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

**Resources referenced in this session:**

[github.com/whyhow-ai/knowledge-table](https://github.com/whyhow-ai/knowledge-table)

[github.com/whyhow-ai/knowledge-graph-studio](https://github.com/whyhow-ai/knowledge-graph-studio)

[github.com/whyhow-ai/knowledge-graph-studio](https://github.com/whyhow-ai/knowledge-graph-studio)[github.com/whyhow-ai/knowledge-table](https://github.com/whyhow-ai/knowledge-table)

[synthetichealth.github.io/synthea/](https://synthetichealth.github.io/synthea/)

[hl7.org/fhir/patient.schema.json.html](https://www.hl7.org/fhir/patient.schema.json.html)

**Case Studies referenced in this session:**

[medium.com/enterprise-rag/case-study-turning-congressional-hearing-transcripts-into-temporal-knowledge-graphs-0d78075181c7](https://medium.com/enterprise-rag/case-study-turning-congressional-hearing-transcripts-into-temporal-knowledge-graphs-0d78075181c7)

[medium.com/enterprise-rag/case-study-turning-doctor-transcripts-into-temporal-medical-record-knowledge-graphs-cf624d4927eb](https://medium.com/enterprise-rag/case-study-turning-doctor-transcripts-into-temporal-medical-record-knowledge-graphs-cf624d4927eb)

[medium.com/enterprise-rag](https://medium.com/enterprise-rag)

**Extracting Insights From Unstructured Text**

Temporal knowledge graphs provide new ways to extract insights from unstructured text data. By discovering relationships and dynamically identifying patterns, these graphs make it possible to track trends, identify key actors, and map shifts in topics or events. 

Extracting entities, modeling how relationships change over time, and structuring the evolving data all require overcoming substantial technical hurdles. While temporal graphs offer significant theoretical promise, practical implementation reveals unexpected complexities—from managing incomplete time data to integrating models which keep pace with evolving information.

In this podcast, we’re joined by both Chia Jeng Yang and Tom Smoker from [WhyHow.AI](http://whyhow.ai), as they share real-world lessons from constructing temporal knowledge graphs using [congressional hearing transcripts](https://medium.com/enterprise-rag/case-study-turning-congressional-hearing-transcripts-into-temporal-knowledge-graphs-0d78075181c7). We’ll look at the practical techniques they used to transform messy, text-based data into meaningful, actionable knowledge, and discuss strategies for handling evolving datasets in research and analytics.

### **Why Attend?**

*   Gain practical insights into building and deploying temporal knowledge graphs.
*   Learn strategies for extracting relationships and patterns from complex text data.
*   Leverage state-of-the-art models for knowledge graph construction.
*   Discover real-world applications for temporal graph modeling in research and analytics.
