Mike Dillinger

Knowledge graphs provide the digital foundation for some of most visible companies on the web.

Mike Dillinger built LinkedIn's Economic Graph, the knowledge graph that powers the social media giant's recommendation systems.

Mike now helps people understand knowledge graph technology and how it can complement and improve generative AI, whether by acting as "jet fuel" to better train LLMs or by providing "adult supervision" for their unruly, adolescent behavior.

We talked about:

how he describes knowledge graphs

how the richness of information in a knowledge graph helps computers better understand the things in a system

the differences between knowledge graphs and LLMs

how LinkedIn's Economic Graph, which Mike's team built, works

how LLMs can help build knowledge graphs, and how knowledge graphs can act as "jet fuel" to train LLMs

the RDF "triples" that are at the foundation of knowledge graphs

the importance of distinguishing between unique concepts in a knowledge graph and how practitioners do this

the two main crafts needed to build knowledge graphs: linguistic expertise and software engineering

the job opportunities for language professionals in the LLM and knowledge graph worlds

the propensity of tech companies to staff knowledge graph efforts with engineers while there is actually a need for a variety of talent, as well as better collaboration skills

his assertion that "language professionals aren't janitors," put on teams only to clean up data for software engineers

how knowledge graphs provide "adult supervision" for unruly, adolescent LLMs

his hypothesis that using KGs as a separate modality of data rather than as training data for LLMs will advance AI

Mike's bio

Mike Dillinger, PhD is a technical advisor, consultant, and thought leader who champions the importance of capturing and leveraging reusable, explicit human knowledge to enable more reliable machine intelligence. He was Technical Lead for Knowledge Graphs in the AI Division at LinkedIn and for LinkedIn’s and eBay’s first machine translation systems. He was also an independent consultant specialized in deploying translation technologies for Fortune 500 companies, and Director of Linguistics at two machine translation software companies where he led development of the first commercial MT-TM integration. He was President of the Association for Machine Translation in the Americas and has two MT-related patents. Dr. Dillinger has also taught at more than a dozen universities in several countries, has been a visiting researcher on four continents, and has a weekly blog on Knowledge Architecture.

Connect with Mike online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/wX2C3DwiWG4

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 2. If you've ever looked for a job or recruited talent on LinkedIn, you've seen Mike Dillinger's work. His team built LinkedIn's Economic Graph, the knowledge graph that powers the social media platform's recommendation system. These days, Mike thinks a lot about how knowledge graph technology can work with generative AI, seeing opportunities for the technologies to help the other, like the ability of knowledge graphs to act as "jet fuel" to train large language models.

Interview transcript

Larry:

Hi everyone. Welcome to Episode Number 2 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the program Mike Dillinger. Mike is a cage-free consultant based in San Jose. He's been doing knowledge graph and other technical things for many years. So welcome, Mike. Tell the folks a little bit more about what you're up to these days.

Mike:

Thanks a lot, Larry. What am I up to? I'm trying to help people wrap their heads around knowledge graphs and how we can make AI, transform GenAI into Next-GenAI by leveraging more explicit content and human knowledge.