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1. 发明申请

US20170046435A1 SEARCH ENGINE RESULTS SYSTEM USING ENTITY DENSITY 有权
标题翻译：使用实体密度搜索引擎结果系统
公开(公告)号：US20170046435A1
公开(公告)日：2017-02-16
申请号：US14822852
申请日：2015-08-10
申请人： Microsoft Corporation
发明人： Franco Salvetti , Srinivas Vadrevu
IPC分类号： G06F17/30
CPC分类号： G06F17/30867 , G06F17/30371 , G06F17/30424 , G06F17/30554
摘要： Architecture that enables search engines to meet user expectations for search results (e.g., questions-answers) by improving on result consistency. This is attained by declining to answer queries when it is known by the system that the system is unable to answer more or equivalently prominent queries in the same query class in a vast majority of cases. To achieve consistency, queries are categorized into classes and then the queries of a specific class are segmented into clusters. The answer density in each cluster is then computed to determine the consistency of that class of queries. Clusters with a relatively low answer density are then suppressed to improve consistency for the user.
摘要翻译：通过改进结果一致性，使搜索引擎能够满足用户对搜索结果的期望（例如，问题答案）的架构。这在系统知道系统在绝大多数情况下不能在同一个查询类中回答更多或相当突出的查询时，通过拒绝回答查询来实现。为了实现一致性，查询被分类为类，然后将特定类的查询分段为群集。然后计算每个集群中的答案密度，以确定该类查询的一致性。然后抑制具有较低答案密度的群集以提高用户的一致性。

2. 发明授权

US09852226B2 Search engine results system using entity density 有权
公开(公告)号：US09852226B2
公开(公告)日：2017-12-26
申请号：US14822852
申请日：2015-08-10
申请人： Microsoft Corporation
发明人： Franco Salvetti , Srinivas Vadrevu
IPC分类号： G06F17/30
CPC分类号： G06F17/30867 , G06F17/30371 , G06F17/30424 , G06F17/30554
摘要： Architecture that enables search engines to meet user expectations for search results (e.g., questions-answers) by improving on result consistency. This is attained by declining to answer queries when it is known by the system that the system is unable to answer more or equivalently prominent queries in the same query class in a vast majority of cases. To achieve consistency, queries are categorized into classes and then the queries of a specific class are segmented into clusters. The answer density in each cluster is then computed to determine the consistency of that class of queries. Clusters with a relatively low answer density are then suppressed to improve consistency for the user.

3. 发明授权

US09229988B2 Ranking relevant attributes of entity in structured knowledge base 有权
标题翻译：排列结构化知识库中实体的相关属性
公开(公告)号：US09229988B2
公开(公告)日：2016-01-05
申请号：US13744416
申请日：2013-01-18
申请人： Microsoft Corporation
发明人： Srinivas Vadrevu , Ying Tu , Franco Salvetti
IPC分类号： G06F17/30
CPC分类号： G06F17/3053 , G06F17/30604 , G06F17/30943
摘要： Architecture that addresses at least the problem of ranking the relevant attributes for a given entity within the context of a structured knowledge base (SKB). The architecture utilizes the attribute, entity type statistics, and the taxonomy of the attributes to consistently and efficiently rank attributes for each and every type of entity in the SKB. Using the SKB, intermediate features are computed, including the importance or popularity each entity type for every entity, inverse document frequency (IDF) computation for each attribute on a global basis, IDF computation for entity types, and the popularity of attributes for each entity type. The intermediate features are aggregated to obtain a final feature set, which can be used in combination with human judgments to train a machine learned classifier model to produce and predict a relevance score for a given entity and each of its attributes. The attributes are ranked for each entity using this score.
摘要翻译：至少解决在结构化知识库（SKB）的上下文中为给定实体排列相关属性的问题的架构。该架构利用属性，实体类型统计和属性分类来一致有效地对SKB中每一类实体的属性进行排序。使用SKB，计算中间特征，包括每个实体的每个实体类型的重要性或受欢迎程度，全局每个属性的逆文档频率（IDF）计算，实体类型的IDF计算以及每个实体的属性的普及类型。中间特征被聚合以获得最终特征集，其可以与人类判断结合使用以训练机器学习分类器模型以产生和预测给定实体及其每个属性的相关性得分。使用此分数为每个实体的属性进行排名。

4. 发明申请

US20140207763A1 RANKING RELEVANT ATTRIBUTES OF ENTITY IN STRUCTURED KNOWLEDGE BASE 有权
标题翻译：在结构化知识库中排除实体的相关属性
公开(公告)号：US20140207763A1
公开(公告)日：2014-07-24
申请号：US13744416
申请日：2013-01-18
申请人： MICROSOFT CORPORATION
发明人： Srinivas Vadrevu , Ying Tu , Franco Salvetti
IPC分类号： G06F17/30
CPC分类号： G06F17/3053 , G06F17/30604 , G06F17/30943
摘要： Architecture that addresses at least the problem of ranking the relevant attributes for a given entity within the context of a structured knowledge base (SKB). The architecture utilizes the attribute, entity type statistics, and the taxonomy of the attributes to consistently and efficiently rank attributes for each and every type of entity in the SKB. Using the SKB, intermediate features are computed, including the importance or popularity each entity type for every entity, inverse document frequency (IDF) computation for each attribute on a global basis, IDF computation for entity types, and the popularity of attributes for each entity type. The intermediate features are aggregated to obtain a final feature set, which can be used in combination with human judgments to train a machine learned classifier model to produce and predict a relevance score for a given entity and each of its attributes. The attributes are ranked for each entity using this score.
摘要翻译：至少解决在结构化知识库（SKB）的上下文中为给定实体排列相关属性的问题的架构。该架构利用属性，实体类型统计和属性分类来一致有效地对SKB中每一类实体的属性进行排序。使用SKB，计算中间特征，包括每个实体的每个实体类型的重要性或受欢迎程度，全局每个属性的逆文档频率（IDF）计算，实体类型的IDF计算以及每个实体的属性的普及类型。中间特征被聚合以获得最终特征集，其可以与人类判断结合使用以训练机器学习分类器模型以产生和预测给定实体及其每个属性的相关性得分。使用此分数为每个实体的属性进行排名。

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