Machine-learning system and method for identifying same person in genealogical databases

A non-transitory computer readable storage media comprising instructions to cause one or more processors to perform operations comprising:

• identifying a first tree person from a first genealogical tree and a second tree person from a second genealogical tree, wherein both the first genealogical tree and the second genealogical tree comprise a plurality of interconnected tree persons corresponding to individuals that are related to each other;
• extracting a plurality of features from both the first tree person and the second tree person to generate a first feature vector and a second feature vector;
• after extracting the plurality of features from both the first tree person and the second tree person, calculating a plurality of metrics between the first feature vector and the second feature vector to generate a metric function containing a plurality of values, wherein each of the plurality of metrics corresponds to a similarity between a first feature from the first feature vector and a second feature from the second feature vector that corresponds to the first feature;
• generating feature weights having a plurality of values by inputting the plurality of features into a machine-learning model, wherein the machine-learning model is configured to output the feature weights based on receiving an input comprising the plurality of features, wherein, prior to generating the feature weights, the machine-learning model was trained by:
• providing training data comprising pairs of tree persons to the machine-learning model;
• and
• modifying the machine-learning model using an error computed based on an output of the machine-learning model when provided with the training data;
• and
• generating a score by calculating a weighted sum of the plurality of values of the metric function being weighted by the plurality of values of the feature weights, wherein the weighted sum is calculated by weighting each of the plurality of values of the metric function by a corresponding value from the plurality of values of the feature weights.

A system comprising:

• one or more processors;
• and
• one or more computer readable storage mediums comprising instructions to cause the one or more processors to perform operations comprising:
• identifying a first tree person from a first genealogical tree and a second tree person from a second genealogical tree, wherein both the first genealogical tree and the second genealogical tree comprise a plurality of interconnected tree persons corresponding to individuals that are related to each other;
• extracting a plurality of features from both the first tree person and the second tree person to generate a first feature vector and a second feature vector;
• after extracting the plurality of features from both the first tree person and the second tree person, calculating a plurality of metrics between the first feature vector and the second feature vector to generate a metric function containing a plurality of values, wherein each of the plurality of metrics corresponds to a similarity between a first feature from the first feature vector and a second feature from the second feature vector that corresponds to the first feature;
• generating feature weights having a plurality of values by inputting the plurality of features into a machine-learning model, wherein the machine-learning model is configured to output the feature weights based on receiving an input comprising the plurality of features, wherein, prior to generating the feature weights, the machine-learning model was trained by:
• providing training data comprising pairs of tree persons to the machine-learning model;
• and
• modifying the machine-learning model using an error computed based on an output of the machine-learning model when provided with the training data;
• and
• generating a score by calculating a weighted sum of the plurality of values of the metric function being weighted by the plurality of values of the feature weights, wherein the weighted sum is calculated by weighting each of the plurality of values of the metric function by a corresponding value from the plurality of values of the feature weights.