Systems and methods for large scale global entity resolution

A system, comprising:

• one or more processors;
• a memory device operatively coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform functions that comprise:
• ingesting text data from a plurality of documents containing a plurality of mentions;
• locating, from the text data, for each of a selected plurality of chains of coreferent mentions, a particular context-based name from the respective chain, wherein the coreferent mentions correspond to entities and the context-based name is a longest name in the respective chain, a last name in the respective chain, or a most frequently occurring name in the respective chain;
• determining an entity category for each respective one of the plurality of chains;
• determining one or more entity attributes from structured data and unstructured data;
• based on the located particular context-based name, the entity category, and the one or more attributes, assigning high-probability coreferent chains to high-confidence buckets, such as to produce a power law probability distribution having a head region and a tail region;
• and
• resolving, based at least in part on the power law probability distribution, the coreferent mentions to identify corresponding real-world entities.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause a computer to perform functions that comprise:

• ingesting text data from a plurality of documents containing a plurality of mentions;
• locating, from the text data, for each of a selected plurality of chains of coreferent mentions, a particular context-based name from the respective chain, wherein the coreferent mentions correspond to entities and the context-based name is a longest name in the respective chain, a last name in the respective chain, or a most frequently occurring name in the respective chain;
• determining an entity category for each respective one of the plurality of chains;
• determining one or more entity attributes from structured data and unstructured data;
• based on the located particular context-based name, the entity category, and the one or more attributes, assigning high-probability coreferent chains to high-confidence buckets, such as to produce a power law probability distribution having a head region and a tail region;
• and
• resolving, based at least in part on the power law probability distribution, the coreferent mentions to identify corresponding real-world entities.