two limitations: (1) the model optimization goal ...
Highlights- This paper proposes a multi-kernel subspace clustering algorithm — PKGT.
- We constructed pure local affinity feature graphs for each base kernel matrix.
- PKGT effectively avoids interference from kernel noise during the optimization ...
- research-articleNovember 2023
Anti‐occlusion person re‐identification via body topology information restoration and similarity evaluation
IET Computer Vision (CVI2), Volume 18, Issue 3April 2024, Pages 393–404https://doi.org/10.1049/cvi2.12256AbstractIn real‐world scenarios, pedestrian images often suffer from occlusion, where certain body features become invisible, making it challenging for existing methods to accurately identify pedestrians with the same ID. Traditional approaches ...
The authors have introduced a novel framework called body topology information generation and matching (BTIGM) for occluded person ReID tasks. To address the issue of spatial dislocation caused by traditional feature alignment methods, the authors ...
- research-articleOctober 2023
MGICL: Multi-Grained Interaction Contrastive Learning for Multimodal Named Entity Recognition
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementOctober 2023, Pages 639–648https://doi.org/10.1145/3583780.3614967Multimodal Named Entity Recognition (MNER) aims to combine data from different modalities (e.g. text, images, videos, etc.) for recognition and classification of named entities, which is crucial for constructing Multimodal Knowledge Graphs (MMKGs). ...
- ArticleSeptember 2023
Inductive Linear Probing for Few-Shot Node Classification
Social, Cultural, and Behavioral ModelingSep 2023, Pages 274–284https://doi.org/10.1007/978-3-031-43129-6_27AbstractMeta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing literature predominantly focuses on transductive few-shot node ...
- research-articleAugust 2023
Virtual Node Tuning for Few-shot Node Classification
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2023, Pages 2177–2188https://doi.org/10.1145/3580305.3599541Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base ...
- research-articleAugust 2023
Contrastive Meta-Learning for Few-shot Node Classification
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2023, Pages 2386–2397https://doi.org/10.1145/3580305.3599288Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. To tackle such a label shortage issue, existing works generally ...
- research-articleJune 2023
Projection-based coupled tensor learning for robust multi-view clustering
Information Sciences: an International Journal (ISCI), Volume 632, Issue CJun 2023, Pages 664–677https://doi.org/10.1016/j.ins.2023.03.072AbstractMulti-view clustering methods based on tensor learning have received extensive attention due to their ability to effectively mine high-order correlation information between views. However, the presence of noise and redundant ...
- research-articleMay 2023
Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 12Dec. 2023, Pages 12770–12784https://doi.org/10.1109/TKDE.2023.3272584Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based ...
- research-articleApril 2023
Graph Self-supervised Learning with Augmentation-aware Contrastive Learning
WWW '23: Proceedings of the ACM Web Conference 2023April 2023, Pages 154–164https://doi.org/10.1145/3543507.3583246Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning ...
- ArticleMarch 2023
Supervised Graph Contrastive Learning for Few-Shot Node Classification
Machine Learning and Knowledge Discovery in DatabasesSep 2022, Pages 394–411https://doi.org/10.1007/978-3-031-26390-3_24AbstractGraphs present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e.,...
- research-articleMarch 2023
Gait recognition based on 3D human body reconstruction and multi-granular feature fusion
The Journal of Supercomputing (JSCO), Volume 79, Issue 11Jul 2023, Pages 12106–12125https://doi.org/10.1007/s11227-023-05143-0AbstractGait recognition is a crucial video-based biometric approach that allows for the identification of pedestrians from the motion of their walk over a distance without direct contact. Despite significant advances in this field, most existing ...
- ArticleFebruary 2023
Iterative Deep Graph Learning with Local Feature Augmentation for Network Alignment
AbstractNetworks are structures that naturally capture relations between entities in different data sources and information systems. To establish the connections among different networks, the task of network alignment is proposed and intensively studied ...
- research-articleJanuary 2023
Node Importance Estimation with Multiview Contrastive Representation Learning
International Journal of Intelligent Systems (IJIS), Volume 20232023https://doi.org/10.1155/2023/5917750Node importance estimation is a fundamental task in graph analysis, which can be applied to various downstream applications such as recommendation and resource allocation. However, existing studies merely work under a single view, which neglects the rich ...
- short-paperOctober 2022
SpCQL: A Semantic Parsing Dataset for Converting Natural Language into Cypher
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementOctober 2022, Pages 3973–3977https://doi.org/10.1145/3511808.3557703The Neo4j query language Cypher enables efficient querying for graphs and has become the most popular graph database language. Due to its complexities, semantic parsing (similar to Text-to-SQL) that translates natural language queries to Cypher becomes ...