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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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A Novel Lifecycle Operation Performance Evaluation Framework for Plant-Wide Industrial Processes 10

A Novel Lifecycle Operation Performance Evaluation Framework...

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10th IEEE data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Zhang, Xueyi Zhang, Kai Peng, Kaixiang Zhang, Chuangfang Ma, Liang Key Laboratory of Knowledge Automation for Industrial Processes School of Automation and Electrical Engineering University of Science and Technology Beijing Ministry of Education Beijing100083 China

ISBN: (纸本)9781665424233

In the modern complex industrial process, such as hot strip mill process (HSMP), the safety and optimality of the production process may deteriorate and operation performance will be degraded due to the wear of equipment, mode conversion and random disturbances. If the process is not adjusted and maintained, there may be more serious faults resulting in greater economic losses and potential safety hazards. Hence, it is essential to develop comprehensive operation performance evaluation. In this paper, a novel lifecycle operation performance evaluation framework based on multi-step total projection to latent structures (T-PLS) is proposed, which is used to deal with normal or faulty performance evaluate problems in the plant-wide HSMP. Firstly, HSMP can be divided into upstream, midstream and downstream by the actual process. Then, the T-PLS monitoring model is established gradually in each stream. Based on the pre-designed transmission rules of fault prior information, comprehensive statistical indexes are constructed to judge whether the process is in normal or faulty operation state. After that, the lifecycle performance evaluations are realized by different evaluation rules. Finally, the feasibility and effectiveness of the proposed method are illustrated through a real HSMP. © 2021 IEEE.

关键词： Accident prevention

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A Baseline for Early Classification of Time Series in An Open World

A Baseline for Early Classification of Time Series in An Ope...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junwei Lv Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Early classification of time series aims to accurately predict the class label of a time series as early as possible, which is significant but challenging in many time-sensitive applications. Existing early classification methods hold a basic closed-world assumption that the classifier must have seen the classes of test samples. However, new samples that do not belong to any trained class may appear in the real world. In this paper, we first address the early classification in an open world and design two detectors to identify which known class or unknown class a sample belongs to. Specifically, based on the observed data, an early known-class detector is designed to determine the known-class confidence and an early unknown-class detector is designed to determine the unknown-class confidence according to the Minimum Reliable Length (MRL) and the Weibull distribution of each class. Experimental results evaluated on real-world datasets demonstrate that the proposed model can identify samples of unknown and known classes accurately and early.

关键词： Computational modeling Conferences Time series analysis Detectors Reliability engineering Software Weibull distribution

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Semi-supervised Short Text Classification Based On Dual-channel data Augmentation

Semi-supervised Short Text Classification Based On Dual-chan...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiajun Li Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City Anhui Province China

In practical applications, high-quality labeled data is critical for short text classification. But in many cases, it is expensive and time-consuming to obtain labeled information. Semi-supervised short text classification is hence attracting more attention. However, due to the sparsity of short texts, the performance of existing short text classification models always needs to be improved. Therefore, in this paper, we propose a semi-supervised Short text classification method based on Dual-Channel data Augmentation called SDCA. More specifically, in order to solve the sparsity of short texts, this model first adopts the multi-stage word-level TCN (Temporal Convolutional Network)-based attention to enhanced semantic features and an one-dimensional convolution-based attention mechanism to augment the relevance of surrounding short texts. Secondly, the unlabeled data are augmented by word embedding weighted augmentation and word replacement augmentation, so that the model can make full use of the unlabeled short texts and further enhance the network training. Finally, extensive experiments conducted on four benchmark datasets demonstrate the effectiveness of the proposed model on semi-supervised short text classification.

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Positive and unlabeled learning with controlled probability boundary fence 24

Positive and unlabeled learning with controlled probability ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Changchun Li Yuanchao Dai Lei Feng Ximing Li Bing Wang Jihong Ouyang College of Computer Science and Technology and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines.

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Which Companies are Likely to Invest: knowledge-graph-based Recommendation for Investment Promotion

Which Companies are Likely to Invest: Knowledge-graph-based ...

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IEEE International Conference on data Mining (ICDM)

作者： Chenyang Bu Jiawei Zhang Xingchen Yu Le Wu Xindong Wu 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 and Institute of Big Knowledge Science Hefei University of Technology Hefei China

Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list of enterprises, and then attract investment through visits, negotiations, and other methods. As contacting and visiting companies one by one requires huge amounts of manpower and time, the choice of target companies is critical for attracting investments. However, to the best of our knowledge, no study has conducted research from the perspective of knowledge-graph-based recommendation. In this study, we define the problem of target company recommendation in the process of investment promotion, and analyze the characteristics of the problem and the challenges it faces based on the background of the actual problem. Then, a two-tier model for solving this problem is provided from the perspective of knowledge graph reasoning. Aiming at the problem that the knowledge graph will frequently change, the model is designed based on the idea of combining the advantages of global and local link prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed model.

