In today’s digital landscape, marketers face the ongoing challenge of creating consistent, high-value content that meets consumers’ constantly evolving needs. The rise of AI in search has heightened concerns about the accuracy and trustworthiness of content, with instances of AI-generated content being misinterpreted.
As a result, users and search engines are increasingly focused on accessing high-quality, reliable information. This shift has led many organizations to revise their content strategies to maintain accuracy, relevance, and trust in this evolving environment.
To develop a successful content strategy in this new search experience, marketers must address several critical questions on an ongoing basis:
- How can you maintain an up-to-date content inventory?
- What content gaps exist, and where are opportunities for new high-quality content to be added?
- Which existing content pieces require improvement or disambiguation?
- How has your content impacted your website’s performance as search evolves?
While your website content is a rich data source, it’s often unstructured. This makes it difficult to analyze and answer these questions at scale. Many marketers manually review and revise content to inform their strategy, which is time-consuming and inefficient.
So what if you could automatically structure your content to make high-level analysis fast and easy? Good news, a content knowledge graph can be leveraged to do precisely that. This approach is particularly valuable for large websites or organizations managing multiple sites, where understanding the full scope of covered topics can be challenging.
This article will explore how leveraging your content knowledge graph can support and enhance your content strategy.
By harnessing the power of your content knowledge graph, you can make well-informed decisions that drive your content strategy forward in today’s competitive digital landscape.
Understanding Content Knowledge Graphs
At Schema App, we define a content knowledge graph as a graph that represents entities (things), their attributes, and the relationships between them on a publicly-facing website.
Like a general knowledge graph, it uses a standardized vocabulary or ontology (such as Schema.org) to create a structured, reusable data layer. This structure enables machines to discover new insights through inferencing, helping to explore and understand the connections between various entities in your content.
At Schema App, we build content knowledge graphs by mapping the content on your website to specific types and properties in the Schema.org vocabulary. This results in a precise and organized framework that accurately reflects your content’s meaning and relationships.
While Schema.org provides an excellent foundation for knowledge graph creation, its available types and properties can be limiting. That’s why Schema App created the Omni Linked Entity Recognition (Omni LER) feature, which automatically identifies entities in your content that have been described in external authoritative databases such as Wikipedia, Wikidata, and Google’s Knowledge Graph. This process is known as entity linking, and it offers two significant benefits:
- Improved SEO: Embedding these entities in the Schema Markup on your pages helps to disambiguate them, which enhances search engine optimization for queries related to those entities.
- Content Inventory: Identified entities also function as an inventory of what’s discussed in your content, offering valuable insights for content strategy planning.
By leveraging both the Schema.org vocabulary and Omni LER, the content knowledge graph provided by Schema App gives you a comprehensive understanding of your content architecture. This enables you to make data-driven decisions to optimize your content strategy for search.
Content Knowledge Graph Use Cases
Now that you’ve been introduced to content knowledge graphs, let’s explore some practical applications and effective ways to leverage this powerful resource to enhance your content strategy.
Improve Content Inventory Organization
When you develop a content knowledge graph with Schema App, you can implement a multi-dimensional categorization method for your content.
Schema App’s Highlighter builds your content knowledge graph by consistently tagging and classifying your website content at scale. This is particularly beneficial for organizations with large websites, a wide variety of assets, and different content stakeholders.
Your content knowledge graph establishes meaningful connections between different content pieces based on entities, types, and properties – not just keywords. For example, a blog post would likely show up in your content knowledge graph as an instance of a BlogPosting with properties like author, datePublished, and dateModified. If Omni LER is also used, additional metadata about the identified entities mentioned within the article body will be added. This enables you to do more detailed content analysis, which we will cover later in the article.
Content Coverage and Gap Identification
By constructing your content knowledge graph with both the Schema.org vocabulary and Omni LER, you can query all of your content with greater precision. Schema.org provides detailed types and properties, and Omni LER adds unique entities for varied levels of granularity in your data layer. When combined, you can leverage your content knowledge graph to help you determine what new content to add or which existing content to improve to better meet your audience’s needs.
