Content Gap Analysis with AI-Powered Knowledge Graphs and LLMs

Posted Monday, April 28, 2025.

Content gap analysis can pinpoint areas lacking relevant material, which can improve rankings and audience engagement. InfraNodus can be used to identify those gaps based on the topical clusters of keyword co-occurrences in text. We can then use the built-in AI to bridge those gaps and generate new content ideas based on them.

This approach differs from the methods used in other tools, like Semrush or Ahrefs, which concentrate solely on competitive analysis — specifically, identifying the keywords your competitors are overlooking. The InfraNodus approach can also be used for competitive analysis but it offers additional AI-powered insights based on analyzing the market discourse, the competitors' websites, and search intent — and combining all these insights together to come up with a unique marketing strategy that addresses the fundamental blind spots that everyone else is missing.

Maximizing Informational Gain

The most typical content gap analysis template in InfraNodus is based on visualizing a knowledge graph of existing search results for a particular query. Network analysis is applied to the knowledge graph to generate the topical clusters: keywords that tend to occur together in the same context. We can then identify which topical clusters are not well connected and target those with our content strategy. For instance, in the example below: "keyword analysis" for "market insights".

We can use the built-in AI to generate new content ideas based on this gap. If we then publish the content that bridge this gap, Google will consider it to have a high degree of informational gain, so it will rank it higher.

Comparing Demand vs Supply

Another interesting approach is to compare the graph of the search results to the graph of the keywords related to the search query. InfraNodus has a "Difference" view which shows the keyword combinations that can be found in the search query graph but not in the search results graph. This visualization will help us see the keyword patterns that are used by our audience in search but for which there are not so many results available.

Additional filters can be applied to show the keyword combinations with a high number of search queries per month. Built-in AI can be used to generate content ideas and article outline for these keyword combinations.

Competitive Analysis

Content gap analysis can also be used for competitive analysis. The easiest way to do that is to analyze the content of the websites that rank at the top of the Google search results for your target query. Find the gaps in their content and then target those gaps (making sure to cover the main topical clusters as well) to provide the highest informational gain. Such content will rank higher on Google and will also be favored by LLM in their responses as it will offer the highest informational gain.

Try It Yourself

You can try content gap analysis with the InfraNodus templates for your own use cases: