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2005 AAAI Workshop

作者： Bangyong, Liang Jie, Tang Gang, Wu Peng, Zhang Kuo, Zhang Hui, Xu Po, Zhang Xuedong, Yan Juanzi, Li Knowledge Engineering Group Department of Computer Science Tsinghua University

ISBN: (纸本)1577352378

This paper introduces SWARMS, a tool for exploring domain knowledge in semantic web. By domain knowledge exploration, we mean searching for or navigating the knowledge in a specific domain. We have found, through an analysis of survey result and an analysis of using log data, that requirements for domain knowledge exploration can be grouped into three categories. The categories include knowledge search, schema based navigation, and search results analysis. Traditional methods usually focus on one of the three types, for example, retrieval of 'relevant knowledge' by exploiting full-text retrieval methods. We propose a tool, called SWARMS, for exploring domain knowledge, in which we provide the ability to conduct domain knowledge exploration by the three categories. Specifically, users can conduct search for special kind of knowledge and they can also interact with the tool by navigating the knowledge base. Furthermore, we conduct analysis for the search or navigation results. The tool is applied to the software management domain. We use ontology as the mean for knowledge representation. The paper describes the architecture, features, and component technologies of the tool. Copyright © 2002, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Grouping abstraction and authority control in policy-based spectrum management

Grouping abstraction and authority control in policy-based s...

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2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks

作者： Feeney, Kevin Lewis, David Argyroudis, Patroklos Nolan, Keith O'Sullivan, Declan Knowledge and Data Engineering Group (KDEG) Department of Computer Science and Statistics TCD Ireland CTVR Ireland Knowledge and Data Engineering Group Department of Computer Science and Statistics Ireland CTVR TCD Ireland

ISBN: (纸本)9781424406623

The management of dynamic spectrum access requires the coordination of administrative functions across multiple organizations, from regulators to secondary market operators and commons cooperatives. Policy-based management approaches to dynamic spectrum access must therefore support a wide range of organizational types and handle the relationships between policies authored separately within those organizations. A policy language for administering dynamic access to spectrum resources has been proposed by the DARPA XG initiative. This defines an expressive and extensible language for specifying policies that determine the correct behavior of RIF transceivers as spectrum usage opportunities occur. However, this language does not yet address the mapping of policies onto the complex organizational settings that are likely to emerge in the dynamic spectrum access domain. This paper defines how the DARPA XG policy language can be integrated with a meta-policy management scheme that provides the powerful organizational modeling and policy authoring authority control needed to manage the evolution of policies in these complex organizational settings. The benefits of this integration are illustrated through a case study based on the potential use of dynamic spectrum access licenses granted in Ireland for the DySpan 2007 conference, and the organizational conflicts that even this simple scenario can yield. The practical issues of administering a multi-organizational policy management system are examined through an analysis of the interactions between existing components that are being assembled to demonstrate sophisticated policy management of a cognitive radio system.

关键词： Abstracting

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Effective short text classification via the fusion of hybrid features for IoT social data

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Digital Communications and Networks 2022年第6期8卷 942-954页

作者： Xiong Luo Zhijian Yu Zhigang Zhao Wenbing Zhao Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Department of Electrical Engineering and Computer Science Cleveland State UniversityClevelandOH44115USA Department of Computer Science and Information Engineering National Taipei University of TechnologyTaipei10608China

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

关键词： Information fusion Short text classi fication BERT Bidirectional encoder representations fr 0om transformers Deep learning Social data

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SEOE:an option graph based semantically embedding method for prenatal depression detection

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Frontiers of computer science 2024年第6期18卷 245-258页

作者： Xiaosong HAN Mengchen CAO Dong XU Xiaoyue FENG Yanchun LIANG Xiaoduo LANG Renchu GUAN College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012China Department of Electrical Engineering and Computer Science and Christopher S.Bond Life Sciences Center University of MissouriColumbiaMO 65211USA Department of Computer Science and Technology Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationZhuhai College of Science and TechnologyZhuhai 519041China Jilin Provincial Institute of Population Science and Technology Changchun 130000China

