Mike Pool

Mike Pool sees irony in the fact that semantic-technology practitioners struggle to use the word "semantics" in ways that meaningfully advance conversations about their knowlege-representation work.

In a recent LinkedIn post, Mike even proposed a moratorium on the use of the word.

We talked about:

his multi-decade career in knowledge representation and ontology practice

his opinion that we might benefit from a moratorium on the term "semantics"

the challenges in pinning down the exact scope of semantic technology

how semantic tech permits reusability and enables scalability

the balance in semantic practice between 1) ascribing meaning in tech architectures independent of its use in applications and 2) considering end-use cases

the importance of staying domain-focused as you do semantic work

how to stay pragmatic in your choice of semantic methods

how reification of objects is not inherently semantic but does create a framework for discovering meaning

how to understand and capture subtle differences in meaning of seemingly clear terms like "merger" or "customer"

how LLMs can facilitate capturing meaning

Mike's bio

Michael Pool works in the Office of the CTO at Bloomberg, where he is working on a tool to create and deploy ontologies across the firm. Previously, he was a principal ontologist on the Amazon Product Knowledge team, and has also worked to deploy semantic technologies/approaches and enterprise knowledge graphs at a number of big banks in New York City. Michael also spent a couple of years on the famous Cyc project and has evaluated knowledge representation technologies for DARPA. He has also worked on tooling to integrate probabilistic and semantic models and oversaw development of an ontology to support a consumer-facing semantic search engine. He lives in New York City and loves to run around in circles in Central Park.

Connect with Mike online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/JlJjBWGwSDg

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 22. The word "semantics" is often used imprecisely by semantic-technology practitioners. It can describe a wide array of knowledge-representation practices, from simple glossaries and taxonomies to full-blown enterprise ontologies, any of which may be summarized in a conversation as "semantics." Mike Pool thinks that this dynamic - using a word that lacks precise meaning while assuming that it communicates a lot - may justify a moratorium on the use of the term.

Interview transcript

Larry:

Hi everyone, welcome to episode number 22 of the Knowledge Graph Insights podcast. I'm really happy today to welcome to the show Mike Pool. Mike is a longtime ontologist, a couple of decades plus. He recently took a position at Bloomberg. But he made this really provocative post on LinkedIn lately that I want to flesh out today, and we'll talk more about that throughout the rest of the show. Welcome, Mike, tell the folks a little bit more about what you're up to these days.

Mike:

Hey, thank you, Larry. Yeah. As you noted, I've just taken a position with Bloomberg and for these many years that you alluded to, I've been very heavily focused on building, doing knowledge representation in general. In the last let's say decade or so I've been particularly focused on using ontologies and knowledge graphs in large banks, or large organizations at least, to help organize disparate data, to make it more accessible, breakdown data silos, et cetera. It's particularly relevant in the finance industry where things can be sliced and diced in so many different ways. I find there's a really important use case in the financial space but in large organizations in general, in my opinion, for using ontology. So that's a lot of what I've been thinking about, to make that more accessible to the organization and to help them build these ontologies and utilize th...

Margaret Warren

As a 10-year-old photographer, Margaret Warren would jot down on the back of each printed photo metadata about who took the picture, who was in it, and where it was taken.

Her interest in image metadata continued into her adult life, culminating the creation of ImageSnippets, a service that lets anyone add linked open data descriptions to their images.

We talked about:

her work to make images more discoverable with metadata connected via a knowledge graph

how her early childhood history as a metadata strategist, her background in computing technology, and her personal interest in art and a photography shows up in her product, ImageSnippets

her takes on the basics of metadata strategy and practice

the many types of metadata: descriptive, administrative, technical, etc.

the role of metadata in the new AI world

some of the good and bad reasons that social media platforms might remove metadata from images

privacy implications of metadata in social media

the linked data principles that she applies in ImageSnippets and how they're managed in the product's workflow

her wish that CMSs and social media platforms would not strip the metadata from images as they ingest them

the lightweight image ontology that underlies her ImageSnippets product

her prediction that the importance of metadata that supports provenance, demonstrates originality, and sets context will continue to grow in the future

Margaret's bio

Margaret Warren is a technologist, researcher and artist/content creator. She is the founder and CEO of Metadata Authoring Systems whose mission is to make the most obscure images on the web findable, and easily accessible by describing and preserving them in the most precise ways possible.

