Senzing is the first to deliver real-time AI for entity resolution. Senzing entity resolution
provides organizations with new insights to make better business decisions while reducing
operational costs. The Senzing API is the fastest and easiest way for commercial software
vendors, and organizations in financial services, public sector, information services and other industries to integrate highly accurate, flexible and scalable entity resolution capabilities into applications, data fabrics and other data services. Senzing is Smarter Entity Resolution. 

Senzing

*   Mike's presentation <https://bit.ly/gphv1e5>
*   Connect with Mike [https://www.linkedin.com/in/mikedillinger/](https://www.linkedin.com/in/mikedillinger)
*   Interesting reads:
*   [https://www.linkedin.com/newsletters/knowledge-architecture-7149627777497841664/](https://www.linkedin.com/newsletters/knowledge-architecture-7149627777497841664)
*   [https://www.linkedin.com/pulse/new-years-resolutions-your-knowledge-graphs-mike-dillinger-phd-g7dsc/](https://www.linkedin.com/pulse/new-years-resolutions-your-knowledge-graphs-mike-dillinger-phd-g7dsc)
*   [https://www.linkedin.com/pulse/knowledge-graphs-artificial-intelligence-mike-dillinger-phd-zxaac/](https://www.linkedin.com/pulse/knowledge-graphs-artificial-intelligence-mike-dillinger-phd-zxaac)
*   Graph of Relations analysis <https://arxiv.org/abs/2305.19987v3>
*   Taxonomies: Connecting Data with Duct Tape <https://www.youtube.com/watch?v=gvoU75vHPQI>

**About this Episode**

Building a knowledge graph isn’t just about gathering and connecting data, it’s about making sense of the entities and relationships represented within it. On the one hand, poorly resolved entities and vague connections will weaken a graph’s ability to deliver meaningful insights. On the other hand, let’s say you have a well-constructed graph and intend to use it for powering AI systems downstream … How do the choices you make about representing your entities and relations have an impact on AI applications?

 

Mike Dillinger is an expert in knowledge graphs and computational linguistics, with key leadership roles at LinkedIn developing their graph assets. In a recent series of articles, Mike advocates a methodical approach to knowledge graph development to support more effective AI systems through what might be called a “shift-left” strategy: provide structured knowledge input – *artificial knowledge* – to enable machine intelligence. Build on *entity resolution* as a required first step, then apply systematic approaches for *relation resolution* as well. We need to measure the diversity, depth, and density of relations so they can be categorized and clearly defined, and then structure the predicates we use – just as we structure our entities. The result is a best-of-breed mix of knowledge graphs and deep learning, aka *neuro-symbolic systems*, more effective and scalable.

 

In this session, Paco Nathan and Mike Dillinger will dive into the critical role of both entity resolution and relation resolution in knowledge graph construction. We’ll explore the challenges of defining clear, accurate relationships and discuss techniques for improving how connections are identified, structured, and maintained over time. Whether you’re building a new graph or enhancing an existing one, you’ll gain practical strategies to ensure your graph remains scalable, flexible, and aligned with evolving data.
