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International Conference on Semantics, knowledge and Grid (SKG)

作者： Xuan Tian Haihua Li Xiaoyong Du School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOESchool of Information Renmin University of China Beijing China

In domain ontology, semantic association (SA) is used to depict the correlation between two concepts. In this paper, we define semantic association degree (SAD) for measuring SA in the domain ontology. We first present a method to measure SAD of two direct related concepts by evaluating the semantic relationship between them, and then give another method to measure SAD of two indirect related concepts though SAD of two directed neighboring concepts. A set of comparison experiments show the benefit of our approaches.

关键词： Ontologies Psychology data mining Diseases Information retrieval Laboratories data engineering knowledge engineering Text categorization History

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Intervention Prediction for Patients with Pressure Injury Using Random Forest 12

Intervention Prediction for Patients with Pressure Injury Us...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Jin, Liuqi Pan, Yan Yang, Jiaoyun Han, Lin Lv, Lin Raviv, Miki An, Ning Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data of The Ministry of Education School of Computer Science and Information Engineering Hefei China Lanzhou University Evidence-Based Nursing Center School of Nursing Lanzhou China Wound and Ostomy Care Center Gansu Provincal Hospital Lanzhou China Vitalerter LTD Ha-Yarden Airport City Israel

ISBN: (纸本)9781665438582

Pressure injury (PI) is one of the major causes of short-term death. Early intervention for patients at risk plays an essential role in PI. However, many nurses may ignore risks. This paper aims to establish a model to predict interventions according to the patient's physical signs, which can help nurses develop care plans. We used data from 1, 483 patients with 25 characteristics and 17 interventions. We use the Random Forest and Particle Swarm Optimization (PSO) to optimize model parameters. Then we compared it with KNN, SVM, and Decision Tree. The 10-fold cross-validation result showed that the Random Forest has better accuracy than other methods, with an f1 score of 0.84. This finding proved the feasibility of using machine learning to help formulate care plans according to the classification of index prediction results. Our model shows that hemoglobin, Braden PI score, and age are the three most influential risk factors. © 2021 IEEE.

关键词： Decision trees

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Constructing Corpus for Query-oriented XML Text Summarization

Constructing Corpus for Query-oriented XML Text Summarizatio...

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2010 International Conference on Management of e-Commerce and e-Government(第四届电子商务与电子政务管理国际会议 ICMeCG 2010)

作者： Shihan WU Dexi LIU Xianpei JIAO Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance & Economics Nanchang Jiangxi China

XML Retrieval is becoming the focus study of the field of Information Retrieval and database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, as the basis of this study, the construction of the query-oriented XML text summarization corpus has not yet received enough attention. In this paper, we introduce our works on constructing this kind of corpus, including the selection of topics and XML elements/documents, construction process and the feature of the constructed corpus. Up to now, the corpus has 25 English query topics, including 422 elements for summarization, and 32 Chinese topics which including 402 elements. For each topic, 4 pieces of extracted summaries and 4 pieces of generated summaries are made manually by 4 experts.

关键词： Query-oriented XML Automatic summarization Corpus

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Convolution Neural Network for Renal Function Assessment Based on Glomerular Filtration Rate

Convolution Neural Network for Renal Function Assessment Bas...

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2021 International Conference on Advanced Technologies and Applications of Modern Industry, ATAMI 2021

作者： Wu, Congzhong Wang, Lulu Yang, Zhen Wang, Jing Han, Li Li, Fei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230601 China School of Computer and Information Hefei University of Technology Hefei Anhui230601 China The Second Hospital of Anhui Medical University Hefei Anhui230601 China

Objective: To develop a Convolution Neural Network (CNN) and discuss its performance in the estimation of Glomerular Fifiltration Rate (GFR) for patients with chronic kidney disease (CKD). Methods: A total of 112 patients with chronic kidney disease were enrolled in this study. The GFR is measured by 99mTc-DTPA renal dynamic and used as standard GFR after normalization by body surface area imaging. We established a CNN model and verified the performance of the model by comparing the GFR predicted by the model with the standard GFR. It turned out that the CNN could better evaluate the GFR of patients which is superior to the CG formula, MDRD formula, CKD-EPI formula and GRNN model. Conclusions: The CNN significantly evaluated GFR for patients with CKD, and it showed better performance than traditional methods and GRNN model and closer to the results of 99mTc-DTPA. The experimental results demonstrated that CNN could be used to estimate GFR. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

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DB Facade: A Web Cache with Improved data Freshness

DB Facade: A Web Cache with Improved Data Freshness

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International Symposium on Electronic Commerce and Security

作者： Xiongpai Qin Xiaoyun Zhou Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China School of Information and Electrical Engineering China University of Mining and Technology Xuzhou Jiangsu China

Traditional Web database cache techniques have a major disadvantage, namely poor data freshness, because they employ an asynchronous data refresh strategy. A novel Web database cache, DB Facade, is proposed in this paper. DB Facade uses a main memory database to cache result sets of previous queries for subsequent reusing. Updates on backend database system are managed by delta tables, and then propagated to Web database cache in a near real-time manner, hence guarantee the freshness of data. DB Facade off loads query burden from backend database systems, and exploits the power of main memory database system to boost query performance. TPC-W testing result shows that the system's performance increases by about 17 percent.