To assist with this mission, she is the creator of a system called, ImageSnippets which can be used by anyone to build linked data descriptions of images into graphs. She is also a research associate with the Florida Institute of Human and Machine Cognition, one of the primary organizers of a group called The Dataworthy Collective and is a member of the IPTC (International Press and Telecommunications Council) photo-metadata working group and the Research Data Alliance charter on Collections as Data.

As a researcher, Margaret's primary focus is at the intersection of semantics, metadata, knowledge representation and information science particularly around visual content, search and findability. She is deeply interested in how people describe what they experience visually and how to capture and formalize this knowledge into machine readable structures. She creates tools and processes for humans but augmented by machine intelligence. Many of these tools are useful for unifying the many types of metadata and descriptions of images - including the very important context element - into ontology infused knowledge graphs. Her tools can be used for tasks as advanced as complex domain modeling but can also facilitate image content to be shared and published while staying linked to it's metadata across workflows.

Learn more and connect with Margaret online

LinkedIn

Patreon

Bluesky

Substack

ImageSnippets

Metadata Authoring Systems

personal and art site

IPTC links

IPTC Photo Metadata

Software that supports IPTC Photo Metadata

Get IPTC Photo Metadata

Browser extensions for IPTC Photo Metadata

Resource not mentioned in podcast

(but very useful for examining structured metadata in web pages)

OpenLink Structured Data Sniffer (OSDS)

Video

Here’s the video version of our conversation:

https://youtu.be/pjoAAq5zuRk

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 21. Nowadays, we are all immersed in a deluge of information and media, especially images. The real value of these images is captured in the metadata about them. Without information about the history of an image, its technical details,

Jans Aasman

Hybrid AI architectures get more complex every day. For Jans Aasman, large language models and generative AI are just the newest additions to his toolkit.

Jans has been building advanced hybrid AI systems for more than 15 years, using knowledge graphs, symbolic logic, and machine learning - and now LLMs and gen AI - to build advanced AI systems for Fortune 500 companies.

We talked about:

his knowledge graph and neuro-symbolic work as the CEO of Franz

the crucial role of a visionary knowledge graph champion in KG adoption in enterprises

the two types of KG champions he has encountered: the magic-seeking, forward-looking technologist and the more pragmatic IT leader trying to better organize their operation

the AI architectural patterns and themes he has seen emerge over the past 25 years: logic, reasoning, event-based KGs, machine learning, and of course gen AI and LLMs

how gen AI lets him do things he couldn't have imagined five years ago

the enduring importance of enterprise taxonomies, especially in RAG architectures which business entities need to be understood to answer complex business questions

his approach to neuro-symbolic AI, seeing it as a "fluid interplay between a knowledge graph, symbolic logic, machine learning, and generative AI"

the power of "magic predicates"

a common combination of AI technologies and human interactions that can improve medical diagnosis and care decisions

his strong belief in keeping humans in the loop in AI systems

his observation that technology and business leaders seeing the need for "a symbolic approach next to generative AI"

his take on the development of reasoning capabilities of LLMs

how the code-generation capabilities of LLMs are more beneficial to senior programmers and may even impede the work of less experiences coders

Jans' bio

Jans Aasman is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of Knowledge Graph Solutions based on AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as government entities worldwide.

Connect with Jans online

LinkedIn

email: ja at franz dot com

Video

Here’s the video version of our conversation:

https://www.youtube.com/watch?v=SZBZxC8S1Uk

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 20. The mix of technologies in hybrid artificial intelligence systems just keeps getting more interesting. This might seem like a new phenomenon, but long before our LinkedIn feeds were clogged with posts about retrieval augmented generation and neuro-symbolic architectures, Jans Aasman was building AI systems that combined knowledge graphs, symbolic logic, and machine learning. Large language models and generative AI are just the newest technologies in his AI toolkit.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 20 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Jans Aasmann. Jans is, he originally started out as a psychologist and he got into cognitive science. For the past 20 years, he's run a company called Franz, where he's the CEO doing neuro-symbolic AI, so welcome, Jans. Tell the folks a little bit more about what you're doing these days.

