Sas Institute Inc.

Nicholas Akbar Ablitt

James Byron Morris

Manoj Keshavmurthi Chari

The computing device trains a first model on a first data set using a first graph to predict relevant links between a plurality of nodes. The computing device obtains the first data set or a second data set associated with the plurality of nodes. The computing device determines the one or more features for the one or more links between the plurality of nodes, applies the trained first model to the one or more links between the plurality of nodes, outputs the relevant links and non-relevant links of the one or more links between the plurality of nodes, removes the non-relevant links between the plurality of nodes, connects each node of the plurality of nodes with the relevant links to generate one or more first sets of networks, and outputs the one or more first sets of generated networks.

Method and system for predicting relevant network relationships

The computing device trains a first model on a first data set using a first graph to predict relevant links between a plurality of nodes. The computing device applies the trained first model to the one or more links between the plurality of nodes from a first node, iteratively connects each node to the one or more first sets of generated networks for each of the relevant links until the relevant links for connection to the plurality of nodes are not present, and outputs the one or more first sets of generated networks. The computing device also applies the trained first model to the one or more links between the plurality of nodes, removes the non-relevant links, connects each node of the plurality of nodes with the relevant links to generate one or more second sets of networks, and outputs the one or more second sets of generated networks.

One example described herein involves a system that can receive a set of data records and execute an automated entity resolution (AER) process configured to assign the set of data records to a set of entities. For each entity in the set of entities, the system can generate a respective consistency score for the entity, generate a respective confidence score for the entity based on the respective consistency score for the entity, and determine a respective visual indicator based on the respective confidence score for the entity. The respective visual indicator can indicate a risk of record misassignment to a user. The system can then generate a graphical user interface that includes the respective visual indicator for each of the entities.

Combining user feedback with an automated entity-resolution process executed on a computer system

A computing system displays an initial graph with icons. Each icon graphically represents data associated with a respective entity. The first icon is connected in the initial graph to other icon(s). The system receives an indication of a graphical network pattern. The graphical network pattern is defined by a user selection of a second icon in the initial graph and: a user selection of a third icon in the initial graph; or a user selection of a graphical representation in the initial graph of a relationship between the second icon and the third icon. The system sends computer instructions indicating a network pattern query for searching an electronic database for electronic record(s) corresponding to a queried network pattern. The system receives a dataset indicating located electronic record(s) corresponding to the queried network pattern. The system generates output data indicating an output graph for a graphical representation of the located record(s).

Graphical user interface for searching on a network pattern

A computing device obtains an indication of data records resolved to describe a single entity in an entity resolution. The data records comprise peripheral records resolved to describe the single entity based on matching data of a central record of the data records. The device generates an indication indicating that at least one of the first peripheral record and the second peripheral record does not describe the single entity by setting a first one of the data records as a source; and setting a second one of the data records as a sink. The device generates a data structure identifying record linkage information for records of the dataset. The record linkage information indicates one or more pathways between the source and the sink along the linked records. The device executes a minimum cut algorithm to identify one or more connections of the one or more pathways to unlink.

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Putney, United Kingdom

Nicholas Akbar Ablitt