Prenatal depression,which can affect pregnant women’s physical and psychological health and cause postpartum depression,is increasing ***,it is essential to detect prenatal depression early and conduct an attribution *** studies have used questionnaires to screen for prenatal depression,but the existing methods lack *** diagnose the early signs of prenatal depression and identify the key factors that may lead to prenatal depression from questionnaires,we present the semantically enhanced option embedding(SEOE)model to represent questionnaire *** can quantitatively determine the relationship and patterns between options and *** first quantifies options and resorts them,gathering options with little difference,since Word2Vec is highly dependent on *** resort task is transformed into an optimization problem involving the traveling salesman ***,all questionnaire samples are used to train the options’vector using ***,an LSTM and GRU fused model incorporating the cycle learning rate is constructed to detect whether a pregnant woman is suffering from *** verify the model,we compare it with other deep learning and traditional machine learning *** experiment results show that our proposed model can accurately identify pregnant women with depression and reach an F1 score of *** most relevant factors of depression found by SEOE are also verified in the *** addition,our model is of low computational complexity and strong generalization,which can be widely applied to other questionnaire analyses of psychiatric disorders.

关键词： questionnaire analyses text classification graph representation learning traveling salesman problem prenatal depression

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2025年 1197卷 v-vi页

作者： Fong, Simon Dey, Nilanjan Joshi, Amit University of Macau China Department of Computer Science and Engineering Techno International New Town West Bengal Kolkata India Global Knowledge Research Foundation Gujarat Ahmedabad India

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MorPhiC Consortium: towards functional characterization of all human genes

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Nature 2025年第8050期638卷 351-359页

作者： Adli, Mazhar Przybyla, Laralynne Burdett, Tony Burridge, Paul W. Cacheiro, Pilar Chang, Howard Y. Engreitz, Jesse M. Gilbert, Luke A. Greenleaf, William J. Hsu, Li Huangfu, Danwei Hung, Ling-Hong Kundaje, Anshul Li, Shuzhao Parkinson, Helen Qiu, Xiaojie Robson, Paul Schürer, Stephan C. Shojaie, Ali Skarnes, William C. Smedley, Damian Studer, Lorenz Sun, Wei Vidović, Dušica Vierbuchen, Thomas White, Brian S. Yeung, Ka Yee Yue, Feng Zhou, Ting Žurauskienė, Justina Zhong, Aaron Zheng, Shujian Zhao, Hengqiang Zhang, Nan Zhang, Zhehao Zhang, Stephen Zeng, Tony Yu, Bo Yordanova, Galabina N. Yang, Dapeng Xing, De Wang, Xianming Wang, Ping Torre, Denis Tang, Wenzhuo Shivalikanjli, Anu Sengupta, Kaustav Seker, Fidan Ventura, Enrique Sapena Ross, Nicolette C. Rivera-Diaz, Jamilex Rana, Arushi Pongpamorn, Praeploy Poirion, Olivier B. Pillai, Ajay Pilarczyk, Marcin Parker, Devon Ning, Gang Neyisci, Ozlem Nasam, Niharika Mittal, Varun Mitchell, Joshua M. Metzl-Raz, Eyal Meng, Chen Melo-Carrillo, Adrian McDonough, Justin A. Marengo, Gabriel Mao, Yuzhen Leite, Davi Lyra Luo, Renhe Liu, Nianping Liu, Dingyu Liu, Zukai Liu, Si Li, Zhaoheng Kraft, Katerina Koci, Orges A. Kirsammer, Gina Kendra, Angelina Hutter, Carolyn Huebner, Aaron J. Huang, Yung-Hsin Hu, Nan Gupta, Dipayan Gupta, Revant Guo, Tingfeng Griffiths, Alexandra M. Goyal, Juhee Glenn, Rachel A. Garofalo, Jessica L. Fukuda, Bryce Nobutoshi Fu, Yihao Flynn, William F. Flores, Jesse Fletcher, Colin F. Fleming, Patrick J. Felsenfeld, Adam L. Eraslan, Basak Doty, Anthony Dahlstrom, Thomas J. Chung, Caty E. Choudhary, Anshul Cho, Hyein S. Chen, Xintong Chen, Sijie Chan, Chia Baroch, Jacob J. Baker, Dylan S. Bai, Tao Avireddy, Rohan N. V. S. R. K. Amiri, Anahita Diniz, Juliana Alcoforado Abbaszadeh, Neda Robert H. Lurie Comprehensive Cancer Center Department of Obstetrics and Gynecology Northwestern University Feinberg School of Medicine Chicago IL United States Department of Biochemistry and Biophysics University of California San Francisco CA United States Omics Section European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Hinxton United Kingdom Department of Pharmacology Center for Pharmacogenomics Northwestern University Feinberg School of Medicine Evanston IL United States William Harvey Research Institute Clinical Pharmacology and Precision Medicine Queen Mary University of London London United Kingdom Department of Dermatology Stanford University Stanford CA United States Department of Genetics Stanford University Stanford CA United States Basic Science and Engineering (BASE) Initiative Stanford University Stanford CA United States Department of Urology University of California San Francisco CA United States Department of Biostatistics Public Health Sciences Fred Hutchinson Cancer Center Seattle WA United States Developmental Biology Program Sloan Kettering Institute New York NY USA School of Engineering and Technology University of Washington Tacoma Tacoma WA United States Departments of Genetics and Computer Science Stanford University Stanford CA United States Jackson Laboratory for Genomic Medicine Farmington CT United States Knowledge Management Section European Bioinformatics Institute (EMBL-EBI) European Molecular Biology Laboratory Hinxton United Kingdom Molecular and Cellular Pharmacology Sylvester Comprehensive Cancer Center University of Miami Coral Gables FL USA Department of Biostatistics University of Washington Seattle WA United States Department of Biochemistry and Molecular Genetics Robert H. Lurie Comprehensive Cancer Center Northwestern University Feinberg School of Medicine Chicago IL United States