Jans:

We help companies build knowledge graphs, but with the special angle that we now offer neuro-symbolic AI so that we, in a very fluid way, mix traditional symbolic logic and the traditional machine learning with the new generative AI. We do this in every possible combination that you could think of.

Larry:

Who?

Jans:

These applications might be in healthcare or in call centers or in publishing. It's many, many, many different domains it supplies.

Larry:

Juan Sequeda

Knowledge graph technology has been around for decades. The benefits so far accruing to only a few big enterprises and tech companies.

Juan Sequeda sees large language models as a critical enabler for the broader adoption of KGs. With their capacity to accelerate the acquisition and use of valuable business knowledge, LLMs offer a path to a better return on your enterprise's investment in semantics.

We talked about:

his work data.world as Principal scientist and the head of the AI lab at data.world

the new discovery and knowledge-acquisition capabilities that LLMs give knowledge engineers

a variety of business benefits that unfold from these new capabilities

the payoff of investing in semantics and knowledge: "one plus one is greater than two"

how semantic understanding and the move from a data-first world to a knowledge-first world helps businesses make better decisions and become more efficient

the pendulum swings in the history of the development of AI and knowledge systems

his research with Dean Allemang on how knowledge graphs can help LLMs improve the accuracy of answers of questions posed to enterprise relational databases

the role of industry benchmarks in understanding the return on your invest in semantics

the importance of treating semantics as a first-class citizen

how business leaders can recognize and take advantage of the semantics and knowledge work that is already happening in their organizations

Juan's bio

Juan Sequeda is the Principal Scientist and Head of the AI Lab at data.world. He holds a PhD in Computer Science from The University of Texas at Austin. Juan’s research and industry work has been on the intersection of data and AI, with the goal to reliably create knowledge from inscrutable data, specifically designing and building Knowledge Graph for enterprise data and metadata management. Juan is the co-author of the book “Designing and Building Enterprise Knowledge Graph” and the co-host of Catalog and Cocktails, an honest, no-bs, non-salesy data podcast.

Connect with Juan online

LinkedIn

Catalog & Cocktails podcast

Video

Here’s the video version of our conversation:

https://youtu.be/xZq12K7GvB8

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 19. The AI pendulum has been swinging back and forth for many decades. Juan Sequeda argues that we're now at a point in the advancement of AI technology where businesses can fully reap its long-promised benefits. The key is a semantic understanding of your business, captured in a knowledge graph. Juan sees large language models as a critical enabler of this capability, in particular the ability of LLMs to accelerate the acquisition and use of valuable business knowledge.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 19 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Juan Sequeda. Juan is the principal scientist and the head of the AI lab at data.world. He's also the co-host of the really good popular podcast, Catalog & Cocktails. So welcome, Juan. Tell the folks a little bit more about what you're up to these days.

Juan:

Hey, very great. Thank you so much for having me. Great to chat with you. So what am I up to now these days? Obviously, knowledge graphs is something that is my entire life of what I've been doing. This was before it was called knowledge graphs. I would say that the last year, year-and-a-half, almost two years now, I would say, is been understanding the relationship between knowledge graphs and LLMs. If people have been following our work, what we've been doing a lot has been on understanding how to use knowledge graphs to increase the accuracy for your chat with your data system, so be able to do question answering over your structured SQL databases and how knowledge graphs increase the accuracy of that. So we can chat about that.

Juan:

Jesús Barrasa

Over his 20-year career, Jesús Barrasa has spanned the worlds of object-oriented property graphs and assertion-based knowledge graphs.

He knows as much about these two foundational technologies as anyone and offers pragmatic advice to help architects and engineers decide which approach will work best for their needs.

We talked about:

his role at Neo4j in which he helps companies adopt graph technology

his academic study of semantic technology and his early work on mapping relational data to ontologies and enterprise ontologies

his move across the graph spectrum from RDF graphs to property graphs, culminating in his current role at Neo4j

his take on the similarities and differences between RDF and property graph approaches, the key commonality being linked data and the key distinction between them being the level of abstraction that they employ

different ways to approach inference

the origins of the semantic web and how it has made data actionable and interoperable and smarter

how the professional backgrounds of software developers can affect their choice of graph technologies

the crucial role of interoperability in graph technology, and our ongoing inability to productively harness it

how semantics is managed and used in the property graph and RDF worlds

ontology as a technology-independent way of representing knowledge

the importance of staying focused on the needs of practitioners when advising them on how to make a graph technology choice

how knowledge graphs can balance the "opaque power" of large language models with "explicit, declarative, explainable power"

Jesús' bio

Dr. Jesús Barrasa is Neo4j's AI Field CTO and the company's resident expert in Knowledge Graphs and Semantic Technologies. He co-authored the O'Reilly book "Building Knowledge Graphs: A Practitioner's Guide" (released in July 2023) and combines over 20 years of professional experience in the data management space split between industry and research and academia.

