An Improved Approach to the Construction of Chinese Medical Knowledge Graph Based on CTD-BLSTM Model
IEEE Access, 2021 In the process of constructing the knowledge graph, entity recognition and relationship extraction are not only the most fundamental but also the most important tasks, and the effect of their model directly affects the final result of the graph.
Yang Wu, Xiyong Zhu, Yinan Zhu
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Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning [PDF]
The Web Conference, 2022 Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users’ preference over items by modeling the user-item interaction graphs.
Zihan Lin +3 more
semanticscholar +1 more source
Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation [PDF]
Computer Vision and Pattern Recognition, 2023 Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the ...
Mingjie Li +5 more
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A Persistent Naming System Based on Graph Transformation Rules
CSRN, 2018 M. Weinstein David +3 more
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Neighbor Contrastive Learning on Learnable Graph Augmentation [PDF]
AAAI Conference on Artificial Intelligence, 2023 Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph ...
X. Shen +4 more
semanticscholar +1 more source
DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021 With the new developments of natural language processing, increasing attention has been given to the task of Named Entity Recognition (NER). However, the vast majority of work focus on a small number of large-scale annotated datasets with a limited ...
Hoang Van Nguyen +2 more
semanticscholar +1 more source
Dictionary-based matching graph network for biomedical named entity recognition
Scientific Reports, 2023 Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER.
Yinxia Lou, Xun Zhu, Kai Tan
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Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data [PDF]
Neural Information Processing Systems, 2023 Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has immediate benefits for various graph learning tasks.
Xin Zheng +5 more
semanticscholar +1 more source
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer [PDF]
The Web Conference, 2023 Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc.
Qiheng Mao +3 more
semanticscholar +1 more source
A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation
Information, 2022 Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems. KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ...
Xi Liu, Rui Song, Yuhang Wang, Hao Xu
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