Invention Grant
US09070090B2 Scalable string matching as a component for unsupervised learning in semantic meta-model development
有权
可扩展字符串匹配作为语义元模型开发中无监督学习的组件
- Patent Title: Scalable string matching as a component for unsupervised learning in semantic meta-model development
- Patent Title (中): 可扩展字符串匹配作为语义元模型开发中无监督学习的组件
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Application No.: US13596844Application Date: 2012-08-28
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Publication No.: US09070090B2Publication Date: 2015-06-30
- Inventor: Philip Ogren , Luis Rivas , Edward A. Green
- Applicant: Philip Ogren , Luis Rivas , Edward A. Green
- Applicant Address: US CA Redwood City
- Assignee: Oracle International Corporation
- Current Assignee: Oracle International Corporation
- Current Assignee Address: US CA Redwood City
- Agency: Marsh Fischmann & Breyfogle LLP
- Main IPC: G06F15/18
- IPC: G06F15/18 ; G06N99/00 ; G06K9/62 ; G06F17/22 ; G06F17/30

Abstract:
A string analysis tool for calculating a similarity metric between a source string and a plurality of target strings. The string analysis tool may include optimizations that may reduce the number of calculations to be carried out when calculating the similarity metric for large volumes of data. In this regard, the string analysis tool may represent strings as features. As such, analysis may be performed relative to features (e.g., of either the source string or plurality of target strings) such that features from the strings may be eliminated from consideration when identifying target strings for which a similarity metric is to be calculated. The elimination of features may be based on a minimum similarity metric threshold, wherein features that are incapable of contributing to a similarity metric above the minimum similarity metric threshold are eliminated from consideration.
Public/Granted literature
- US20140067728A1 SCALABLE STRING MATCHING AS A COMPONENT FOR UNSUPERVISED LEARNING IN SEMANTIC META-MODEL DEVELOPMENT Public/Granted day:2014-03-06
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