ISSN 1000-1239 CN 11-1777/TP


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    Journal of Computer Research and Development    2018, 55 (12): 2585-2586.  
    Abstract820)   HTML19)    PDF (341KB)(454)       Save
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    Research Review of Knowledge Graph and Its Application in Medical Domain
    Hou Mengwei, Wei Rong, Lu Liang, Lan Xin, Cai Hongwei
    Journal of Computer Research and Development    2018, 55 (12): 2587-2599.   DOI: 10.7544/issn1000-1239.2018.20180623
    Abstract5630)   HTML230)    PDF (2825KB)(4268)       Save
    With the advent of the medical big data era, knowledge interconnection has received extensive attention. How to extract useful medical knowledge from massive data is the key for medical big data analysis. Knowledge graph technology provides a means to extract structured knowledge from massive texts and images.The combination of knowledge graph, big data technology and deep learning technology is becoming the core driving force for the development of artificial intelligence. The knowledge graph technology has a broad application prospect in the medical domain. The application of knowledge graph technology in the medical domain will play an important role in solving the contradiction between the supply of high-quality medical resources and the continuous increase of demand for medical services.At present, the research on medical knowledge graph is still in the exploratory stage. The existing knowledge graph technology generally has several problems such as low efficiency, multiple restrictions and poor expansion in the medical domain. This paper firstly analyzes the medical knowledge graph architecture and construction technology for the strong professionalism and complex structure of big data in the medical domain. Secondly, the key technologies and research progress of the three modules of knowledge extraction, knowledge expression, knowledge fusion and knowledge reasoning in medical knowledge map are summarized. In addition, the application status of medical knowledge maps in clinical decision support, medical intelligence semantic retrieval, medical question answering system and other medical services are introduced. Finally, the existing problems and challenges of current research are discussed and analyzed, and its development is prospected.
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    Question Answering Algorithm on Image Fragmentation Information Based on Deep Neural Network
    Wang Yilei, Zhuo Yifan, Wu Yingjie, Chen Mingqin
    Journal of Computer Research and Development    2018, 55 (12): 2600-2610.   DOI: 10.7544/issn1000-1239.2018.20180606
    Abstract1542)   HTML31)    PDF (3061KB)(591)       Save
    Many fragmentation information is highly dispersed in different data sources, such as text, image, video and Web. They are characterized by structural disorder and content one-sided. Current researches implement the extraction, expression and understanding of multi-modal fragmentation information by constructing visual question answering (VQA) system. The VQA task is required to provide the correct answer to a given problem with a corresponding image. The aim of this paper is to design a complete framework and algorithm for image fragmentation information question answering under the basic background of visual question answering task. The main research includes image feature extraction, question text feature extraction, multi-modal feature fusion and answer reasoning. Deep neural network is constructed to extract features for representing images and problem information. Attention mechanism and variational inference method are combined to fusion two modal features of image and problem and reason answers. Experiment results show that the model can effectively extract and understand multi-modal fragmentation information, and improve the accuracy of VQA.
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    Clustering Ensemble Algorithm with Cluster Connection Based on Wisdom of Crowds
    Zhang Hengshan, Gao Yukun, Chen Yanping, Wang Zhongmin
    Journal of Computer Research and Development    2018, 55 (12): 2611-2619.   DOI: 10.7544/issn1000-1239.2018.20180575
    Abstract1100)   HTML7)    PDF (1962KB)(402)       Save
    The accuracy and stability of clustering will be obviously improved when a lot of independent clustering results for the same data set are aggregated by utilizing the principle of wisdom of crowds. In this paper, clustering ensemble algorithm with cluster connection based on wisdom of crowds (CECWOC) is proposed. Firstly, the independent clustering results are produced by the different clustering algorithms, which is guided by utilizing the independency, decentralization, diversity of wisdom of crowds. Secondly, the clustering ensemble algorithm based on connecting triple is developed to grouping aggregate the produced independent clusters, and the obtained results are aggregated again and the final cluster set is produced. The advantages of proposed algorithm are that: 1)The produced clusters by base clustering is grouping aggregated and weights of clusters are adjusted so that the selection of clusters is avoided, as a result, information on the produced clusters are not ignored; 2)Similarities of data are computed by using connected triple algorithm, the relations of data that their similarities are zero can be used. The experimental results at the different data sets show that the proposed algorithm can obtain the more accurate and stable results than other clustering ensemble algorithms, including the ones based on framework of wisdom of crowds.
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