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US 12,602,595 B2Granted
System and method of using a knowledge representation for features in a machine learning classifier
AssigneePrimal Fusion Inc.
InventorsMathew Whitney Wilson, Ihab Ilyas, Peter J. Sweeney
Life of this patent
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Abstract
Systems and methods are provided for classifying at least one unlabeled content item with a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used as a source of features for which training data is evaluated. The machine learning classifier is trained based on features based on the attributes and the training data.
Claims18
A computer-implemented method of transforming a set of raw data into a set of featurized data using an object of interest comprising a set of unstructured data for training machine learning models, the method comprising executing via a computer system comprising at least one processor and at least one non-transitory memory:
- receiving, by the at least one processor, the set of raw data for use with the machine learning models, the set of raw data comprising content items;
- receiving, by the at least one processor, the object of interest containing terms and/or properties;
- synthesizing, by the at least one processor executing executable instructions stored in the at least one non-transitory memory, a knowledge representation based on terms and/or properties of the object of interest, the knowledge representation being encoded as a non-transitory computer-readable data structure, the synthesizing comprising:
- generating an initial knowledge representation using the object of interest as input, the initial knowledge representation containing a plurality of concepts and at least one relationship between the plurality of concepts;
- executing a semantic synthesis process on the initial knowledge representation to obtain an augmented initial knowledge representation, the semantic synthesis process including (i) augmenting the initial knowledge representation using at least one information source external to the object of interest, (ii) deriving at least a first concept or a first relationship between two or more concepts that is not present in the initial knowledge representation based on a semantic relationship between the plurality of concepts in the initial knowledge representation and the at least a first concept or a first relationship, and (iii) adding the at least a first concept or a first relationship to the initial knowledge representation;
- deriving one or more attributes from the augmented initial knowledge representation, wherein the one or more attributes are properties of the augmented initial knowledge representation;
- deriving a first set of one or more content item features from the derived one or more attributes for use in obtaining featurized content items with derived features;
- determining evaluation values of the derived first set of one or more content item features for each of the content items of the set of raw data by evaluating the content items of the set of raw data with respect to the derived first set of one or more content item features;
- associating the evaluation values of the derived first set of one or more content item features with each of the content items of the set of raw data to obtain the set of featurized data;
- and
- training the machine learning models using the set of featurized data as training data.
8 more elements
A system for transforming a set of raw data into a set of featurized data using an object of interest comprising a set of unstructured data for training machine learning models, the system comprising:
- at least one non-transitory memory, and at least one processor configured to perform a method comprising:
- receiving the set of raw data for use with the machine learning models, the set of raw data comprising content items;
- receiving the object of interest containing terms and/or properties;
- synthesizing, by the at least one processor executing executable instructions stored in the at least one non-transitory memory, a knowledge representation based on terms and/or properties of the object of interest, the knowledge representation being encoded as a non-transitory computer-readable data structure, the synthesizing comprising:
- generating an initial knowledge representation using the object of interest as input, the initial knowledge representation containing a plurality of concepts and at least one relationship between the plurality of concepts;
- executing a semantic synthesis process on the initial knowledge representation to obtain an augmented initial knowledge representation, the semantic synthesis process including (i) augmenting the initial knowledge representation using at least one information source external to the object of interest, (ii) deriving at least a first concept or a first relationship between two or more concepts that is not present in the initial knowledge representation based on a semantic relationship between the plurality of concepts in the initial knowledge representation and the at least a first concept or a first relationship, and (iii) adding the at least a first concept or a first relationship to the initial knowledge representation;
- deriving one or more attributes from the augmented initial knowledge representation, wherein the one or more attributes are properties of the augmented initial knowledge representation;
- deriving a first set of one or more content item features from the derived one or more attributes for use in obtaining featurized content items with derived features;
- determining evaluation values of the derived first set of one or more content item features for each of the content items of the set of raw data by evaluating the content items of the set of raw data with respect to the derived first set of one or more content item features;
- associating the evaluation values of the derived first set of one or more content item features with each of the content items of the set of raw data to obtain the set of featurized data;
- and
- training the machine learning models using the set of featurized data as training data.
9 more elements
At least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method of transforming a set of raw data into a set of featurized data using an object of interest comprising a set of unstructured data for training machine learning models, the method comprising:
- receiving the set of raw data for use with the machine learning models, the set of raw data comprising content items;
- receiving the object of interest containing terms and/or properties;
- synthesizing a knowledge representation based on terms and/or properties of the object of interest, the knowledge representation being encoded as a non-transitory computer-readable data structure, the synthesizing comprising:
- generating an initial knowledge representation using the object of interest as input, the initial knowledge representation containing a plurality of concepts and at least one relationship between the plurality of concepts;
- executing a semantic synthesis process on the initial knowledge representation to obtain an augmented initial knowledge representation, the semantic synthesis process including (i) augmenting the initial knowledge representation using at least one information source external to the object of interest, (ii) deriving at least a first concept or a first relationship between two or more concepts that is not present in the initial knowledge representation based on a semantic relationship between the plurality of concepts in the initial knowledge representation and the at least a first concept or a first relationship, and (iii) adding the at least a first concept or a first relationship to the initial knowledge representation;
- deriving one or more attributes from the augmented initial knowledge representation, wherein the one or more attributes are properties of the augmented initial knowledge representation;
- deriving a first set of one or more content item features from the derived one or more attributes for use in obtaining featurized content items with derived features;
- determining evaluation values of the derived first set of one or more content item features for each of the content items of the set of raw data by evaluating the content items of the set of raw data with respect to the derived first set of one or more content item features;
- associating the evaluation values of the derived first set of one or more content item features with each of the content items of the set of raw data to obtain the set of featurized data;
- and
- training the machine learning models using the set of featurized data as training data.
8 more elements
Dependencies are inferred from the claim text.