1. Knowledge graph alignment network with node-level strong fusionShuang Liu, Man Xu, Yufeng Qin, Niko Lukač, 2022, original scientific article Abstract: Entity alignment refers to the process of discovering entities representing the same object
in different knowledge graphs (KG). Recently, some studies have learned other information about
entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a
novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects:
structure, attribute, relation and names. The attribute information and name information are learned
first, then structure information is learned based on these two aspects of information through graph
convolutional network (GCN), the alignment signals from attribute and name are already carried
at the beginning of the learning structure. In the process of continuous propagation of multi-hop
neighborhoods, the effect of strong fusion of structure, attribute and name information is achieved
and the more meticulous entity representations are obtained. Additionally, through the continuous
interaction between sub-alignment tasks, the effect of entity alignment is enhanced. An iterative
framework is designed to improve performance while reducing the impact on pre-aligned seed pairs.
Furthermore, extensive experiments demonstrate that the model improves the accuracy of entity
alignment and significantly outperforms 13 previous state-of-the-art methods.
Keywords: knowledge graph, entity ealignment, graph convolutional network, knowledge fusion
Published in DKUM: 27.03.2025; Views: 0; Downloads: 4
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2. A hierarchical knowledge graph embedding framework for link predictionShuang Liu, Chengwang Hou, Jiana Meng, Peng Chen, Simon Kolmanič, 2024, original scientific article Keywords: knowledge graph embedding, knowledge graph completion, negative sampling, link prediction
Published in DKUM: 04.02.2025; Views: 0; Downloads: 3
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3. Improved relation extraction through key phrase identification using community detection on dependency treesShuang Liu, Xunqin Chen, Jiana Meng, Niko Lukač, 2025, original scientific article Abstract: A method for extracting relations from sentences by utilizing their dependency trees to identify key phrases is presented in this paper. Dependency trees are commonly used in natural language processing to represent the grammatical structure of a sentence, and this approach builds upon this representation to extract meaningful relations between phrases. Identifying key phrases is crucial in relation extraction as they often indicate the entities and actions involved in a relation. The method uses community detection algorithms on the dependency tree to identify groups of related words that form key phrases, such as subject-verb-object structures. The experiments on the Semeval-2010 task8 dataset and the TACRED dataset demonstrate that the proposed method outperforms existing baseline methods.
Keywords: community detection algorithms, dependency trees, relation extraction
Published in DKUM: 17.01.2025; Views: 0; Downloads: 5
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5. CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel TrajectoriesPeng Chen, Shuang Liu, Niko Lukač, 2023, original scientific article Abstract: How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.
Keywords: AIS Data, CNN, Dynamic Rammer-Douglas-Peucker, knowledge discovery, maneuvering pattern, traffic pattern, trajectory
Published in DKUM: 19.03.2024; Views: 538; Downloads: 417
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6. A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation NetworkShuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanič, 2023, original scientific article Abstract: With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. However, Transformers were originally designed for NLP and ignore the multidimensional nature of images. In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. We propose two attention mechanisms, namely MSC-Attention and LKA-Cross-Attention, for capturing low-level features and promoting global information interaction. MSC-Attention is designed for multi-scale channel attention, while LKA-Cross-Attention is a cross-attention mechanism based on the large kernel attention (LKA). Extensive experiments show that our method outperforms current mainstream methods in evaluation metrics such as Dice coefficient and Hausdorff 95 coefficient.
Keywords: CNNs, deep learning, medical image processing, NLP, semantic segmentation
Published in DKUM: 14.03.2024; Views: 496; Downloads: 311
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7. Knowledge Graph Completion with Triple Structure and Text RepresentationShuang Liu, Yufeng Qin, Man Xu, Simon Kolmanič, 2023, original scientific article Abstract: Knowledge Graphs (KGs) describe objective facts in the form of RDF triples, each triple contains sufficient semantic information and triple structure information. Knowledge Graph Completion (KGC) is to acquire new knowledge by predicting hidden relationships between entities and adding the new knowledge to the KG. At present, the mainstream KGC approaches only applied the triple structure information or only utilized the semantic information of the text. This paper proposes an approach (TSTR) using BERT and deep neural networks to fully extract the semantic information of knowledge, and designs an aggregated re-ranking scheme that incorporates existing graph embedding approach to learn the structural information of triples. In experiments, the approach achieves state-of-the-art performance on three benchmark datasets, and outperforms recent KGC approaches on sparsely connected datasets.
Keywords: knowledge graph completion, BERT, deep convolutional architecture, re-ranking
Published in DKUM: 19.02.2024; Views: 229; Downloads: 21
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8. Research on vehicle re-identification algorithm based on fusion attention methodPeng Chen, Shuang Liu, Simon Kolmanič, 2023, original scientific article Abstract: The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details-the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image's attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm's effectiveness.
Keywords: vehicle re-identification, attention mechanism, key-point, local feature, feature fusion
Published in DKUM: 06.02.2024; Views: 348; Downloads: 30
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