George Anadiotis

Every profession has its connectors, sharers, and community organizers. In the knowledge graph world, George Anadiotis fills all of these roles.

Through his industry analysis and reporting, his conference organizing, and his writing and podcasting, George connects ideas and people across the semantic-tech landscape.

We talked about:

his work at Linked Data Orchestration and as a consultant and analyst in the knowledge graph and linked-data world

his diverse background in computing and his studies at the intersection of knowledge management, the semantic web, and distributed systems

his extensive writing experience and consulting background

his definition of a knowledge graph

the differences between RDF-based knowledge graphs and labeled property graphs (LPG)

the focus in the RDF community on standards and interoperability versus the focus in the LPG community on implementation

the variety of query languages in the LPG world and recent efforts like GQL to create a standard way of querying LPGs, as well as efforts to query across both RDF and LPG graphs

the origins of his annual Year of the Graph report

some of the reasons that knowledge graphs are positioned in the bullseye of Gartner's Impact Radar this year

where knowledge graphs fit in the AI landscape

the role of knowledge graphs in RAG architectures

the conference he organizes, Connected Data London, coming up December 11-13

George's bio

George Anadiotis has got tech, data, AI and media, and he's not afraid to use them.

He helps organizations map and understand complex domains to make better decisions; design, implement and monitor models, processes and systems to achieve goals; and craft communication strategies and outreach initiatives to grow awareness and market share.

He enjoys researching, developing, applying, writing and talking about cutting edge concepts and technology, and their implications on society and business.

Connect with George online

LinkedIn

Twitter

TikTok

Instagram

George's publications, podcasts, and conference

Connected Data London (conference roundtable recording)

The Year of the Graph

Orchestrate All the Things

Video

Here’s the video version of our conversation:

https://youtu.be/lEHj5_9y-30

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 3. In any domain, there are people who seem to do it all - practice and consultation, industry analysis and reporting, and community building and event organizing. In the world of knowledge graphs and the semantic web, George Anadiotis has filled all of these roles. Whether he's publishing his Year of the Graph newsletter, organizing the annual Connected Data conference, or producing the latest Orchestrate All the Things podcast, George is always connecting the dots.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number three of the Knowledge Graph Insights Contest. Sorry, I'm going to redo that again. I have too many podcasts. I need a new intro for this one. Okay. Hi, everyone. Welcome to episode number three of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show George Anadiotis. George is really well known in the knowledge graph world and the graph world in general and the tech world in general, as an analyst, a consultant, a really well-developed engineer. He runs a big conference around knowledge graph and graph technology, and he is the principal at his organization called Linked Data Orchestration. So welcome, George, tell the folks a little bit more about what you're up to these days.

George:

Great. Thanks for the intro, Larry, and good to be here. Actually one of the opening, I guess, guests for this new podcast series of yours. Well, the truth is I have a long and kind of convoluted story, but I've kind of honed my skills of telling it in as simple way as possible.

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.