Emeka Okoye

Semantic technologies permit powerful connections across a variety of linked data resources across the web. Until recently, developers had to learn the RDF language to discover and use these resources.

Leveraging the new Model Context Protocol (MCP) and LLM-powered natural-language interfaces, Emeka Okoye has created the RDF Explorer, an MCP service that lets any developer surf the semantic web without having to learn its specialized language.

We talked about:

his long history in knowledge engineering and AI agents

his deep involvement in the business and technology communities in Nigeria, including founding the country's first internet startup

how he was building knowledge graphs before Google coined the term

an overview of MCP, the Model Context Protocol, and its benefits

the RDF Explorer MCP server he has developed

how the MCP protocol and helps ease some of the challenges that semantic web developers have traditionally faced

the capabilities of his RDF Explorer:

facilitating communication between AI applications, language models, and RDF data

enabling graph exploration and graph data analysis via SPARQL queries

browsing, accessing, and evaluating linked-open-data RDF resources

the origins of RDF Explorer in his attempt to improve ontology engineering tooling

his objections to "vibe ontology" creation

the ability of RDF Explorer to let non-RDF developers users access knowledge graph data

how accessing knowledge graph data addresses the problem of the static nature of the data in language models

the natural connections he sees between neural network AI and symbolic AI like knowledge graphs, and the tech tribalism he sees in the broader AI world that prevents others from seeing them

how the ability of LLMs to predict likely language isn't true intelligence or actual knowledge

some of the lessons he learned by building the RDF Explorer, e.g., how the MCP protocol removes a lot of the complexity in building hybrid AI solutions

how MCP helps him validate the ontologies he creates

Emeka's bio

Emeka is a Knowledge Engineer, Semantic Architect, and Generative AI Engineer who leverages his over two decades of expertise in ontology and knowledge engineering and software development to architect, develop, and deploy innovative, data-centric AI products and intelligent cognitive systems to enable organizations in their Digital Transformation journey to enhance their data infrastructure, harness their data assets for high-level cognitive tasks and decision-making processes, and drive innovation and efficiency enroute to achieving their organizational goals.

Emeka’s experience has embraced a breadth of technologies his primary focus being solution design, engineering and product development while working with a cross section of professionals across various cultures in Africa and Europe in solving problems at a complex level. Emeka can understand and explain technologies from deep diving under the hood to the value proposition level.

Connect with Emeka online

LinkedIn

Making Knowledge Graphs Accessible: My Journey with MCP and RDF Explorer

RDF Explorer (GitHub)

Video

Here’s the video version of our conversation:

https://youtu.be/GK4cqtgYRfA

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 36. The widespread adoption of semantic technologies has created a variety of linked data resources on the web. Until recently, you had to learn semantic tools to access that data. The arrival of LLMs, with their conversational interfaces and ability to translate natural language into knowledge graph queries, combined with the new Model Context Protocol, has empowered semantic web experts like Emeka Okoye to build tools that let any developer surf the semantic web.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 36 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show my good friend, Emeka Okoye. Emeka is a really interesting ontology practitioner and knowledge engineer, and he's operating now at the intersection of knowledge engineering and generative AI, which I think is a really interesting intersection and that's what we're going to talk about today. So welcome, Emeka. Tell the folks a little bit more about what you're up to these days.

Emeka:

Oh, well, thank you bringing me to this awesome podcast. I'm proud to be here. I have been involved in knowledge engineering or more like AI. We need to understand that knowledge engineering is important for AI because it creates the knowledge layer. So that's where we have knowledge graphs. There's been a lot of tribalism in AI, the neural nets on one side and the symbolic AI on the other side. So I am in for the convergence. I've always believed in the convergence.

Emeka:

Funny enough, I've been teaching and mentoring young ones on both sides of the divide since 2016 in the Nigerian data science space. So no surprises that generative AI boomed, and I needed to find reasons to see how we can integrate both sides, because that's what AI is all about, the best of both worlds, best of neural nets, and then best of symbolic AI. That's the future. I mean, there's no doubt about it. So that foundation, I needed to be there and that's why I've been working on both sides. So from knowledge graphs to AI agents.

Larry:

That's so funny, we didn't talk about this before I hit record, but right before we started this interview, I posted a thing to LinkedIn about exactly that. It was specifically about the need for executive education around hybrid AI architectures 'cause all they have is Silicon Valley hype. That's all the information they have. But more to the point, you're a hybrid practice. Well, first of all, I've known you for years now, and it just occurred to me, I don't really know your academic background, but it sounds like you're equally grounded in machine learning and knowledge representation stuff. Have you always pursued both?

Emeka:

I'm a geologist. That's the only qualification I do have. Immediately I found love with personal computers. So once the PC era boomed, I just went in programming. Nigeria was once one of the biggest software countries in the world at a point in time. Our software houses were building financial and banking systems the whole of North America were using, and some part of Europe. So we are that big. So when the internet came, we embraced it that early. I was already building internet protocols using Visual Basic, and not long after I co-founded the first startup in Nigeria. And then after that I worked with probably one of the earliest Semantic Web brands in the world, which is OpenLink Software. I became the Chief Technical Officer in the whole of Africa.

Emeka:

So I was with OpenLink Software when Tim Berners-Lee came up with the Semantic Web thing and Ora and co coming up with agents. So I started early on, thanks to my mentor, my boss then, Kingsley Idehen, who mentored me throughout and made me understand that the future was Semantic Web. So I dove right into it. And can you believe this, we were already creating knowledge graph before Google called it knowledge graph. I had created one for a client, which is Music In Africa by 2011, 2012.

Larry:

That's right before they introduced the term knowledge graph with their... That's so interesting because... And the RDF and the OWL and all the Semantic Web tech goes back 10 years before that. So that gap between the dawn of the Semantic Web and the coining of the term knowledge graph, you were just in there doing it.

Emeka:

Yes. Yeah, we were already doing it. And remember I came from a company that is on top of this technology. You who Kingsley Idehen is. He's my former boss, and mentor today, even after. I left OpenLink Software, he was there to guide me in. So most of what I know in semantic technology comes from Kingsley. So we were already doing this. So my understanding of the technology is very sound. Academia-wise, I didn't do anything much in that regards on the technology, but I'm hoping I'll do research in the future, because as I'm trying to come into Europe, I noticed that there are a lot of research-based jobs and AI is something I would love to devote research time.

Larry:

Yeah, and I know a lot of those people, and there's not a specific track yet around the hybrid AI stuff. I hope you get a chance to do that. But hey, that's what I want to really focus on today. So your background, your RDF Explorer project makes even more sense to me now. I just want to say real quickly about that. Emeka and I meet once or twice a week, and our Dataworthy Collective, which we co-organize with some other folks, and I was just embarrassed that I had totally missed this awesome piece you wrote for LinkedIn about RDF Explorer, and then you just happened to mention it in one of our meetings, and I went and read it and I was like, "Whoa, that's amazing. We got to talk about this."

Larry:

So here we are. Finally, I get to share the RDF Explorer with folks. So tell me, I think one thing I've been a little bit surprised by is that not everybody in the knowledge graph and semantic tech space is familiar with MCP. They maybe know the acronym and what it stands for, but can you talk just a little bit about the Model Context Protocol?

Emeka:

All right, so the Model Context Protocol, which was created by Anthropic sometimes in November 2024, is a standardized protocol which allows AI agents to connect and interact with external tools and different data sources in a simplified manner. It's that simplicity that is the attraction. So it removes a lot of stress that comes to connecting different data source to it. Now, just to give you an idea what we are talking about. Before MCP, we had all these agentic RAG solutions.