关键词： database systems System testing Assembly systems Web pages Web server Electronic commerce data security Information security Laboratories data engineering

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Global Top-k aggregate queries based on x-tuple in uncertain database

Global Top-k aggregate queries based on x-tuple in uncertain...

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24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010

作者： Liu, Dexi Wan, Changxuan Xiong, Naixue Park, Jong Hyuk Yeoe, Sang-Soo School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China Dept. of Computer Science Georgia State University Atlanta GA United States Dept. of Computer Science and Engineering Seoul National University of Technology Seoul Korea Republic of Division of Computer Engineering Mokwon University Daejeon Korea Republic of

ISBN: (纸本)9780769540191

A Top-k aggregate query, which is a powerful technique when dealing with large quantity of data, ranks groups of tuples by their aggregate values and returns k groups with the highest aggregate values. However, compared to Top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. As a powerful semantic of Top-k in uncertain database, Global Top-k return k highest-ranked tuples according to their probabilities of being in the Top-k answers in possible worlds. We propose a x-tuple based method to process Global Top-k aggregate queries in uncertain database. Our method has two levels, group state generation and G-x-Top-k query processing. In the former level, group states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming based Global x-tuple Top-k query processing are employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions. © 2010 IEEE.

关键词： Dynamic programming

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Link Prediction for Opportunistic Networks Based on Hybrid Similarity Metrics and E-LSTM-D Models 24th

Link Prediction for Opportunistic Networks Based on Hybrid...

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24th International Conference on Web Information Systems engineering, WISE 2023

作者： Yang, Xiaoying Li, Lijie Xu, Gang Song, Ming Zhang, Feng College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China School of Computer and Information Technology Shanxi University Taiyuan030000 China

ISBN: (纸本)9789819972531

Link prediction is a crucial issue in opportunistic networks routing research. Static link prediction methods ignore the historical information of network evolution, which affects the prediction accuracy. In this paper, we propose a link prediction method (HS-LSTM) that combines hybrid similarity metrics and the E-LSTM-D model. The approach extracts structural features using hybrid similarity metrics, constructs a structural feature matrix, and applies the E-LSTM-D model to extract temporal features and predict the links that have not yet appeared in the network. Our experiments on three real datasets of opportunistic networks (Infocom5, MIT, and Haggle) shows that our proposed method outperforms the baseline method in terms of prediction accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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Graph Neural Networks with Learnable and Optimal Polynomial Bases

arXiv

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arXiv 2023年

作者： Guo, Yuhe Wei, Zhewei Gaoling School of Articial Intelligence Renmin University of China China Peng Cheng Laboratory China Beijing Key Laboratory of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined polynomial basis and learn the coefficients from the training data. It has been observed that the effectiveness of the model is highly dependent on the property of the polynomial basis. Consequently, two natural and fundamental questions arise: Can we learn a suitable polynomial basis from the training data? Can we determine the optimal polynomial basis for a given graph and node features? In this paper, we propose two spectral GNN models that provide positive answers to the questions posed above. First, inspired by Favard’s Theorem, we propose the FavardGNN model, which learns a polynomial basis from the space of all possible orthonormal bases. Second, we examine the supposedly unsolvable definition of optimal polynomial basis from Wang & Zhang (2022) and propose a simple model, OptBasisGNN, which computes the optimal basis for a given graph structure and graph signal. Extensive experiments are conducted to demonstrate the effectiveness of our proposed models. Our code is available at https://***/yuziGuo/FarOptBasis. © 2023, CC BY.

关键词： Graph neural networks

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Creating Something From Nothing: Unsupervised knowledge Distillation for Cross-Modal Hashing

Creating Something From Nothing: Unsupervised Knowledge Dist...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hengtong Hu Lingxi Xie Richang Hong Qi Tian School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Huawei Inc.

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

In recent years, cross-modal hashing (CMH) has attracted increasing attentions, mainly because its potential ability of mapping contents from different modalities, especially in vision and language, into the same space, so that it becomes efficient in cross-modal data retrieval. There are two main frameworks for CMH, differing from each other in whether semantic supervision is required. Compared to the unsupervised methods, the supervised methods often enjoy more accurate results, but require much heavier labors in data annotation. In this paper, we propose a novel approach that enables guiding a supervised method using outputs produced by an unsupervised method. Specifically, we make use of teacher-student optimization for propagating knowledge. Experiments are performed on two popular CMH benchmarks, i.e., the MIRFlickr and NUS-WIDE datasets. Our approach outperforms all existing unsupervised methods by a large margin.

关键词： Training Semantics Computational modeling Feature extraction Dogs Training data Task analysis

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