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    MODEL SEARCH METHOD AND DEVICE BASED ON SEMANTIC MODEL FRAMEWORK
    4.
    发明申请
    MODEL SEARCH METHOD AND DEVICE BASED ON SEMANTIC MODEL FRAMEWORK 审中-公开

    公开(公告)号：WO2018138205A1

    公开(公告)日：2018-08-02

    申请号：PCT/EP2018/051839

    申请日：2018-01-25

    Applicant: SIEMENS AKTIENGESELLSCHAFT

    Inventor： WANG, Qi ,  YUAN, Yong ,  DONG, Ming Kai ,  ZHANG, Rui Guo ,  YU, Ming ,  CAO, Jing ,  ZHANG, Zhen ,  ZHANG, Ming

    IPC: G06F17/30

    CPC classification number: G06F16/33 ,  G06F16/3334 ,  G06F16/335 ,  G06F16/36

    Abstract: The present invention provides a model search method based on a semantic model framework, including: a buffering step of buffering and analyzing model query information of a user and buffering relative knowledge; and a query step of querying a model in a buffer, an index, and a data library, comparing a model queried by the user and the model queried in the buffer, the index, and the data library, ranking relative models, returning a ranking result as a search result, and sending the search result to the user. According to the model search method and device based on a semantic model framework provided in the present invention, search for a relative model may be performed at a high response speed, and particularly this is quite practical in a recommendation process in modeling. According to the present invention, query, analysis, and the search result can be provided to buffer the relative knowledge, so as to ensure rapid search in modeling. According to the present invention, self-extension can be performed under the semantic model framework, that is, new knowledge having no classification information is incorporated into the semantic model framework.

    CONVERSATIONAL DATA ANALYSIS
    5.
    发明申请
    CONVERSATIONAL DATA ANALYSIS 审中-公开
    对话数据分析

    公开(公告)号：WO2018063924A1

    公开(公告)日：2018-04-05

    申请号：PCT/US2017/052839

    申请日：2017-09-22

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： HOU, Zhitao ,  LOU, Jian-Guang ,  ZHANG, Bo ,  LIANG, Xiao ,  ZHANG, Dongmei ,  ZHANG, Haidong

    IPC: G06F17/30 ,  G06F17/27

    CPC classification number: G06F17/2785 ,  G06F16/26 ,  G06F16/3329 ,  G06F16/36

    Abstract: Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.

    Abstract translation: 这里描述的主题的实现涉及对话数据分析。 在从用户接收到数据分析请求之后，可以基于数据分析请求确定启发信息。 这里提到的启发式信息不是数据分析请求的结果，而是可用于引导对话继续进行的信息。 基于这样的启发式信息，用户可以提供与数据分析请求相关联的补充信息，例如，阐明数据分析请求的含义，提交相关的进一步分析请求等等。 根据用户提供的补充信息，可以向用户提供真正想要的和有意义的数据分析结果。 因此，数据分析将变得更加准确和有效。 在获得真正有用​​的信息的同时，用户也获得了良好的用户体验。

    COMPILING DOCUMENTS INTO A TIMELINE PER EVENT
    7.
    发明申请
    COMPILING DOCUMENTS INTO A TIMELINE PER EVENT 审中-公开
    每次将文档编入时间表

    公开(公告)号：WO2018048683A1

    公开(公告)日：2018-03-15

    申请号：PCT/US2017/049233

    申请日：2017-08-30

    Applicant: MICROSOFT TECHNOLOGY LICENSING, LLC

    Inventor： ALONSO, Omar ,  KANDYLAS, Vasileios ,  TREMBLAY, Serge-Eric

    IPC: G06F17/30 ,  G06Q50/00

    CPC classification number: G06F17/24 ,  G06F16/24578 ,  G06F16/285 ,  G06F16/36 ,  G06F16/93 ,  G06F17/2211 ,  G06F17/2235 ,  G06F17/2775 ,  G06Q10/1091 ,  G06Q30/0201 ,  G06Q50/01 ,  H04L51/16 ,  H04L51/32 ,  H04L67/306

    Abstract: Representative embodiments disclose mechanisms to compile documents into a timeline document that tracks the evolution of a topic over time. Social media documents can be used to identify importance or popularity of linked documents (i.e., documents shared by social media in a post, tweet, etc.). A collection of social media documents is analyzed and used to identify a series of n-grams and a ranked list of linked documents. A subset of the ranked list is selected based upon similarity to the series of n-grams. The subset is then summarized and captured, along with underlying supporting data, into an entry of a timeline document. Related entries in different timeline documents can be linked to create a pivot point that allows a user to jump from one timeline to another. Timeline documents can be made available as part of a search performed by a query system.

    Abstract translation: 代表性实施例公开了将文档编译成时间线文档的机制，该时间线文档随着时间的推移跟踪话题的演变。 社交媒体文档可用于识别链接文档的重要性或受欢迎程度（即社交媒体在文章中发布的文档，推文等）。 分析社交媒体文档的集合并用于识别一系列n元组和链接文档的排名列表。 基于与一系列n元组的相似性来选择分级列表的子集。 然后将该子集与基础支持数据一起汇总并捕获到时间线文档的条目中。 可以链接不同时间轴文档中的相关条目以创建一个支点，以便用户从一个时间轴跳转到另一个时间轴。 时间轴文件可作为查询系统执行的搜索的一部分。

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