Graph + AI Summit was the first and only open conference for accelerating analytics, AI and machine learning with graph algorithms.
Terry Goodman was invited to join a highly esteemed group of speakers at the inaugural Spring 2021 Graph+AI Summit. His highly informative presentation delved into modern techniques for optimising data quality for identity resolution with graph databases. The contents of the Identity resolution with graph databases presentation included:
- Introduction and definitions
- Graph schema design(s) for identity resolution
- Transforming/parsing data to optimised schema design
- Enhancing data for identity resolution
- Identity resolution algorithms with graph.
The full presentation can be viewed in the video below.
Techniques for optimising data quality for identity resolution with graph databases.
Data collected by organizations is increasing at a relentless pace, but can still give a misleading or fragmented view of the real world.
For example, a person could appear multiple times or have multiple digital entities within the same database, due to typos, name changes, aggregation of different systems and so on. So, how do we match entities when the ID systems may be different or contain errors?
Entity resolution (ER) helps get to the truth. Entity resolution, which is the disambiguation of real-world entities in a database, is an essential data quality tool.
Graph provides an efficient approach for the entity resolution problem. A native graph database with massive parallel computing capability is the best tool to implement the approach.
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