A Conversation about Knowledge Graphs vs Structured Content for Enterprises Part 1
Jul 29, 2022
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Aaron Bradley, an expert in knowledge graphs and structured content implementation, discusses the differences between structured and unstructured content, the integration of knowledge graphs and structured data, and the value proposition of knowledge graphs in connecting and inferring knowledge from multiple sources.
Structured content breaks down information into individual components for machines to understand and connect the data.
Knowledge graphs enable the inference of new facts from existing ones and combine data from multiple sources to derive meaningful insights.
Deep dives
Overview of the Podcast Episode
In this podcast episode, Marcello interviews Aaron Bradley, a senior structured content architect at Teles. The conversation revolves around structured content and knowledge graphs for the enterprise. Aaron discusses his background starting as a technical services librarian and transitioning into web design, search engine optimization, and his deep involvement with schema.org. He explains that structured content breaks down information into individual and discrete components, allowing machines to understand and connect the data. The discussion then delves into the concept of knowledge graphs, which are machine-readable facts intelligently connected to provide meaningful insights. Aaron emphasizes the importance of connections and the ability to infer new knowledge from existing facts in a knowledge graph.
Defining Content and Structured Content
Aaron defines content as data materialized as information for human consumption. He highlights that the building block of all content is data, but content goes beyond raw data by providing it in a context that is comprehendible by humans. Structured content, on the other hand, is content that is broken down into discrete and named components with a defined structure and machine-readable semantics. Aaron explains that structured content allows for better organization, meaningful connections, and the ability to customize content for different individuals based on their preferences or marketing goals.
Introduction to Knowledge Graphs
The discussion then shifts to knowledge graphs, which are knowledge bases made up of machine-readable facts about things. Daniel clarifies that connections between things and the meaning of those connections are essential in knowledge graphs. By using relationships as first-class citizens, knowledge graphs enable the inference of new facts from existing ones. This allows for the intelligent connection of data from multiple sources. Aaron distinguishes knowledge graphs from relational databases and highlights the value of knowledge graphs in desiling data and combining different sources to derive meaningful insights.
Differentiating Knowledge Graphs and Ontologies
Aaron explains that having an ontology, which provides the common language and rules for organizing knowledge, is an essential part of a knowledge graph. However, an ontology alone is not sufficient to constitute a knowledge graph. While an ontology helps define the structure and meaning of the data, a knowledge graph requires both the organizing scaffolding (ontology) and the actual data (facts) to populate the graph. Structured content is not inherently a knowledge graph, but it can be effectively used within a knowledge graph environment due to its organization and semantic structure.
In this conversation with Aaron Bradley, you will learn what a knowledge graph is, how (and if) it is related to structured content and what enterprises should know before implementing either.
To watch the interview or for more information on the Discover Headless Course, please visit: https://www.headlesscreator.com/course/discover-headless-tech
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