摘要:家谱数据是典型的碎片化数据，具有海量、多源、异构、自治的特点.通过数据融合技术将互联网中零散分布的家谱数据融合成一个全面、准确的家谱数据库，有利于针对家谱数据进行知识挖掘和推理，从而为用户提供姓氏起源、姓氏变迁和姓氏间关联等隐含信息.在大数据知识工程BigKE模型的基础上，提出了一个结合HAO智能模型的碎片化数据融合框架FDF-HAO （fragmented data fusion with human intelligence，artificial intelligence and organizational intelligence），阐述了架构中每层的作用、关键技术和需要解决的问题，并以家谱数据为例，验证了该数据融合框架的有效性.最后，对碎片化数据融合的前景进行展望.



Abstract:Genealogy data is a typical example for data fragmentation with massive, multiple, heterogeneous, and autonomous sources. Merging scattered genealogy data on the Internet into a comprehensive and accurate genealogy database through data fusion technologies, can be beneficial to knowledge mining and reasoning from genealogy data, and can provide users with implicit information such as surname origins, surname changes, and surname associations. Based on BigKE, a big data knowledge engineering model for big knowledge, this study proposes an FDF-HAO framework (fragmented data fusion with human intelligence, artificial intelligence, and organizational intelligence), describes the functionalities, key technologies, and problems to be solved of each layer in the framework, and verifies the validity of the data fusion framework by using genealogy data as an example. Finally, the challenges and opportunities of fragmented data fusion are also discussed.



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