US 2020/0050946 A1Published application

Generating data associated with underrepresented data based on a received data input

AssigneeAccenture Global Solutions Limited

InventorsFreddy Lecue, Md Faisal Zaman

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Abstract

An example method described herein involves receiving a data input; identifying a plurality of topics in the data input; determining an underrepresented set of data for a first set of topics of the plurality of topics based on a plurality of knowledge graphs associated with the first set of topics; calculating a score for each topic of the first set of topics based on a representative learning technique; determining that the score for a first topic of the first set of topics satisfies a threshold score; selecting a topic specific knowledge graph based on the first topic; identifying representative objects that are similar to objects of the data input based on the topic specific knowledge graph; generating representation data that is similar to the data input based on the representative objects to balance the underrepresented set of data with a set of data associated with a second set of topics of the plurality of topics; and performing an action associated with the representation data.

Claims21

Dependent claims

A method, comprising:

receiving, by a device, a data input;
receiving, by the device, a domain knowledge graph associated with objects of the data input;
identifying, by the device, a plurality of topics in the data input based on the domain knowledge graph;
determining, by the device, a represented set of data for a first set of topics of the plurality of topics;
determining, by the device, an underrepresented set of data for a second set of topics of the plurality of topics,
the underrepresented set of data being determined using a representative learning technique, and
the representative learning technique being machine learning;
determining, by the device, a distance between an identified topic of the data input and a first topic in the domain knowledge graph;
calculating, by the device, a score for the first topic based on relevance of each topic to the data input the distance;
determining, by the device, that the score for the first topic satisfies a threshold score;
determining, by the device and based on determining that the score for the first topic satisfies the threshold score, that the first topic of the plurality of topics is one topic of the second set of topics;
selecting, by the device, a topic specific knowledge graph based on the first topic;
identifying, by the device, objects of the data input based on the topic specific knowledge graph;
identifying, by the device, representative objects that have a threshold level of similarity with the objects of the data input based on the topic specific knowledge graph;
generating, by the device and based on the representative objects, representation data that is of a similar data type to the data input and increases an amount of data associated with the underrepresented set of data,
generating the representation data comprising:
substituting a representative object, of the representative objects, with an object, of the objects of the data input, based on an edge distance of the representative object from the object of the data input in the topic specific knowledge graph;
and
performing, by the device, an action associated with the representation data.

16 more elements

A device, comprising:

one or more memories;
and
one or more processors, communicatively coupled to the one or more memories, to:
receive a data input;
identify a plurality of topics in the data input;
determine an underrepresented set of data for a first set of topics of the plurality of topics based on a plurality of knowledge graphs associated with the first set of topics,
the underrepresented set of data being determined using a representative learning technique, and
the representative learning technique being machine learning;
determine a distance between an identified topic of the data input and a first topic in a domain knowledge graph;
calculate a score for the first topic based on the distance;
determine that the score for the first topic satisfies a threshold score;
select a topic specific knowledge graph based on the first topic;
identify representative objects that are similar to objects of the data input based on the topic specific knowledge graph;
generate, based on the representative objects, representation data that is of a similar data type to the data input to balance the underrepresented set of data with a set of data associated with a second set of topics of the plurality of topics and increase an amount of data associated with the underrepresented set of data,
wherein the one or more processors, when generating the representation data, are to:
substitute a representative object, of the representative objects, with an object, of the objects of the data input, based on an edge distance of the representative object from the object of the data input in the topic specific knowledge graph;
and
perform an action associated with the representation data.

15 more elements

A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to:
receive a data input;
determine a represented set of data for a first set of topics of a plurality of topics of the data input based on a domain knowledge graph of the plurality of topics;
determine an underrepresented set of data for a second set of topics of the plurality of topics based on a representative learning technique,
wherein the underrepresented set of data is underrepresented relative to the represented set of data, and
wherein the representative learning technique is machine learning;
determine a distance between an identified topic of the data input and a first topic in the domain knowledge graph;
calculate a score for the first topic based on the distance;
determine that the score for Hall the first topic satisfies a threshold score;
determine that the first topic of the plurality of topics is one of the second set of topics;
select a topic specific knowledge graph based on the first topic being one of the second set of topics;
identify representative objects that are similar to objects of the data input based on the topic specific knowledge graph;
generate, based on the representative objects, representation data that is of similar data type to the data input and increases an amount of data associated with the underrepresented set of data,
wherein the one or more instructions to generate the representation data cause the one or more processors to:
substitute one of the representative objects with a corresponding object of the data input based on an edge distance of the one of the representative objects from the object of the data input in the topic specific knowledge graph;
generate a representation knowledge graph based on the representation data,
wherein the representation knowledge graph includes a new topic that is associated with the underrepresented set of data;
and
store the representation knowledge graph in a knowledge graph data structure,
wherein the knowledge graph data structure stores the domain knowledge graph and the topic specific knowledge graph.

17 more elements

Dependencies are inferred from the claim text.