This holistic view of the Schema.org types and entities covered by your website allows you to:
- Identify areas of content saturation
- Discover underrepresented topics
- Align your content with current business goals and market trends
Use Case 1: Aligning Content With Business Goals
For instance, one of Schema App’s customers aimed to be recognized for their product’s ease of use. To align their content with this business goal, we employed the following strategy:
1. First, we identified Schema.org types and properties that indicate user support and ease of use. These included:
These content types can represent ease of use by making processes feel manageable and easy to follow, empowering users to self-serve, or simplifying site navigation.
2. We then queried their content knowledge graph to pinpoint where these types and properties already exist on their site and where existing content could be further enhanced to align content with their goals.
3. Finally, we identified opportunities to add net new content in alignment with their goal. For instance, we recommended creating more HowTo content with clear steps and accompanying images and/or videos to support ease of use.
Through this process, our customer identified content gaps that, when addressed, aligned better with their business goals and enhanced the quality of their site’s content.
Just like how content knowledge graphs can identify gaps in your content, they can also reveal how much of your existing content overlaps with desired entities. This information is crucial for ensuring that your content strategy covers all necessary areas and effectively addresses your audience’s interests.
Use Case 2: Assessing and Revising Content Coverage
Consider another example from one of our healthcare customers:
Our customer did entity linking using our OmniLER feature. The feature automatically identified known entities in their content, which revealed an unexpected insight: they had numerous blog posts mentioning COVID-19, a topic they no longer wished to emphasize in their content strategy.
Armed with this information, the customer was able to:
- Quickly identify all content pieces mentioning COVID-19
- Assess the relevance and necessity of each mention
- Selectively remove or update content to align with their updated business goals
This targeted approach allowed the customer to refine their content strategy without needing a time-consuming manual review of their entire content inventory.
Disambiguating Entities to Ensure Brand Name Consistency
Your content knowledge graph can also ensure the disambiguation of your entities and brand voice consistency across your website. This capability is particularly valuable when dealing with ambiguous terms or acronyms that could lead to misinterpretation or unintended associations.
For instance, imagine a scenario where our brand, Schema App, faces a challenge when its name is shortened to just “Schema” in some content. The word “Schema” can refer to various concepts on the web, from psychology to structured data. If machines unintentionally link this shortened form to unrelated information, it could confuse and potentially damage our brand image.
To resolve this issue, we would leverage our content knowledge graph to:
- Locate all instances where our brand name is inconsistently represented
- Implement a standardized approach to always use our full brand name, “Schema App”
- Disambiguate our brand using Schema Markup and entity linking. This ensures our brand is accurately identified and associated with the correct definition in external authoritative knowledge bases
- Ensure that our brand is consistently and correctly represented across all content
This scenario illustrates how a content knowledge graph enables organizations like ours to:
- Gain a holistic view of entity usage across our content
- Identify areas where content should be more explicit based on entity interpretation and their links to external knowledge bases
- Make informed decisions about content revisions to maintain brand integrity
- Ensure consistent brand voice and messaging across all content
By leveraging a content knowledge graph, we can proactively address potential ambiguities, maintain brand consistency, and enhance our content’s overall quality and clarity. This approach not only improves user experience but also protects our brand from unintended associations or misrepresentations, ultimately enhancing our performance in search.
Schema App Helps Develop Your Content Knowledge Graph
As explored throughout this article, a content knowledge graph is a powerful tool for optimizing your content strategy, improving SEO performance, and preparing your organization for the future of AI-driven search.
At Schema App, we implement semantic Schema Markup and automate the entity linking on your website to develop your organization’s content knowledge graph.
If you’re a current Schema App customer interested in leveraging your content knowledge graph, we encourage you to reach out to your Customer Success Manager. They can take you through what’s currently available so that you can leverage your content knowledge graph to support and enhance your content strategy.
If you’re new to Schema App and interested in harnessing the power of a content knowledge graph for your organization, now is the perfect time to get started. Our team is ready to help you navigate the complexities of semantic SEO and knowledge graph development, ensuring your content strategy is primed for success in today’s digital landscape.
Don’t let your content strategy fall behind in the era of semantic search and AI. Contact our team today to begin developing your content knowledge graph and optimizing your content strategy for search.
Develop a content knowledge graph for your organization today with Schema App!
Jasmine is the Product Manager at Schema App. Schema App is an end-to-end Schema Markup solution that helps enterprise SEO teams create, deploy and manage Schema Markup to stand out in search.