Prior to joining Neo4j, Jesús worked for data integration companies like Denodo and Ontology Systems (now EXFO), where he gained first-hand experience with many successful enterprise-wide data integration deployments and large graph technology projects enhancing the operations and analytics of major companies worldwide.

Jesús' doctoral work in Artificial Intelligence and Knowledge Representation focused on the automatic repurposing of legacy data as knowledge graphs. He's an active thought leader in the graph and semantics communities and co-hosts the popular monthly webcast on knowledge graphs "Going Meta."

Connect with Jesús online

LinkedIn

Going Meta webcast.

Video

Here’s the video version of our conversation:

https://youtu.be/7WFP_oDQsxI

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 18. If you search for the term "knowledge graph," you're likely to get an equal number of results about property graphs and RDF-based graphs. Jesús Barrasa has been immersed in both of those technologies for more than 20 years. He takes a pragmatic approach to graph technology adoption, focusing on the needs of practitioners and on the ability of knowledge graphs to balance the "opaque power" of large language models with the explainable power of knowledge graphs.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 18 of the Knowledge Graph Insights Podcast. I am really delighted to welcome to the show Jesús Barrasa. Jesús is about as graph-ey a person as it gets. I got to say he has a 20-year background in this stuff. He's currently the AI field CTO, Chief Technical Officer for the graph database company Neo4j. Welcome to the show, Jesús. Tell the folks a little bit more about what you're doing these days.

Jesús:

Hi Larry. Thank you very much. I'm really, really glad to be here. I mean, we've been trying to plan this for a while now. Really,

Yaakov Belch

Yaakov Belch is an AI researcher with strong ideas about the role of humans in AI systems. Instead of "human in the loop," he argues, we should put "humans in control."

Yaakov's research looks at business contracts and how knowledge graphs and AI systems can both capture their meaning more accurately and help managers make better business decisions.

We talked about:

his assertion that we need humans in control, not just in the loop

his research on applying AI technology to business contracts, in particular the issue of resolving inconsistencies in language model results

reasons to put human concerns ahead of any particular technology

the importance of having humans in control when interpreting ambiguous business decisions

the importance of both accounting for business intent and asking the right questions of your data and how the loop between the two tightens over time

the responsibility of human users to understand how LLMs work and to prompt and otherwise interact with them accordingly

why he doesn't use the term "hallucination" when talking about LLM outputs

the role and implications of applying different kinds of logic in the use of knowledge graphs

an analogy that shows how the concept of a Git fork can help knowledge graph engineers account in their models for different versions of reality

the real-world applications of his research, especially how the practices he is exploring can create new business value

the importance of building any model off of real data and always thinking about which human needs to be in control

Yaakov's bio

As a mathematician and data scientist, Yaakov Belch brings a unique perspective to the world of AI and knowledge graphs. With a strong background in mathematics, including participation in prestigious International Mathematical Olympiads, Yaakov went on to earn a Ph.D. in pure mathematics from the University of Cambridge.

Yaakov's career has spanned both research and industry roles. He has worked as an Algorithm Programmer, collaborating with researchers in bioinformatics and economics, co-authoring academic papers. Yaakov also served as a Senior Data Scientist at Israeli e-commerce startups, where he tackled challenges in symbolic and semantic search from different angles.

Currently, Yaakov is on a sabbatical, working as an independent Data Scientist to develop his method of reliable business reasoning, precise contract understanding, and humans-in-control.ai. He sees an interesting connection between the problems from the International Mathematics Olympiads and taming the inconsistencies of large language models: "At one hand, the problems are hard and just don't open up to the known, standard methods of the field. But you know that there is a beautiful solution. You need to find the right perspective to appreciate the problem and to see that beautiful solution."

