Juan Sequeda - The Power of Knowledge Graphs and LLMs on Structured Data in the Enterprise
Sep 15, 2023
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Juan Sequeda, expert in knowledge graphs and pioneer in the field, discusses the potential of LLMs on structured datasets, the transition to a knowledge-first world, the role of knowledge graphs in enterprise structured data, the power of knowledge graphs in understanding RDF data, and the benefits of utilizing large language models for small to medium businesses.
Implementing chat GPT requires collaboration between legal, privacy, business, and tech teams to establish policies and guidelines for automating tasks and enhancing productivity in companies.
Integrating knowledge graphs and large language models can enhance data modeling, semantic layers, and overall data management, presenting an opportunity for companies to drive innovation and improve efficiency.
Companies must ensure their data house is in order by implementing a data catalog and managing data quality before leveraging large language models and knowledge graphs, as this helps streamline data management processes and enable better decision-making.
Deep dives
Using large language models to improve productivity
Large language models like chat GPT can greatly improve productivity in small to medium companies. By utilizing the capabilities of chat GPT, companies can automate tasks, generate emails faster, and speed up coding processes. Implementing chat GPT requires collaboration between legal, privacy, business, and tech teams to establish policies and guidelines. Companies should define risk profiles and outline what data can and cannot be used. While legal and privacy concerns are valid, embracing the use of chat GPT can significantly enhance productivity and competitiveness in the market.
Knowledge Graphs and AI: A Perfect Match
Knowledge graphs and large language models complement each other, providing a powerful combination for data teams. Knowledge graphs bring context and explainability to AI systems, while large language models offer general knowledge and natural language processing capabilities. Integrating these two technologies can enhance data modeling, semantic layers, and overall data management. The evolving landscape of AI and knowledge graphs presents an opportunity for companies to drive innovation and improve efficiency.
Data House in Order: The Foundation for AI Success
Companies must first ensure their data house is in order before leveraging large language models and knowledge graphs. This involves implementing a data catalog and managing data quality. The combination of a well-structured data catalog and a knowledge graph helps streamline data management processes and enable better decision-making. Data teams play a critical role in leveraging AI technologies effectively and maximizing their impact.
Paradigm Shift: From Data-First to Knowledge-First
The industry is experiencing a paradigm shift from a data-first approach to a knowledge-first approach. This shift involves shifting focus from technical aspects of data management to a more social-technical perspective. Embracing knowledge graphs as a central part of the data infrastructure enables the connection of data, metadata, and context. Companies that embrace this paradigm shift will be better equipped to leverage AI technologies for improved productivity and innovation.
Driving Innovation with Large Language Models
Large language models like chat GPT offer significant potential for driving innovation and productivity in small to medium companies. By adopting these models, companies can automate tasks, improve customer service, enhance data analysis, and more. Proper implementation requires collaboration between legal, privacy, and tech teams, along with clear guidelines and policies. The adoption of large language models is crucial for companies aiming to stay competitive and leverage AI technologies effectively.
Juan Sequeda and I chat about knowledge graphs (he's an OG in this area), the potential of LLMs on structured datasets, and much more. This is an honest, no-BS chat about the transition from a data-first world to a knowledge-first world. Enjoy!