US 10,242,320 B1Granted

Machine assisted learning of entities

AssigneeMaana, Inc.

InventorsAlexander Hussam Elkholy, Balasubramanian Kandaswamy, Steven Matt Gustafson, Hussein S. Al-Olimat

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Abstract

A data model is traversed to determine concept characteristics associated with concepts that may be associated with entities. Associated documents may be evaluated to identify document characteristics associated with the entities. Entity models may be trained based on the concept characteristics and the document characteristics with each entity model being associated with a confidence value. Results for one or more queries based on the documents and the entity models may be provided. The results may reference the documents that may be associated with the entities. Some entity models may produce results that have a confidence value below a threshold value. Accordingly, the entity models that provide low confidence results may be re-trained.

Claims28

Dependent claims

A method for managing data using one or more processors, included in one or more network computers, to execute instructions for implementing a modeling platform server that performs actions, comprising:

instantiating a data engine that performs actions including:
providing a data model that includes one or more concepts and one or more relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
and
providing one or more documents that include one or more entities, wherein the one or more entities are associated with the one or more concepts;
and
instantiating a training engine that performs actions, including:
traversing the data model to determine one or more concept characteristics associated with the one or more concepts that are associated with the one or more entities;
evaluating the one or more documents to identify one or more document characteristics associated with the one or more entities;
and
training one or more entity models based on the one or more concept characteristics and the one or more document characteristics, wherein each entity model is associated with a confidence value;
and
instantiating a query engine to perform actions including:
resolving results for one or more queries based on the one or more documents and the one or more entity models, wherein the results reference the one or more documents that are associated with the one or more entities;
and
determining one or more entity models that are associated with a confidence value that is below a threshold value for the results;
and
providing the one or more determined entity models to the training engine for re-training.

14 more elements

A system for managing data over a network, comprising:

a network computer, comprising:
a transceiver that communicates over the network;
a memory that stores at least instructions;
and
one or more processors that execute instructions that perform actions, including:
instantiating a data engine that performs actions including:
providing a data model that includes one or more concepts and one or more relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
and
providing one or more documents that include one or more entities, wherein the one or more entities are associated with the one or more concepts;
and
instantiating a training engine that performs actions, including:
traversing the data model to determine one or more concept characteristics associated with the one or more concepts that are associated with the one or more entities;
evaluating the one or more documents to identify one or more document characteristics associated with the one or more entities;
and
training one or more entity models based on the one or more concept characteristics and the one or more document characteristics, wherein each entity model is associated with a confidence value;
and
instantiating a query engine to perform actions including:
resolving results for one or more queries based on the one or more documents and the one or more entity models, wherein the results reference the one or more documents that are associated with the one or more entities;
and
determining one or more entity models that are associated with a confidence value that is below a threshold value for the results;
and
providing the one or more determined entity models to the training engine for re-training;
and
a client computer, comprising:
another transceiver that communicates over the network;
another memory that stores at least instructions;
and
one or more processors that execute instructions that perform actions, including:
displaying the result on a hardware display.

26 more elements

A processor readable non-transitory storage media that includes instructions for managing data, wherein execution of the instructions by one or more hardware processors performs actions, comprising:

instantiating a data engine that performs actions including:
providing a data model that includes one or more concepts and one or more relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
and
providing one or more documents that include one or more entities, wherein the one or more entities are associated with the one or more concepts;
and
instantiating a training engine that performs actions, including:
traversing the data model to determine one or more concept characteristics associated with the one or more concepts that are associated with the one or more entities;
evaluating the one or more documents to identify one or more document characteristics associated with the one or more entities;
and
training one or more entity models based on the one or more concept characteristics and the one or more document characteristics, wherein each entity model is associated with a confidence value;
and
instantiating a query engine to perform actions including:
resolving results for one or more queries based on the one or more documents and the one or more entity models, wherein the results reference the one or more documents that are associated with the one or more entities;
and
determining one or more entity models that are associated with a confidence value that is below a threshold value for the results;
and
providing the one or more determined entity models to the training engine for re-training.

14 more elements

A network computer for managing data, comprising:

a transceiver that communicates over the network;
a memory that stores at least instructions;
and
one or more processors that execute instructions that perform actions, including:
instantiating a data engine that performs actions including:
providing a data model that includes one or more concepts and one or more relations between the concepts, wherein each concept is a node in the data model and each relation is an edge in the data model;
and
providing one or more documents that include one or more entities, wherein the one or more entities are associated with the one or more concepts;
and
instantiating a training engine that performs actions, including:
traversing the data model to determine one or more concept characteristics associated with the one or more concepts that are associated with the one or more entities;
evaluating the one or more documents to identify one or more document characteristics associated with the one or more entities;
and
training one or more entity models based on the one or more concept characteristics and the one or more document characteristics, wherein each entity model is associated with a confidence value;
and
instantiating a query engine to perform actions including:
resolving results for one or more queries based on the one or more documents and the one or more entity models, wherein the results reference the one or more documents that are associated with the one or more entities;
and
determining one or more entity models that are associated with a confidence value that is below a threshold value for the results;
and
providing the one or more determined entity models to the training engine for re-training.

18 more elements

Dependencies are inferred from the claim text.