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    • 4. 发明申请
    • Methods and apparatuses for cross-ontologial analytics
    • 交叉本体分析的方法和装置
    • US20080025617A1
    • 2008-01-31
    • US11493503
    • 2006-07-25
    • Christian PosseAntonio P. SanfilippoBanu GopalanRoderick M. RienscheRobert L. Baddeley
    • Christian PosseAntonio P. SanfilippoBanu GopalanRoderick M. RienscheRobert L. Baddeley
    • G06K9/62
    • G06K9/62G06F19/24G06F19/28G06K9/6892
    • Methods and apparatuses for quantifying the similarity between nodes in a plurality of electronic classification schemes are disclosed according to some aspects. In one embodiment, quantification of the similarity between a first node in a first electronic classification scheme and a second node in a second electronic classification scheme comprises finding a third node among those in the first electronic classification scheme, wherein a first product value of an inter-scheme similarity value between the second and third nodes and an intra-scheme similarity value between the first and third nodes is a maximum. It further comprises finding a fourth node among those in the second electronic classification scheme, wherein a second product value of an inter-scheme similarity value between the first and fourth nodes and an intra-scheme similarity value between the second and fourth nodes is a maximum. The maximum between the first and second product values represents a measure of similarity between the first and second nodes.
    • 根据一些方面公开了用于量化多个电子分类方案中的节点之间的相似性的方法和装置。 在一个实施例中,在第一电子分类方案中的第一节点与第二电子分类方案中的第二节点之间的相似度的量化包括在第一电子分类方案中找到第三节点之间的第三节点,其中, 第二节点和第三节点之间的节目相似度值和第一节点与第三节点之间的方案间相似度值是最大的。 其还包括在第二电子分类方案中找到第四节点,其中第一和第四节点之间的方案间相似度值的第二乘积值与第二和第四节点之间的方案间相似度值是最大值 。 第一和第二乘积值之间的最大值表示第一和第二节点之间的相似性度量。
    • 7. 发明授权
    • Cross-ontological analytics for alignment of different classification schemes
    • 跨本体分析用于校准不同的分类方案
    • US07805010B2
    • 2010-09-28
    • US11493503
    • 2006-07-25
    • Christian PosseAntonio P SanfilippoBanu GopalanRoderick M RienscheRobert L Baddeley
    • Christian PosseAntonio P SanfilippoBanu GopalanRoderick M RienscheRobert L Baddeley
    • G06K9/62
    • G06K9/62G06F19/24G06F19/28G06K9/6892
    • Quantification of the similarity between nodes in multiple electronic classification schemes is provided by automatically identifying relationships and similarities between nodes within and across the electronic classification schemes. Quantifying the similarity between a first node in a first electronic classification scheme and a second node in a second electronic classification scheme involves finding a third node in the first electronic classification scheme, wherein a first product value of an inter-scheme similarity value between the second and third nodes and an intra-scheme similarity value between the first and third nodes is a maximum. A fourth node in the second electronic classification scheme can be found, wherein a second product value of an inter-scheme similarity value between the first and fourth nodes and an intra-scheme similarity value between the second and fourth nodes is a maximum. The maximum between the first and second product values represents a measure of similarity between the first and second nodes.
    • 通过自动识别电子分类方案内和之间的节点之间的关系和相似性,提供了多种电子分类方案中节点之间相似性的量化。 量化第一电子分类方案中的第一节点与第二电子分类方案中的第二节点之间的相似度涉及在第一电子分类方案中找到第三节点,其中第二电子分类方案之间的方案间相似性值的第一乘积值 并且第三节点和第一和第三节点之间的方案内相似度值是最大的。 可以找到第二电子分类方案中的第四节点,其中第一和第四节点之间的方案间相似度值的第二乘积值和第二和第四节点之间的方案间相似度值是最大的。 第一和第二乘积值之间的最大值表示第一和第二节点之间的相似性度量。
    • 8. 发明申请
    • Object clustering methods, ensemble clustering methods, data processing apparatus, and articles of manufacture
    • 对象聚类方法,集群聚类方法,数据处理设备和制造
    • US20070174268A1
    • 2007-07-26
    • US11331529
    • 2006-01-13
    • Christian PosseBobbie-Jo Webb-RobertsonSusan HavreBanu GopalanAnuj Shah
    • Christian PosseBobbie-Jo Webb-RobertsonSusan HavreBanu GopalanAnuj Shah
    • G06F17/30
    • G06K9/6226G06F16/355
    • Object clustering methods, ensemble clustering methods, data processing apparatuses, and articles of manufacture are described according to some aspects. In one aspect, an object clustering method includes accessing a plurality of respective cluster results of a plurality of different clustering solutions, wherein the cluster results of an individual one of the different clustering solutions associate a plurality of objects with a plurality of respective first clusters and indicate probabilities of the objects being correctly associated with the respective ones of the first clusters of the respective individual clustering solution, and using the cluster results including the associations of the objects and the first clusters of the respective different clustering solutions and the probabilities of the objects being correctly associated with the respective first clusters of the respective different clustering solutions, generating additional associations of the objects with a plurality of second clusters and wherein the additional associations comprise additional cluster results of an additional clustering solution.
    • 根据一些方面描述对象聚类方法,集合聚类方法,数据处理设备和制品。 在一个方面,一种对象聚类方法包括访问多个不同聚类解的多个相应聚类结果,其中,所述不同聚类解中的单独一个的聚类结果将多个对象与多个相应的第一聚类相关联,并且 指示对象正确地与相应的单独聚类解决方案的第一个聚类中相应的对象相关联的概率,并且使用包括各个不同聚类解决方案的对象和第一聚类的关联的聚类结果以及对象的概率 与相应的不同聚类解决方案的相应的第一聚类正确相关联,生成对象与多个第二聚类的附加关联,并且其中附加关联包括附加聚类解决方案的附加聚类结果。