Ancestry.com Operations Inc.

Rey Furner

Tyler Folkman

Ian James Stiles

Eugene Greenwood

Donald B Curtis

Peng Jiang

Lei Wu

Atanu Roy

Drew Pearson

Jianlong Qi

Aaron Ling

Douglas Garry Earl

Bryce Damon Ririe

Matt Rasmussen

Geoffrey Scott Slinker

Kaleb Benjamin White

Anne Gillespie Mitchell

Systems, methods, and other techniques for genealogical entity resolution. In some embodiments, first tree data and second tree data are obtained, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. A set of features is extracted from the first tree data and the second tree data. An individual-level similarity score for each possible pairing of tree persons is generated based on the set of features. A set of most-similar tree persons is identified based on the individual-level similarity score for each possible pairing. A plurality of individual-level similarity vectors for the set of most-similar tree persons are provided as input to a family-level ML model to determine that the first tree person and the second tree person correspond to a same individual.

System and method for genealogical entity resolution

Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include identifying two tree persons in a genealogical database and extracting a plurality of features from both tree persons to generate two vectors. Embodiments also include calculating a plurality of metrics between the two vectors to generate a metric function. Embodiments further include generating feature weights using a recursive process based on training data input by external users, and generating a score by calculating a weighted sum of the metric function being weighted by the feature weights. The generated score may then be compared to a threshold value.

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

A computing server may receive genealogical records that include historical records of deceased individuals. The computing server may normalize the genealogical records into normalized genealogical records. Normalizing the genealogical records may include converting a text string of a genealogical record into a standardized format. The computing server may stitch the normalized genealogical records into a plurality of clusters. Each cluster corresponds to an individual and includes one or more genealogical records associated with the individual. The computing server may identify a life-event record that is commonly associated with a subset of clusters, the life-event record indicating that a plurality of deceased individuals are connected through a non-familial relationship in a life event documented by the life-event record. The computing server may cause a graphical user interface to display a representation of a historical network among the plurality of deceased individuals that are connected through the non-familial relationship.

Determining relationships of historical data records

Systems, methods, and computer-program products for entity resolution are disclosed. Entity resolution embodiments include receiving tree data from each of a pair of entities, extracting and/or aggregating feature vectors or metric functions therefrom, and generating similarity scores between the pair of entities. The similarity scores may be weighted using machine-learned weights. The weighted similarity scores are used to generate a combinatorial probability score accounting for combined likelihoods of field values between the pair of entities. A classification of the pair of entities is performed based on the combinatorial probability score, with a genealogical database modified based on the classification.

Entity resolution in genealogical databases

Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include obtaining, from a tree database, a first tree person from a first genealogical tree and a second tree person from a second genealogical tree. Embodiments also include identifying a plurality of familial categories. Embodiments further include, for each familial category of the plurality of familial categories, extracting a first quantity of features for each of the tree persons in the familial category, generating a first similarity score for each possible pairing of tree persons, identifying a representative pairing based on a maximum first similarity score, and extracting a second quantity of features for each of the tree persons in the representative pairing. Embodiments may also include generating a second similarity score based on the second quantity of features.

Genealogical entity resolution system and method

Systems, methods, and other techniques for genealogical entity resolution. The systems obtain first tree data and second tree data, the first tree data corresponding to a first tree person and the second tree data corresponding to a second tree person. The systems extract a set of features from the first tree data and the second tree data. The systems further generate an individual-level similarity score for each possible pairing of tree persons based on the set of features to identify a set of most-similar tree persons based on the individual-level similarity score for each possible pairing. The systems also provide a plurality of individual-level similarity scores for the set of most-similar tree persons as input to a family-level ML model to determine that the first tree person and the second tree person correspond to a same individual.

A data record management system develops person match scores for data records that may relate to the same person or subject. In a genealogical system where the data records are represented as nodes in a family tree, and where multiple data records may relate the same person in question, the data record management system evaluates not only two data records to determine if they relate to the same person, but also individually evaluate various data records pertaining to people that are related to the person in question. Person match scores are calculated by combining sub-scores based on the comparison of corresponding nodes. Sub-scores for nodes pertaining to related people may be adjusted higher even if those notes do not match, to reflect circumstances such as remarriage.

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Lehi, UT, United States of America

Rey Furner