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arXiv 2025年

作者： Zhang, Jian Wei, Bifan Qi, Shihao Zhu, Haiping Liu, Jun Lin, Qika School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China National University of Singapore Singapore

The construction of Generalized knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typically construct these types of graph separately, overlooking holistic insights and potential unification that could be beneficial in computing resources and usage perspectives. However, a key challenge in developing a unified framework for GKG is obstacles arising from task-specific differences. In this study, we propose a unified framework for constructing generalized knowledge graphs to address this challenge. First, we collect data from 15 sub-tasks in 29 datasets across the three types of graphs, categorizing them into in-sample, counter-task, and out-of-distribution (OOD) data. Then, we propose a three-stage curriculum learning fine-tuning framework, by iteratively injecting knowledge from the three types of graphs into the Large Language Models. Extensive experiments show that our proposed model improves the construction of all three graph types across in-domain, OOD and counter-task data. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering 12

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Yang, Ze Tai, Changyang Wu, Gongqing Zhang, Zan Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance. © 2021 IEEE.

关键词： data mining

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DAGKT: Difficulty and Attempts Boosted Graph-based knowledge Tracing

arXiv

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arXiv 2022年

作者： Luo, Rui Liu, Fei Liang, Wenhao Zhang, Yuhong Bu, Chenyang Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology China Jianzai Tech Hefei China

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students’ mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions’ difficulties and students’ attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students’ different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT)3, using rich information from students’ records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT. Copyright © 2022, The Authors. All rights reserved.

关键词： Students

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification 12

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Tai, Changyang Yang, Ze Zhang, Huicheng Wu, Gongqing Lv, Junwei Bao, Xianyu Key Laboratory of Knowledge Engineering With Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438582

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input. © 2021 IEEE.

关键词： data mining

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Flavours of Convolution for Unsupervised Aspect Extraction and Aspect-based Sentiment Analysis 7

Flavours of Convolution for Unsupervised Aspect Extraction a...

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7th Workshop on Natural Language for Artificial Intelligence, NL4AI 2023

作者： Chatterjee, Soumick Prakash, Sowmya Nürnberger, Andreas Genomics Research Centre Human Technopole Viale Rita Levi-Montalcini 1 Milan20157 Italy Faculty of Computer Science Otto von Guericke University Magdeburg Universitätspl. 2 Magdeburg39106 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Universitätspl. 2 Magdeburg39106 Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Sentiment analysis, the task of understanding the underlying sentiment from a given data, is frequently used in various fields, from market research to recommender systems. To analyse different sentiments for different aspects present in a given sentence, aspect-based sentiment analysis (ABSA) approaches have been proposed. The task of ABSA can be divided into two subtasks: aspect extraction (AE) and aspect-based polarity detection (ABPD). Most of the state-of-the-art approaches are based on some form of neural networks using convolutional layers. Recently, flavours of convolution, like extremely separated convolution layer (XSepConv) - which can reduce computational cost along with the parameter size of large kernels and depth-wise over-parameterised convolutional layer (DOConv) - which can improve the training efficiency, have been proposed. They have shown their superior capabilities when it comes to image-related tasks, but they have not been explored for textual tasks like ABSA. This paper performs unsupervised AE and weakly supervised ABSA using those specialised convolutional layers. It could be shown that using such layers instead of traditional convolutional layers can significantly improve the performance of the model. © 2023 Copyright for this paper by its authors.

关键词： Extraction

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data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big data System, data Mining, and Closed-Loop Technologies

arXiv

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arXiv 2024年

作者： Li, Lincan Shao, Wei Dong, Wei Tian, Yijun Yang, Kaixiang Zhang, Wenjie Data and Knowledge Research Group University of New South Wales Sydney Australia Data61 CSIRO ClaytonVIC Australia BYD Intelligent Driving R&D Centre Shenzhen China University of Notre Dame South Bend United States School of Computer Science & Engineering South China University of Technology Guangzhou China

The aspiration of the next generation’s autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology. Recent advancement in AD simulation, closed-loop model training, and AD big data engine have gained some valuable experience. However, there is a lack of systematic knowledge and deep understanding regarding how to build efficient data-centric AD technology for AD algorithm self-evolution and better AD big data accumulation. To fill in the identified research gaps, this article will closely focus on reviewing the state-of-the-art data-driven autonomous driving technologies, with an emphasis on the comprehensive taxonomy of autonomous driving datasets characterized by milestone generations, key features, data acquisition settings, etc. Furthermore, we provide a systematic review of the existing benchmark closed-loop AD big data pipelines from the industrial frontier, including the procedure of closed-loop frameworks, key technologies, and empirical studies. Finally, the future directions, potential applications, limitations and concerns are discussed to arouse efforts from both academia and industry for promoting the further development of autonomous driving. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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Embedding VLAD in Transformer for Video Question Answering

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Jisuanji Xuebao/Chinese Journal of Computers 2023年第4期46卷 671-689页

作者： Guo, Dan Yao, Shen-Tao Wang, Hui Wang, Meng School of Computer and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230094 China Key Laboratory of Knowledge Engineering with Big Data（Hefei University of Technology） Ministry of Education Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province（Hefei University of Technology） Hefei230601 China

Video question answering（VideoQA）is a typical cross-modal understanding task. Its challenge lies in how to learn appropriate multimodal representation and cross-modal correlation for answer inference. Most existing video question answering methods focus on the latter，e. g.，relationship learning between each video frame or clip and word. In this work， we devote to advanced feature embedding of both video and query. We develop a clustering-based VLAD technique for VideoQA. The novelty of our work is the joint exploitation of temporal aggregation and correlation in multimodality. We propose an end-to-end trainable Transformed VLAD embedding network， named TVLAD-Net. TVLAD-Net constructs a differentiable aggregation network module（i. e.， convolutional Residual-less VLAD Block）to generate compact VLAD descriptors（transforming N frames，clips or words to compact K descriptors while K © 2023 science Press. All rights reserved.

关键词： Semantics

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Chinese Emergency Event Extraction Based on Contrastive Learning with Event Semantic Features

Chinese Emergency Event Extraction Based on Contrastive Lear...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xinyi Sheng Jinguang Gu Youcheng Yan Fangfang Xu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China

Extracting emergency events from a large amount of unstructured information is essential for improving early warning and emergency response. Existing event extraction methods for specialist fields often rely on well-designed syntactic dependencies or external knowledge graphs, which can provide high accuracy in their specialist fields. However, these approaches may have poor generalization capabilities and are difficult to apply directly to the emergency field. In addition, most existing methods ignore the interdependencies between event triggers and event arguments, which may be crucial semantic features for event extraction. To solve these problems, we propose a Chinese emergent event extraction method based on contrastive learning with event semantic features(CNEmergEE). Firstly, the proposed approach automatically expands the pre-trained model’s vocabulary on a domain corpus. Then, continuing learning on the same corpus allows the model to complete domain adaptation. Finally, we use contrast learning to enable models to learn semantic representations of events. Experimental results on the CEC (Chinese Emergency Corpus) dataset show that our approach achieves better improvement.

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High-Quality Noise Detection for knowledge Graph Embedding with Rule-Based Triple Confidence 18th

High-Quality Noise Detection for Knowledge Graph Embedding w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hong, Yan Bu, Chenyang Wu, Xindong Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China

ISBN: (纸本)9783030891879

knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise. © 2021, Springer Nature Switzerland AG.

关键词： knowledge graph

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