Connect with Yaakov online

humans-in-control.ai

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/ZS9r0bfkGQc

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 17. Machine learning architects often talk about the "human in the loop." Yaakov Belch thinks that when it comes to language models the right approach is to put "humans in control." Yaakov's research looks at how knowledge graphs and large language models can help put humans in control of business contracts, capturing the actual intent that underlies them and facilitating better business decision-making based on the discoveries that they enable.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 17 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Yaakov Belch. Yaakov is an independent senior data scientist and he’s made this really provocative statement about … There’s all this talk in the AI world in general about humans in the loop. And Yaakov says, “No.

Michael Iantosca

Where content, knowledge management, and AI converge, you'll find Michael Iantosca.

As many in the AI world flock to probabilistic models like LLMs, Michael takes a deterministic approach to content management and knowledge engineering, using ontologies and knowledge graphs to ground content in a concrete facts.

This approach embodies his insight that content and the models that describe it are not static information but rather valuable, ever-evolving enterprise IP assets.

We talked about:

his 44-year career in content, knowledge management and localization/globalization roles

the three pillars of his work: content, knowledge management, and engineering

the need he sees in his work to move away from probabilistic, vector-based models to deterministic, neuro-symbolic models like knowledge graphs

how he decides which models are appropriate to use with each of the varied kinds of data he works with

his explorations of how to automatically construct a knowledge graph to use to power generative AI solutions

how he acquires and develops ontology skills in his team

how graph technology supports the "total content experiences" he builds

how the non-static nature of content makes it a poor candidate to be managed in a static system like a vector-based model

the relative merits and utility of 1) deterministic retrieval for structured content and 2) probabilistic retrieval for unstructured content

the power of combining content models, knowledge models, and ontologies and how they can become crucial enterprise IP assets

his belief that we are entering a golden age of content and knowledge engineering

Michael's bio

Michael Iantosca is the Senior Director of Knowledge Platforms and Engineering at Avalara, a sales tax automation company. With over four decades of leadership in technical content management, Michael has been a pioneer in advancing the profession, driving innovations in structured content, intelligent authoring, and scalable knowledge platforms. Renowned for bridging engineering and content teams, he has championed the adoption of AI and cutting-edge technologies to enhance user experience. A thought leader and mentor, Michael continues to shape the future of technical communication through his expertise and passion for innovation.

Connect with Michael online

LinkedIn

Medium

ThinkingDocumentation

Video

Here’s the video version of our conversation:

https://youtu.be/WG9Nl5OY3QI

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 16. A lot of work in the AI world these days is about vectorizing giant collections of static, unstructured content and data for LLMs. Michael Iantosca has worked for decades in a world where content is dynamic, always precisely structured, and contextualized with rich metadata. So he has a different take on architectural innovations like graph RAG, favoring knowledge-based deterministic retrieval of content over vector-based models and probabilistic methods.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 16 of the knowledge graph Insights podcast. I am really delighted today to welcome to the show, Michael Iantosca. Michael is currently the Senior Director of Knowledge Platforms and Engineering at Avalara, the big tax-compliance automation software company. He's also got a long history. He's spent a couple of decades, a few decades at IBM prior to his role at Avalara. Welcome to the show, Michael. Tell the folks a little bit more about what you're up to these days.

Michael:

Larry, thank you for having me. It's a pleasure and an honor to get a few minutes to talk to you today. Yeah, I have just started my 44th year primarily in the professional content space, but also in the knowledge management and localization globalization space as well. I have been involved with content since the early days of SGML that began the structured content revolution and wo...

Fran Alexander

When Fran Alexander looks at the current AI landscape she sees some interesting parallels between the Alien vs Predator science fiction franchise and the way RAG and other architectures are combining LLMs and knowledge graphs.

