Panos Alexopoulos
Any knowledge graph or other semantic artifact must be modeled before it's built.
Panos Alexopoulos has been building semantic models since 2006. In 2020, O'Reilly published his book on the subject, "Semantic Modeling for Data."
The book covers the craft of semantic data modeling, the pitfalls practitioners are likely to encounter, and the dilemmas they'll need to overcome.
We talked about:
his work as Head of Ontology at Textkernel and his 18-year history working with symbolic AI and semantic modeling
his definition and description of the practice of semantic modeling and its three main characteristics: accuracy, explicitness, and agreement
the variety of artifacts that can result from semantic modeling: database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies, etc.
the difference between identifying entities with human understandable descriptions in symbolic AI and numerical encodings in sub-symbolic AI
the role of semantic modeling in RAG and other hybrid AI architectures
a brief overview of data modeling as a practice
how LLMs fit into semantic modeling: as sources of information to populate a knowledge graph, as coding assistants, and in entity and relation extraction
other techniques besides NLP and LLMs that he uses in his modeling practice: syntactic patterns, heuristics, regular expressions, etc.
the role of semantic modeling and symbolic AI in emerging hybrid AI architectures
the importance of defining the notion of "autonomy" as AI agents emerge
Panos' bio
Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.
Connect with Panos online
LinkedIn
Video
Here’s the video version of our conversation:
https://youtu.be/ENothdlfYGA
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 23. In order to build a knowledge graph or any other semantic artifact, you first need to model the concepts you're working with, and that model needs to be accurate, to explicitly represent all of the ideas you're working with, and to capture human agreements about them. Panos Alexopoulos literally wrote the book on semantic modeling for data, covering both the principles of modeling as well as the pragmatic concerns of real-world modelers.
Interview transcript
Larry:
Hi everyone. Welcome to episode number 23 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Panos Alexopoulos. Panos is the head of ontology at Textkernel, a company in Amsterdam that works on knowledge graphs for the HR and recruitment world. Welcome, Panos. Tell the folks a little bit more about what you're doing these days.
Panos:
Hi Larry. Thank you very much for inviting me to your podcast. I'm really happy to be here. Yeah, so as you said, I'm head of ontology at Textkernel. Actually, I've been working in the field of data semantics, knowledge graph ontologies for almost now 18 years, even before the era of machine learning,
