Search Patents
  • Publication number: 20200057946
    Abstract: Techniques disclosed herein relate generally to constructing a customized knowledge graph. In one embodiment, entities and relations among entities are extracted from a user dataset based on certain rules to generate a seed graph. Large-scale knowledge graphs are then traversed using a finite state machine to identify candidate entities and/or relations to add to the seed graph. A priority function is used to select entities and/or relations from the candidate entities and/or relations. The selected entities and/or relations are then added to the seed graph to generate the customized knowledge graph.
    Type: Application
    Filed: August 15, 2019
    Publication date: February 20, 2020
    Applicant: Oracle International Corporation
    Inventors: Gautam Singaraju, Prithviraj Venkata Ammanabrolu
  • Publication number: 20190102345
    Abstract: Techniques disclosed herein relate to querying a hierarchical classification model that includes a plurality of classification models. The hierarchical classification model is configured to classify an input into a class in a plurality of classes and includes a tree structure. The tree structure includes leaf nodes and non-leaf nodes. Each non-leaf node has two child nodes associated with two respective sets of classes in the plurality of classes, where a difference between numbers of classes in the two sets of classes is zero or one. Each leaf node is associated with at least two but fewer than a first threshold number of classes. Each of the leaf nodes and non-leaf nodes is associated with a classification model in the plurality of classification models of the hierarchical classification model. The classification model associated with each respective node in the tree structure can be trained independently.
    Type: Application
    Filed: September 28, 2018
    Publication date: April 4, 2019
    Applicant: Oracle International Corporation
    Inventors: Gautam Singaraju, Jiarui Ding, Sangameswaran Viswanathan
  • Publication number: 20190102701
    Abstract: Techniques disclosed herein relate to generating a hierarchical classification model that includes a plurality of classification models. The hierarchical classification model is configured to classify an input into a class in a plurality of classes and includes a tree structure. The tree structure includes leaf nodes and non-leaf nodes. Each non-leaf node has two child nodes associated with two respective sets of classes in the plurality of classes, where a difference between numbers of classes in the two sets of classes is zero or one. Each leaf node is associated with at least two but fewer than a first threshold number of classes. Each of the leaf nodes and non-leaf nodes is associated with a classification model in the plurality of classification models of the hierarchical classification model. The classification model associated with each respective node in the tree structure can be trained independently.
    Type: Application
    Filed: September 28, 2018
    Publication date: April 4, 2019
    Applicant: Oracle International Corpoation
    Inventors: Gautam Singaraju, Jiarui Ding, Sangameswaran Viswanathan
  • Publication number: 20190103095
    Abstract: Techniques disclosed herein relate to improving quality of classification models for differentiating different user intents by improving the quality of training samples used to train the classification models. Pairs of user intents that are difficult to differentiate by classification models trained using the given training samples are identified based upon distinguishability scores (e.g., F-scores). For each of the identified pairs of intents, pairs of training samples each including a training sample associated with a first intent and a training sample associated with a second intent in the pair of intents are ranked based upon a similarity score between the two training samples in each pair of training samples. The identified pairs of intents and the pairs of training samples having the highest similarity scores may be presented to users through a user interface, along with user-selectable options or suggestions for improving the training samples.
    Type: Application
    Filed: September 28, 2018
    Publication date: April 4, 2019
    Applicant: Oracle International Corporation
    Inventors: Gautam Singaraju, Jiarui Ding, Vishal Vishnoi, Mark Joseph Sugg, Edward E. Wong