We talked about:

the analogy she draws between the Alien and Predator science fiction franchise with LLMs and knowledge graphs

how the human-esque (if malevolent) cognitive and behavioral nature of Predators aligns more with knowledge graphs and how the unpredictable and stochastic nature of Aliens aligns more with LLMs

how the eloquence of LLM outputs can deceive humans

the lack of explainability and transparency in both Alien and LLM behavior, and the opposite in knowledge graphs

the difficulty of dealing with baked-in biases in LLMs

the lack of repeatability in LLMs and the opposite in KGs

the current trend of architectures and practices like RAG that draw on the strengths of KGs and LLMs to get better results, just as the Alien and Predator media franchises combined forces

how over the past year or so investment in LLMs has overshadowed all other investments, just as Aliens are out to wipe out anything that's not an Alien

her approach to AI architectures that combine LLMs and knowledge graphs

how different kinds of people consume LLM output

how she helps enterprise decision makers choose whether to address a use case with a knowledge graph or an LLM

how taxonomists and ontologists can use LLMs in their work

the Alien Loves Predator UK Facebook group and Alien and Predator on a seesaw

Alien and Predator cosplay actors on a seesaw

Fran's bio

Fran started her career as a writer and editor of dictionaries and thesauruses in the UK, and, as technology evolved, she specialised in information architecture, search systems, and digital archives, and more recently, the use of semantics in knowledge graphs and LLM applications. Having worked on reference publications including the Collins English Dictionary, and as Taxonomy Manager for the BBC Archive, she now lives in Montreal, Canada, and is the Senior Taxonomist for Expedia Group. She was Taxonomy Bootcamp London’s Taxonomy Practitioner of the Year 2023.

Connect with Fran online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/VWwDBIws6G8

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 15. When two impressive domains converge, amazing things can happen. When the Alien and Predator science fiction franchises joined forces, both enjoyed new commercial success. Similarly, in the AI world right now, Fran Alexander sees knowledge graphs and large language models combining forces to create retrieval augmented generation and similar architectures that work together to create systems more useful and valuable than the sum of their individual capabilities.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 15 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show, Fran Alexander. Fran is an independent taxonomist and ontologist based in Montreal. And so, welcome Fran. Tell the folks a little bit more about what you're up to these days.

Fran:

Hi Larry. Well, it's nice to talk to you again. I really enjoyed talking to you on the previous podcast that we did a little while ago. And that one was kind of a bit of a general introduction to taxonomies, ontologies, thesauruses, knowledge modeling and semantics. But this time, I thought we could talk about knowledge graphs and LLMs. They're a big hot topic and I did a presentation earlier on in the year for Taxonomy Boot Camp London, Bite-sized Taxonomy Boot Camp London. That was a lot of fun and has been really popular. A lot of people have been asking me about it. I've revisited it a couple of times and that's LLMs versus knowledge graphs, Alien versus Predator.

Larry:

Okay. So why Alien versus Predator?

Andreas Blumauer

Every enterprise nowadays is awash in data, content, and knowledge, the understanding of which is all too often available only in employees' heads.

Forward-thinking businesses are moving to knowledge graphs to capture that tacit knowledge so that they can better understand and use it.

Andreas Blumauer shows how those graphs work best when they're accompanied by a domain knowledge model, creating a "semantic layer" that provides a vivid map of your business knowledge.

We talked about:

the recent merger of his company, Semantic Web Company, with Ontotext to form a new company, Graphwise, which aims to help enterprises build their semantic layer

the 20-year-old origin story behind his definition and description of a semantic layer

the emerging trend he sees of data, content, and knowledge people coming together, often around explorations of language and content structure

how getting domain knowledge out of people's heads and into multimodal AI architectures can streamline business research

how a semantic layer provides a map of your business knowledge

the importance of a domain knowledge model in a semantic layer architecture

the inadequacy of "desktop data integration," the practice of calling colleagues, consulting varied sources, and otherwise searching through ad hoc enterprise knowledge sources, and the stress it can cause

how a domain knowledge model can connect and reveal knowledge across an organization

the surprisingly small footprint of the domain knowledge model in an enterprise knowledge graph, typically just 1% or so of the semantic layer

how LLMs can help in the discovery of data and the construction of knowledge graphs

the two main elements of the semantic layer: domain knowledge models (taxonomies, ontologies, etc.) and an automatically generated enterprise knowledge graph

the different perceptions of the value of the semantic layer across data and content professionals, and how the arrival of gen AI has resulted in them talking together more to each other

how the semantic layer can facilitate the alignment of vocabularies and the understanding of data across business divisions

the benefits of a hybrid centralized-decentralized/global-local "glocalization" semantic layer strategy

Andreas' bio

Andreas Blumauer is SVP Growth at Graphwise, and CEO and founder of the Semantic Web Company (SWC), provider and developer of the PoolParty Semantic Platform and leading solution provider in the field of semantic AI and RAG. For more than 20 years, he has worked with more than 200 organizations worldwide to deliver AI and semantic search solutions, knowledge platforms, content hubs and related data modeling and integration services. Most recently, Andreas has been involved in the development of AI-powered ESG solutions for companies and investors.

