Welcome to the exciting world of knowledge graphs, structured content and large language models. This isn’t an average SEO topic; it’s about understanding your website’s ecosystem and how it interacts with Google’s algorithms. Let’s dive into the future of content creation and digital marketing, while keeping this subject matter accessible!
But first, there are a couple of things you should know.
Understanding Knowledge Graphs and Structured Content
Knowledge graphs are your information treasure map, linking related concepts that make sense to humans and machines. They’re the backbone of how search engines like Google categorize the web’s vast information. But more than that, they’re pivotal in developing artificial intelligence, specifically in natural language processing.
Structured content, on the flip side, is your knowledge graph’s building blocks. It’s about categorizing your content so that machines can easily comprehend it. This includes any content from technical documentation to marketing content. The key is to structure your content to align with your knowledge graph, creating an easy-to-navigate flow of information for your audience and search engines.
How Knowledge Graphs and Structured Content Impact AI
AI models, especially those dealing with language, struggle with understanding context and meaning. Our latest research on generative AI shows that 72% of people trust the output coming from generative AI. But knowledge graphs and structured content can provide a clear, structured information framework, helping these AI models make sense of the world. If you’re in SEO or content marketing, understanding these concepts can give you a significant edge. By creating more relevant, engaging content that resonates with your audience, you may achieve higher rankings in search engine results.
- Knowledge graphs and structured content play a crucial role in generative AI and LLMs.
- Ontologies help in building knowledge graphs and understanding the relationships between different topics.
- Understanding the structure of a website’s content is crucial for SEO.
- The use of LLMs and generative AI can greatly improve workflows and increase efficiency.
- The combination of neural net-based technologies and knowledge representation models such as ontologies can lead to new insights and applications.
The future of content strategy lies in the integration of knowledge graphs, structured content, and generative AI.
Going forward, understanding and utilizing structured content, knowledge graphs, and generative artificial intelligence (AI) will be key to creating effective content strategies. The discussion emphasizes that these technologies and methodologies are shaping the future of content generation and SEO.
Larry explains, “I’ve been trying to tell them, like in my world, I run mostly in the product content world, out in with the content designers at big enterprises and stuff. In that world, there’s a real clear distinction between research and design and content design, content practices of various kinds, and and engineering.”
Generative AI, especially Large Language Models (LLMs), can be used to predict and create content, improving efficiency and capacity.
This discussion highlights the increasing use of generative AI and LLMs in predicting and creating content. These technologies are seen as a way to increase efficiency and capacity in content production and SEO.
Larry notes, “It’s really interesting to me is that you know one of the the things that generative AI and you know chat bots and LLM’s have shown to be really good at is like helping folks write code.” He also mentions that the combination of generative AI and knowledge graphs could provide “genuine insights that a human being might have come up with but that’ll be accelerated too.”
Jeff also shares his insights, saying, “What are you going to do with that becomes your dream… take a taxonomy and get that into the system.”
Understanding content structures, knowledge graphs, and how they influence generative AI is essential for future content marketers and SEOs.
The discussion emphasizes the importance of understanding content structures and knowledge graphs, and how they’re influencing the development and application of generative AI. As content marketing and SEO evolve, these will become fundamental knowledge areas.
Larry stresses, “I think figuring out, and I don’t know this is, I go back and forth on this as a content person. Like it’s really my job to ascribe the purpose and intent, you know the content that I’m doing and to do my best to match it up with channel needs.”
Jeff concurs, saying, “How can unstructured data becomes structured data? How can someone, whose job is to build these things, get ahead of the game there? And that’s what I’m thinking about all the time, is how can I generate taxonomies automatically, right? How can I take a collection of prompts and automatically build knowledge graph representation?”
Here are some interesting quotes pulled from the conversation between Jeff and Larry.
- “Knowledge graphs and structured content play a crucial role in generative AI and LLMs.”
- “Ontologies help in building knowledge graphs and understanding the relationships between different topics.”
- “Understanding the structure of a website’s content is crucial for SEO.”
- “The use of LLMs and generative AI can greatly improve workflows and increase efficiency.”
- “The combination of neural net-based technologies and knowledge representation models such as ontologies can lead to new insights and applications.”
Larry Swanson is an independent content architect and digital strategist based in The Netherlands. He hosts the Content Strategy Insights podcast, co-organizes the Decoupled Days conference, co-chairs the content track at the Knowledge Graph Conference, and regularly convenes panels at conferences like Utterly Content. Previously, he has helped organize community events like the Seattle Content Strategy meetup, World Information Architecture Day, and WordCamp Seattle.
What you should do now
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