Teodora Petkova

Teodora Petkova is a scholar and content marketer whose PhD dissertation explored semantic technologies and dialogical theory and how they apply in the field of digital marketing communication.

She thoughtfully combines her rigorous academic thinking with pragmatic data- and knowledge-management practices in her content-marketing work.

She's currently building a knowledge graph with marketing content at Ontotext, the RDF database and data-management platform company.

We talked about:

her work building a knowledge graph of marketing content at Ontotext

her book "Being Dialogic" and the concept of dialogic communication

the importance of metadata in dialogic communication architectures

how creating a controlled vocabulary or an ontology can support a shared understanding of the concepts stakeholders and users work with

her overview of the semantic web

her current study with the 90-year-old marketing-education legend Philip Kotler

the connections she makes between corporate knowledge management and corporate marketing

the utility of enterprise-wide controlled vocabularies

how your content efforts can help you curate your knowledge graph

the urgent need to cultivate the social practices to help us generate and curate metadata

Teodora's bio

Teodora Petkova is a philologist fascinated by the metamorphoses of text on the Web and curious about the ways the Semantic Web unfolds. She holds a PhD. in Marketing Communication, an MS in Creative writing and a Bachelor of Science in Classics. Teodora is the author of the books The Brave New Text and Being Dialogic.

Following her genuine commitment to creating dialogic moments through semantic annotations, from 2022, Teodora is part of the Ontotext Knowledge Graph team. The Ontotext Knowledge Graph is where Teodora strives to harness the potential of the Semantic Web to foster dialogic marketing communications. Driven by the fascination with the ever-evolving nature of text on the Web, Teodora is also teaching web writing to students at the Content Strategy Masters program in FH Joanneum.

Connect with Teodora online

TeodoraPetkova.com

Teodora's newsletter

LinkedIn

Teodora's books

The Brave New Text

Being Dialogic

Resources mentioned in this interview

How to Do Things with Data, Klaus Bruhn Jensen

The Semantic Web, Tim Berners-Lee, James Hendler, and Ora Lassila

Marketing 4.0: Moving from Traditional to Digital, Philip Kotler

Video

Here’s the video version of our conversation:

https://www.youtube.com/watch?v=vF5xKzXxoWM

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 7. When you combine deep academic curiosity, a powerful concept like the semantic web, and an intellectually innovative approach to understanding how humans, computers, and corporate knowledge interact, you get Teodora Petkova's concept of "dialogic communication." Combine this powerful idea with a nuanced appreciation for modern marketing and carefully curated enterprise metadata and you can powerfully convey your organization's value to the world.

Interview transcript

Larry:

Hi everyone. Welcome to episode number seven of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show, Teodora Petkova. Teodora is just an ordinary content writer stumbled into the knowledge graph world. So welcome to the show, Teodora. Tell the folks a little bit more about what you are doing these days.

Teodora:

Hi, Larry. Thanks for having me. I'm doing what I have been dreaming about. I'm building a knowledge graph with Ontotext, out of marketing content, and I'm doing a lot of fights, a lot of things that I imagined and envisioned, but they need to be translated into specifications and requirements. So I now have a lot of time to write, do content writing per se; however, I write in the book of life.

Larry:

Yeah,

Dean Allemang

Dean Allemang literally wrote the book on the semantic web. "Semantic Web for the Working Ontologist" is now in its third edition.

In the book, Dean and his co-authors, James Hendler and Fabien Gandon, show how to apply web standards to build a meaningful web of global, connected knowledge.

More recently, Dean has conducted research with his colleagues at data.world that shows how using knowledge graphs can triple the accuracy of LLM-based question-answering systems.