A globally recognized thought leader and author in the field of semantic AI and graph technologies, Andreas has helped define and implement knowledge and AI strategies for various industries and domains. He is the author of "The Knowledge Graph Cookbook: Recipes that Work", a practical guide to building and deploying knowledge graphs in organizations. He is passionate about enabling clients to harness the power of semantic AI and graph technologies to achieve their business goals and make a social contribution.

Since 2022, Andreas has focused primarily on developing AI-based solutions that help organizations implement ESG and sustainable systems.

Connect with Andreas online

LinkedIn

Graphwise

Resources mentioned in this episode

Knowledge Graphs, LLMs and Semantic AI LinkedIn group

From Data to Trust: Leveraging Knowledge Graphs for Enterprise AI Solutions webinar

Enterprise architecture model that includes a semantic layer

Video

Here’s the video version of our conversation:

https://youtu.be/dEQh6m6zoDE

Podcast intro transcript

Jessica Talisman

Jessica Talisman is a seasoned information architect with decades of experience across a variety of domains.

She's done a lot of education and outreach around her semantic and and information architecture practices. One of the most important lessons she's learned is the crucial role of standards like the W3C SKOS model to bring structure and semantics to information and knowledge systems.

Since there are never enough information architects in any organization, she supports the democratization of IA practices, but she's also quick to highlight the unique skills that you can only get with deep study.

We talked about:

her work as a senior information architect at Adobe and previously in GLAM (galleries, libraries, art, and museums) and other domains

how her work in GLAM showed her the importance of the concept of lineage and attribution and benefits of the FRBR (Functional Requirements for Bibliographic Records) framework

how standards and rules bring discipline and structure to information and data ecosystems

how capturing knowledge via the SKOS standard can provide on its own the structure, semantics, and disambiguation your data needs, as well as set you up for future successes

the importance of focusing on semantic fundamentals and how the ensuing understanding if your data assets can improve activities like a graph RAG implementation

the importance of collaborating and sharing ideas across domains

democratization, evangelism, and other kinds of information architecture outreach

the "Golden Spike" railroad metaphor she uses to illustrate cross-functional collaboration challenges

how linked data can help span organizational silos and align stakeholders on language and terminology

the importance of understanding your unique organizational fingerprint

how applying the library science concept of "scholarly communications" can move organizations forward and promote innovation

Jessica's bio

Jessica Talisman is a Senior Information Architect at Adobe. She has been building information systems to support human and machine information retrieval for more than 25 years. Jessica has worked in a variety of domains such as e-commerce, government, AdTech, EdTech and GLAM. Jessica holds a Masters in Library and Information Science with a concentration in Informatics. She lives in Santa Cruz, California with her partner Dave, and two dogs.

Connect with Jessica online

LinkedIn - Jessica is working on a new book about information architecture and is looking for anecdotes and other input. If you're an IA practitioner with good stories to share, she'd love to connect.

Video

Here’s the video version of our conversation:

https://youtu.be/1tlrZTJ52Vs

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 12. Anyone who has tried to discern how people in a domain talk about the concepts in it, and then try to align stakeholders in an organization around those concepts and the words that describes them, and then share that information with computers so that you can scale the impact of your work, knows that you need a good system to manage your taxonomies and other terminology. Jessica Talisman argues that the W3C SKOS standard is your best friend in such endeavors.

Interview transcript

Larry:

Okay. Hi everyone. Welcome to episode number 12 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Jessica Talisman. Jessica's currently a senior information architect at Adobe, but she is extremely experienced in information architecture and knowledge graph stuff, so welcome Jessica, tell the folks a little bit more about what you're up to these days.

Jessica:

Thanks, Larry. So I'm currently, as Larry said, a senior information architect at Adobe, and before this, I was information architect over at Amazon. I've worked in many different domain spaces,