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    • 3. 发明申请
    • METHOD AND SYSTEM FOR CATEGORIZING WEB-SEARCH QUERIES IN SEMANTICALLY COHERENT TOPICS
    • 网络搜索查询在半隐性主题中的分类方法与系统
    • US20130132433A1
    • 2013-05-23
    • US13301786
    • 2011-11-22
    • Umut OzertemDebora DonatoLuca Aiello
    • Umut OzertemDebora DonatoLuca Aiello
    • G06F17/30
    • G06F16/951G06F16/36
    • A method and system for categorizing web-search queries in semantically coherent topics. The method includes receiving plurality of web-search queries from one or more users and storing the plurality of web-search queries in a query log. The method further includes processing the plurality of web-search queries for topic generation by generating plurality of missions from the query log and merging together one or more missions belonging to a similar topic. Further, the method includes determining topical user profile of a user by matching each mission of the user with one or more relevant topics, and detecting user activity of the user from random user activity. Moreover, the method includes naming one or more semantically coherent topics using a set of common concept terms extracted from the plurality of web-search queries. The system includes one or more electronic devices, a communication interface, a memory, and a processor.
    • 一种用于在语义一致的主题中对网络搜索查询进行分类的方法和系统。 该方法包括从一个或多个用户接收多个网络搜索查询,并将多个网络搜索查询存储在查询日志中。 该方法还包括通过从查询日志生成多个任务并将属于相似主题的一个或多个任务合并在一起来处理用于主题生成的多个web搜索查询。 此外,该方法包括通过将用户的每个任务与一个或多个相关主题进行匹配来确定用户的主题用户简档,以及从随机用户活动中检测用户的用户活动。 此外,该方法包括使用从多个网络搜索查询中提取的一组通用概念项来命名一个或多个语义上相干的主题。 该系统包括一个或多个电子设备,通信接口,存储器和处理器。
    • 8. 发明授权
    • Taxonomy-driven lumping for sequence mining
    • 用于序列挖掘的分类学驱动的块
    • US08346686B2
    • 2013-01-01
    • US12534706
    • 2009-08-03
    • Aristides GionisFrancesco BonchiDebora Donato
    • Aristides GionisFrancesco BonchiDebora Donato
    • G06F15/18
    • G06N99/005
    • Methods and apparatus are described for modeling sequences of events with Markov models whose states correspond to nodes in a provided taxonomy. Each state represents the events in the subtree under the corresponding node. By lumping observed events into states that correspond to internal nodes in the taxonomy, more compact models are achieved that are easier to understand and visualize, at the expense of a decrease in the data likelihood. The decision for selecting the best model is taken on the basis of two competing goals: maximizing the data likelihood, while minimizing the model complexity (i.e., the number of states).
    • 描述了使用马尔可夫模型建模事件序列的方法和装置,其状态对应于所提供的分类法中的节点。 每个状态表示相应节点下的子树中的事件。 通过将观察到的事件结合到与分类学中的内部节点对应的状态,实现更容易理解和可视化的更紧凑的模型,牺牲了数据可能性的降低。 选择最佳模型的决定是基于两个竞争目标:最大化数据可能性,同时最小化模型复杂度(即状态数)。