We talked about:

his role as a principal solutions architect at data.world

the meaning of the "semantic web" and its intent of sharing meaning across the web

the long history of knowledge representation and how the connectedness of the semantic web adds to it

the crucial difference between documents about things and the strings that describe them

the contrast between the persistent nature of enterprise data and the ephemerality of the applications that use the data

the power of the simple structure of RDF, its mathematical affordances, and the ease of distribution it permits

the impact of newer AI tech on knowledge graph building and querying

the research that he and Juan Sequeda have conducted that shows how using knowledge graphs can triple the accuracy of LLM-based question-answering systems

his thoughts on the yet-to-be-resolved one-way or two-way ontology question

the crucial role of trust in AI and how replacing LLMs with knowledge graphs as the point of contact in AI systems could build more trust

Dean's bio

Dean Allemang has been active in the field of Artificial Intelligence (AI) since the 1980s. With a notable emphasis on Semantic Web, he is the author of the book "Semantic Web for the Working Ontologist." His passion for understanding and implementing knowledge graphs led to a significant publication about using LLMs to answer queries over structured data, which introduced a new benchmark for evaluation.

In his current role as a Principal Solutions Architect at data.world, he contributes extensively to the development of the AI Context Engine product, which is inspired by his recent research (with Juan Sequeda and Bryon Jacob), and underscores his commitment to practical application of theoretical principles.

For a span of about a decode, Dean operated as an independent consultant, utilizing knowledge graph solutions to address challenges in industries such as Media, Finance, and Life Sciences. This diverse experience has cultivated a broad perspective on applying AI and Semantic Web principles.

Influenced by Sir Tim Berners-Lee's concept of linked data and data sharing, Dean Allemang's work reflects a consistent focus on these principles. His contributions have advanced the field of AI and his current interest lies in how knowledge graphs can make generative AI more effective.

Connect with Dean online

LinkedIn

Medium

Resources mentioned in this interview

Semantic Web for the Working Ontologist

A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases, Juan Sequeda, Dean Allemang, Bryon Jacob

The Semantic Web, Tim Berners-Lee, James Hendler, and Ora Lassila

Video

Here’s the video version of our conversation:

https://youtu.be/29kmAc6tobU

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 6. Long before the introduction of the semantic web - the innovation that added meaning and metadata to documents on the web - AI pioneers like Dean Allemang had been thinking about how knowledge could be formalized to help people do their work. The web itself, along with the W3C standards that power its semantic capabilities, gave Dean and his peers the ability to scale and connect existing practices and technologies to build a more meaningful web.

Interview transcript

Larry:

Things. Hi everyone. Welcome to episode number six of the Knowledge Grap...

Alan Morrison

After 20-plus years of industry analysis, Alan Morrison has developed a keen sense for how knowledge graphs can help enterprises.

Even though he has focused on advanced tech and emerging IT practices and is deeply immersed and invested in current tech developments, much of his advice for enterprises looking to develop their data maturity involves pragmatic baby steps and basic mindset shifts.

We talked about:

his work in the consulting world and his organizing work around the knowledge graph community to improve awareness of the technology

the need to find "foxes instead of the hedgehogs" in enterprises when you're trying to promote adoption of new tech

the relationships between different AI tech, like LLMs and knowledge graphs, and the common connection they share: data

the importance of having mature data practices in any enterprise

how even simple metadata practices in common tools like spreadsheets can support better enterprise data practices

how sidestepping the formal org chart and forming guerrilla teams can advance data practice

the benefits of starting small in any knowledge graph project

how representing organization knowledge at a high level in a knowledge graph can help solve big enterprise problems

how a knowledge graph gives you a multidimensional Tinker Toys set to model and understand your org's data

the benefits of moving from tabular thinking to graph thinking

his frustration with the current framing of AI as being solely about machine learning

his observation that practices across any org - content, knowledge management, data management, business people - could benefit from long-standing standards and proven technologies (that might not be as sexy and topical as LLMs)

Alan's bio

Alan Morrison is a longtime analyst, writer, advisor and podcaster on advanced data technologies and emerging IT. For 20 years at PwC's R&D and innovation think tanks, Alan identified emerging technologies on the cusp of adoption, assessed their business impacts, and advised PwC's clients on innovation strategy.

Before PwC, he was a semiconductor industry market analyst and forecaster, a retail site location analyst, and a US Navy intelligence analyst, Russian linguist and aircrewman.

