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Integrity constraints in OWL ontologies based on grounded circumscription

Frontiers of Computer Science 2013年第6期7卷 812-821页

作者： Dantong OUYANG College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China National Engineering Research Center of Geophysics Exploration Instruments Changchun 130026 China

The extensions for logic-based knowledge bases with integrity constraints are rather popular. We put forward an alternative criteria for analysis of integrity constraints in Web ontology language （OWL） ontology under the closed world assumption. According to this criteria, grounded circumscription is applied to define integrity constraints in OWL ontology and the satisfaction of the integrity constraints by minimizing extensions of the predicates in integrity con- straints. According to the semantics of integrity constraints, we provide a modified tableau algorithm which is sound and complete for deciding the consistency of an extended ontol- ogy. Finally, the integrity constraint validation is converted into the corresponding consistency of the extended ontology. Comparing our approach with existing integrity constraint validation approaches, we show that the results of our approach are more in accordance with user requirements than other approaches in certain cases.

关键词： semantic Web description logic ontology integrity constraints grounded circumscription

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DeciLS-PBO: an effective local search method for pseudo-Boolean optimization

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Frontiers of Computer Science 2024年第5期18卷 239-241页

作者： Luyu JIANG Dantong OUYANG Qi ZHANG Liming ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education) Jilin UniversityChangchun 130012China

1 Introduction Local search method is a rising star for solving combinatorial optimization problems in recent years,and the state-of-the-art local search-based incomplete Maximum Satisfiability(MaxSAT)solversshowpromi... 详细信息

关键词： Max optimization local

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A Simplified Rational Representation for Positive-Dimensional Polynomial Systems and SHEPWM Equations Solving

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Journal of Systems Science & Complexity 2017年第6期30卷 1470-1482页

作者： SHANG Baoxin ZHANG Shugong TAN Chang XIA Peng School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering (Ministry of Education) Jilin University College of Science Northeast Electric Power University College of Science Northeast Forestry University School of Mathematics Liaoning University

The paper is concerned with the improvement of the rational representation theory for solving positive-dimensional polynomial systems. The authors simplify the expression of rational representation set proposed by Tan and Zhang(2010), obtain the simplified rational representation with less rational representation sets, and hence reduce the complexity for representing the variety of a positive-dimensional ideal. As an application, the authors compute a "nearly" parametric solution for the SHEPWM problem with a fixed number of switching angles.

关键词： Positive-dimensional polynomial system solving rational univariate representation SHEPWM simplified rational representation

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Research on Trust Prediction from a Sociological Perspective

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Journal of Computer Science & Technology 2015年第4期30卷 843-858页

作者：王英王鑫左万利 College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun 130012 China School of Computer Technology and Engineering Changehun Institute of Technology Changchun 130012 China

Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.

关键词： trust prediction homophily coefficient status coefficient social theory matrix factorization

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Applying model-transformation techniques to ontology evolution

Applying model-transformation techniques to ontology evoluti...

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2nd International Conference on Grid Service engineering and Management, GSEM 2005 - Co-located with the 6th International Conference on Net.ObjectDays 2005, NODe 2005

作者： Jin, Longfei Liu, Lei Guo, Degui Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education of China Jilin University Changchun China

ISBN: (纸本)3885793989

Ontology evolution in the Model Driven Semantic Web can be looked as a process of model transformations. A model-transformation based conceptual framework for ontology evolution is presented in the paper. Applications of model transformations in every phase of ontology evolution process are described. The framework combines technologies of ontology evolution, Ontology Definition Metamodel and model transformations, and it can be looked as a method for ontology evolution in the Model Driven Semantic Web.

关键词： Ontology

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2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model for WMH Segmentation from 3D MR Brain Image

2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Lan Sun, Yinglu Zhao, Ziqi Guo, Chunjie Wang, Yan College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China The First Hospital of Jilin University Department of Radiology Changchun China

ISBN: (纸本)9798350386226

Segmenting brain white matter hyperintensities (WMH) from 3D Magnetic Resonance (MR) images is crucial for the diagnosis, treatment, and prognosis of Multiple Sclerosis (MS). Unlike common 2D images, this task is more challenging and time-consuming. Classical deep learning methods for 3D image segmentation face two main challenges: 1) Pure 3D networks have more parameters and are prone to overfitting. 2) When using 2D networks to segment slices of 3D images, the lack of 3D structural information results in suboptimal segmentation after reconstruction. To address these difficulties, we propose the 2.5D ASF-UNet, which employs the 2.5D workflow and uses adjacent slices as the input for 2D segmentation network ASF-UNet. In ASF-UNet, separate down-sampling paths are used for the adjacent slices, and the Local Spatial Attention Module (LSAM) is designed to more effectively integrate 3D spatial information into the 2D network. Additionally, the Conv-Spectral-Block (CSB) is designed to extract and integrate local and global features. It allows the model to capture global spatial structures while preserving detailed information. Experimental results on MICCAI MSSEG 2016 and Local MS datasets show that 2.5D ASF-UNet achieve better segmentation performance than other deep learning methods. © 2024 IEEE.