关键词： Economics knowledge acquisition Government Companies Production Manuals Predictive models

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Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on knowledge Graph

Visual Analysis of the National Characteristics of the COVID...

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作者： Yong Feng Ning Zhang Hongyan Xu Rongbing Wang Yonggang Zhang College of Information Liaoning University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

The aim is to construct a country-dimension knowledge graph of COVID-19 vaccines from the information of COVID-19 vaccines and to analyze the leading countries of vaccine R&D by combining the advantages of easy operation and intuitive feeling of knowledge graph visualization,to provide a reference for Chinese vaccine R&D departments and international *** this paper,through data collection,based on entity extraction and relationship construction,a knowledge graph of country dimensions was established by specifying the central vaccine R&D countries and vaccine distribution,and multidimensional microdata such as word frequency and betweenness centrality were combined to analyze the national characteristics of the COVID-19 *** analysis of the knowledge graph of the country dimension of the COVID-19 vaccine shows that countries with robust technology and economies,such as the US and China,choose to develop vaccine distribution independently,countries with advanced economies,such as Saudi Arabia,decide to purchase vaccine distribution,and less developed countries,such as South Africa and Latin America,need international aid for vaccines or purchase low-cost *** paper constructs the correlation between nodes and nodes of the COVID-19 vaccine with the help of a knowledge graph,systematically and comprehensively reveals the research mainstay and distribution model of the COVID-19 vaccine from the national level,and provides rationalized suggestions for international cooperation in vaccine R&D in China.

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Acoustical Field Modeling for Communication Through Steel Based on FDTD

Acoustical Field Modeling for Communication Through Steel Ba...

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Chinese Intelligent Systems Conference, CISC 2020

作者： Liu, Tao Liu, Jiajia Bai, Zongmei Liu, Ouming Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789811584572

The acoustical field characteristics of the steel-through communication system are studied and the Finite-Difference Time-Domain is used to establish the acoustical field model of the ultrasonic propagated in the system using Yee-type mesh division. Further, the accuracy of acoustical field model is improved by the high-order difference method applied in the algorithm which the effectiveness is demonstrated by the dynamic simulation result of the acoustical field, and the determination coefficient for data verification is increased by about 2%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Finite difference time domain method

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An Offensive Language Identification Based on Deep Semantic Feature Fusion 8

An Offensive Language Identification Based on Deep Semantic ...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Li, Xiang Zeng, Zhi Wu, Mingmin Huang, Zhongqiang Sha, Ying Shi, Lei College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

ISBN: (纸本)9781665450515

Various forms of social interactions are often char-acterized by toxic or offensive words that can be collectively referred to as offensive languages, which has become a unique linguistic phenomenon in social media platforms. How to detect and identify these offensive languages in social media platforms has become one of the important research in the field of natural language processing. Existing methods utilize machine learning algorithms or text representation models based on deep learning to learn the features of offensive languages and identify them, which have achieved good performances. However, traditional machine learning-based methods mainly rely on keyword identi-fication and blocking, deep learning-based methods do not ade-quately explore the fused deep semantic features of the content by combining word-level embeddings and sentence-level deep semantic feature representations of sentences, which cannot ef-fectively identify offensive languages that do not contain common offensive words but indicate offensive meanings. In this research, we propose a novel offensive language identification model based on deep semantic feature fusion, which uses the pre-trained model Bert to obtain word-level embedding representations of offensive languages, and then integrates the RCNN that combines with the attention mechanism to extract the fused deep semantic feature representations of offensive languages, and label encoder and offensive predictor to improve the identification accuracy and generalization ability of the model so that the performances of the model do not rely on the offensive language lexicon entirely and can identify offensive languages that do not contain common offensive words but indicate offensive meanings. Experimental results on Wikipedia and Twitter comment datasets show that our proposed model can better understand the context and discover potential offensive meanings, and outperforms existing methods. © 2022 IEEE.

关键词： Embeddings

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Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging based on Adversarial Learning

Low-Sampling-Frequency Plane wave Medical Ultrasound Imaging...

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IEEE International Conference on Image Processing

作者： Xiaoteng Chen Junying Chen School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education China

In plane wave medical ultrasound imaging, the analog echo signals are received via the transducer, and sampled as digital radio frequency data which is further processed by the beamforming algorithm to obtain the output ultrasound image. The sampling frequency of radio frequency data has a great impact on the image quality. The radio frequency data with higher sampling frequency is closer to the true distribution of the analog echo signals, leading to better image quality. However, high-frequency sampling chips are usually expensive. Thus, improving the plane wave imaging quality for radio frequency data with low sampling frequency has important practical significance. The ultrasound imaging quality is mainly affected by shallow features such as texture and color. However, low-sampling-frequency data has poor shallow features while high-sampling-frequency data has better shallow features. In this work, we propose an adversarial learning method to replace the shallow features of low-frequency data with the shallow features of high-frequency data, but retaining the deep features of low-frequency data. The experiment results demonstrate that the proposed method improves the plane wave imaging quality for low-sampling-frequency data by fusing shallow and deep features.

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