For the last five years, Alan has been a contributor on knowledge graph and related topics for Data Science Central. His writings over the years have covered dozens of different technologies.

Connect with Alan online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/TXOWWjM-DBc

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 5. You might think that the lofty perch of multiple decades in industry-analyst roles would inspire grand visions of tech transformation with leading-edge technology. Quite the opposite in the case Alan Morrison. He shows how enterprises can advance their data maturity by cultivating basic graph thinking in their organizations and by taking small, pragmatic steps like adopting established standards for interoperability or simply adding metadata to a spreadsheet.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number five of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show Alan Morrison. Currently, he's a contributor at Data Science Central, a well-known publication in the field. He's a freelancer and consultant around knowledge graphs and a lot of other areas as well. His background, he comes out of the consulting world. Most recently before his current role as a freelancer and consultant, he worked for many years at PriceWaterhouseCooper, the big consultancy as a senior research fellow. So welcome, Alan. Tell the folks a little bit more about what you're up to these days.

Alan:

Hey, Larry. Great to talk with you and folks should know that you and I have some history together as a part of the Data Worthy Collective,

Ellie Young

Ellie Young effortlessly connects the human and technical elements that go into ontologies and knowledge graph building.

Ellie came to the world of knowledge graphs with backgrounds in both literature and sustainability. "If the world wasn't on fire," she says, " I would probably be writing novels."

That sense of urgency drives her work at Common Action, a platform she is creating to address climate change and advance sustainability. She also applies her knowledge graph expertise in projects like HelioWeb at NASA, which connects scientists in the field of heliophysics.

We talked about:

her work at Common Action, a platform for climate and for sustainability that uses knowledge graph technology

her work at NASA to facilitate collaboration and expose knowledge across the domain of heliophysics (the study of the sun)

how personal knowledge graphs can connect individuals and collectives of people

how her background in design, art,literature, and the humanities manifests in her knowledge graph work

her desire to leverage metadata and capabilities like her language knowledge to facilitate topical discovery

the interplay between the efficiencies that AI tech like LLMs offer and the uniquely imaginative variations that human beings create

the importance of the practice of design in advancing the productive use of information

how user experience design connects to ontologies and back-end tech

how she applies, and imagines how others might apply, a literary mindset to ontology practice

how she applied ethnographic methods from anthropology to a paper she co-authored on the NASA HelioWeb ontology

her ongoing call for volunteers to help with her Common Action program, specifically a current need for creating a "phenomena ontology"

Ellie's bio

Ellie Young brings knowledge to communities to catalyze successful, local actions to address climate/sustainability problems.

She is the founder of Common Action, an innovation network facilitating climate and sustainability action through the development of community and knowledge graph technology.

Previously she served as Head of Community and Director of Conference Operations at The Knowledge Graph Conference.

Connect with Ellie online

LinkedIn

ellie at common-action dot org

Resources

The cultural-social nucleus of an open community: A multi-level community knowledge graph and NASA application, Applied Computing and Geosciences

Common Action vision

Video

Here’s the video version of our conversation:

https://youtu.be/CL9HWocoh7I

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 4. In the domains of ontology engineering and the semantic web, there are plenty of people with advanced technical skills. Practitioners with social-science skills, well-developed literary instincts, and a design mindset are harder to find. Ellie Young smoothly navigates the technical and linguistic worlds that intersect in knowledge graphs, applying her humanities mindset to projects that connect scientists at NASA and address climate change and sustainability.

Interview transcript

Larry:

Okay. Hi everyone. Welcome to episode number four of the Knowledge Graph Insights podcast. I am super delighted today to welcome to the show Ellie Young. Ellie is the founder of Common Action, a sustainability and climate change activist organization. She's also ... The way I first met her, she's the former head of community for the Knowledge Graph Conference, and one of the people who really ushered a lot of people into this community. So welcome Ellie. Tell the folks a little bit more about what you're up to these days.

Ellie:

Thanks, Larry, and thank you for inviting me to be a special guest with you on this very nice community visit. So yeah. Now I've gone from KGC. I think I left about three years ago to starting Common Action, which is not really an activist organization.

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.