关键词： Image segmentation

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Computing minimal diagnosis with binary decision diagrams algorithm

Computing minimal diagnosis with binary decision diagrams al...

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6th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2009

作者： Wang, Kun Li, Zhan-Shan Ai, Yang Zhang, Yong-Gang Key Laboratory of Symbolic Computation Knowledge Engineering for Ministry of Education Jilin University Jilin Changchun China

ISBN: (纸本)9780769537351

In this paper we propose an algorithm of computing minimal diagnosis based on BDD (Binary Decision Diagram). First we give the concept of disjunction equations, and map the collection of conflict sets into disjunction equations, then we compile the system into a BDD, further we compute the minimal hitting sets by solving the BDD and minimizing hitting sets. The reliability and completeness of the algorithm are proved. Variable ordering and construct controlling of BDD are used to further reduce the size of final BDD and the number of non-minimal hitting sets to be generated. The efficiency of computing minimal hitting sets is highly improved. Experimental results show that the algorithm isn't restricted by the structure of system and conflict sets, compared with three algorithms in the references, it can generate all the minimal hitting sets, and it works well in random circumstances. © 2009 IEEE.

关键词： Binary decision diagrams

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Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

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Journal of Bionic engineering 2008年第2期5卷 111-119页

作者： Jin-hui Yang Liang Sun Heow Pueh Lee Yun Qian Yan-chun Liang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012 P. R. China Institute of High Performance Computing Singapore 117528 Singapore Department of Mechanical Engineering National University of Singapore Singapore 117576 Singapore Institute of Electric and Information Engineering Beihua University Jilin 13202 l P. R. China

A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.

关键词： job shop scheduling problem clonal selection algorithm simulated annealing global search local search

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Design for network camera based on DSP

Design for network camera based on DSP

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2006 International Conference on Hybrid Information Technology, ICHIT 2006

作者： Zhezhou, Yu Yuchen, Xie Congying, Lv Chunguang, Zhou Faner, Meng Lei, Liu Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)0769526748

The hardware design of the DSP-based network camera is described in this paper which includes the implementation of CCD camera, DSP, Flash, SDRAM, CPLD, and Ethernet. The program of JPEG2000has been tested on the designed board and the result turns out to be good. It'll be implemented as a network camera with real-time video compression and transmission which can be connected to the Ethernet. © 2006 IEEE.

关键词： Cameras

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Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

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ACM Transactions on knowledge Discovery from Data 2024年第9期18卷 1-30页

作者： Liu, Yonghao Li, Mengyu Li, Ximing Huang, Lan Giunchiglia, Fausto Liang, Yanchun Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China University of Trento Trento Italy Zhuhai Lab. of the Key Lab. of Symbolic Computation and Knowledge Eng. of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies have attempted to combine meta-learning with graph neural networks to solve few-shot node classification on graphs. Despite their promising performance, some limitations remain. First, they employ the node encoding mechanism of homophilic graphs to learn node embeddings, even in heterophilic graphs. Second, existing models based on meta-learning ignore the interference of randomness in the learning process. Third, they are trained using only limited labeled nodes within the specific task, without explicitly utilizing numerous unlabeled nodes. Finally, they treat almost all sampled tasks equally without customizing them for their uniqueness. To address these issues, we propose a novel framework for few-shot node classification called Meta-GPS. Specifically, we first adopt an efficient method to learn discriminative node representations on homophilic and heterophilic graphs. Then, we leverage a prototype-based approach to initialize parameters and contrastive learning for regularizing the distribution of node embeddings. Moreover, we apply self-Training to extract valuable information from unlabeled nodes. Additionally, we adopt S (scaling and shifting) transformation to learn transferable knowledge from diverse tasks. The results on real-world datasets show the superiority of Meta-GPS. Our code is available here. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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