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions. © 2025. Springer Nature Limited.

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knowledge discovery in multidimensional knowledge representation framework: An integrative approach for the visualization of text analytics results

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Iran Journal of computer science 2018年第4期1卷 199-216页

作者： Zenkert, Johannes Klahold, André Fathi, Madjid Department of Electrical Engineering and Computer Science Institute of Knowledge Management and Knowledge Based Systems University of Siegen Siegen Germany

Visualization of results is one of the central challenges in big data analytics and integrative text mining. With a growing amount of unstructured data and different perspectives on big data, knowledge graphs have difficulties to simultaneously represent and visualize all analyzed dimensions of knowledge. This paper proposes integrative text mining as a solution to combine results from different dimensional analysis in a multidimensional knowledge representation (MKR) for knowledge discovery and visualization purpose. Analysis results from named entity recognition, topic detection, sentiment analysis and the extraction of semantic relationships are, therefore, integrated into a common representation structure. In the implementation part of this research, an application is introduced which utilizes MKR based on the results of stated text mining methods applied on a German and English news data set. State-of-the-art visualizations are used in the application and MKR adaptively transforms the visualization type of the knowledge graph according to the selected context for knowledge discovery. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Information visualization knowledge graphs knowledge representation Natural language processing Text mining

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A Depth-first Algorithm of Finding All Association Rules Generated by a Frequent Itemset

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Journal of Donghua University(English Edition) 2006年第6期23卷 1-4,9页

作者：武坤姜保庆魏庆 Institute of Data and Knowledge Engineering Henan University Institute of Data and Knowledge Engineering Henan University The Computer Science Department Henan University of Finance and Economics

The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.

关键词： association rule frequent itemset breath-first,depth-first consequent.

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Empirical estimation of various data stream mining methods

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International Journal of Computational Intelligence Studies 2021年第1期10卷 13-26页

作者： Srivastava, Ritesh Mittal, Veena Computer Science and Engineering Department Galgotias College of Engineering and Technology Knowledge Park II Uttar Pradesh Greater Noida201310 India Computer Science and Engineering Department Faculty of Engineering and Technology MRIIRS Faridabad India

Online learning is done in order to work on dynamic environments in which the concept tends to change with time and the accuracy of classifiers decreases. The current and previous research is done in static environments, but there is a need of a real time data streaming due to the potentially larger number of applications available in the scientific and business domains. There are several methods used in learning in the presence of dynamic environments like single classifier methods such as batch and incremental learning approaches, classification methods with explicit drift detection method, windowing techniques and ensemble approaches. This paper, investigates these approaches for determining the best suitable method among them. We utilised light emitting diode (LED) data generator for evaluating the performance of the methods. © 2021 Inderscience Enterprises Ltd.

关键词： Mining

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