Fuzzy search of graph database
AssigneeWells Fargo Bank, N.A.
InventorsDavid Newman, Omar B. Khan, Nathan Anthony Susanj, Alexander Joseph Kalinowski
Life of this patent
Estimating the term…
Abstract
A method may compiling, using at least one processor, a corpus of training data by obtaining, for each respective concept object in an ontology, a respective concept label and respective annotations describing the respective concept object; generating a vocabulary of terms based on the corpus of training data; training a semantic model using the corpus of training data and the vocabulary of terms, wherein input features for the semantic model are based on context words in proximity to a term in the vocabulary of terms; and storing a set of word embeddings for the vocabulary of terms based on the trained semantic model.
Claims17
A computer-implemented method comprising:
- compiling, using at least one processor, a corpus of training data by obtaining, for each respective concept object in an ontology, a respective concept label and respective annotations describing the respective concept object;
- generating a vocabulary of terms based on the corpus of training data;
- training a semantic model using the corpus of training data and the vocabulary of terms, wherein input features for the semantic model are based on context words in proximity to a term in the vocabulary of terms,
- wherein the semantic model is a neural network and wherein an input layer of the neural network and an output layer of the neural network are of a size equivalent to a number terms in the vocabulary of terms;
- and
- wherein training the semantic model includes using word pairs from the corpus of training data, a first word of a word pair being an input and a second word of the word pair being an expected output;
- and
- storing a set of word embeddings for the vocabulary of terms based on the trained semantic model.
A computer-implemented method comprising:
- receiving, at a processing computing system, a search query from a user;
- querying a data store for a search result using the search query as an input, the data store including a plurality of concept objects for an ontology;
- in response to the search result indicating a concept object for the input is not in the data store:
- determining a set of result concept objects for the search query based on a closeness of the search query to the plurality of concept objects in the data store, wherein closeness is determined according to word embeddings from a semantic model trained using data based on the ontology, wherein the semantic model is a neural network and wherein an input laver of the neural network and an output layer of the neural network are of a size equivalent to a number terms in a vocabulary of terms;
- presenting the set of result concept objects to the user;
- and
- receiving a selection of a concept object of the set of result concept objects from the user;
- and
- presenting data retrieved from the data store for the selected concept object.
A non-transitory computer-readable medium comprising instructions, which when executed by at least one processor, configure the at least one processor to perform operations comprising:
- compiling, using the at least one processor, a corpus of training data by obtaining, for each respective concept object in an ontology, a respective concept label and respective annotations describing the respective concept object;
- generating a vocabulary of terms based on the corpus of training data;
- training a semantic model using the corpus of training data and the vocabulary of terms, wherein input features for the semantic model are based on context words in proximity to a term in the vocabulary of terms,
- wherein the semantic model is a neural network and wherein an input layer of the neural network and an output layer of the neural network are of a size equivalent to a number terms in the vocabulary of terms;
- and
- wherein training the semantic model includes using word pairs from the corpus of training data, a first word of a word pair being an input and a second word of the word pair being an expected output;
- and
- storing a set of word embeddings for the vocabulary of terms based on the trained semantic model.
A system comprising:
- at least one processor;
- and
- a storage device comprising instructions, which when executed by the at least one processor, configure the system to:
- compile a corpus of training data by obtaining, for each respective concept object in an ontology, a respective concept label and respective annotations describing the respective concept object;
- generate a vocabulary of terms based on the corpus of training data;
- train a semantic model using the corpus of training data and the vocabulary of terms, wherein input features for the semantic model are based on context words in proximity to a term in the vocabulary of terms,
- wherein the semantic model is a neural network and wherein an input layer of the neural network and an output layer of the neural network are of a size equivalent to a number terms in the vocabulary of terms;
- and
- wherein to train the semantic model, word pairs are used from the corpus of training data, a first word of a word pair being an input and a second word of the word pair being an expected output;
- and
- store a set of word embeddings for the vocabulary of terms based on the trained semantic model.
Dependencies are inferred from the claim text.