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. Unlike other tools, InfraNodus visualizes the results as a knowledge graph, so you can quickly see the important patterns and use the graph to explore the keyword relations in more detail. You can also use the built-in AI to generate content ideas that bridges the gaps you discover.

This approach differs from the content gap analysis methods used in other tools, like Semrush or Ahrefs, which concentrate solely on competitor research: specifically, identifying the keywords where you could rank higher than your competitors. The InfraNodus approach can also be used for competitor analysis but it offers additional AI-powered insights based on analyzing the market discourse at large, the competitors' websites, and search intent. You can then combine all these insights 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 for the "seo tools" query we discover the gap between "keyword analysis" and "market insights":

This means that there's a gap between these two topical clusters. If we create the content that bridges this gap and also mentions the other topics discovered (website optimization, Google search), then Google will consider this content to have a high degree of informational gain and therefore rank our content higher than that of competitors.

We can use the built-in AI to generate new content ideas based on this gap. The underlying knowledge graph structure will be used to make sure that the outline of the article touches upon all the topics identified to make it contextually relevant while also focused on bridging the specific gap we discovered.

What do People Search For But Don't Find?

Another interesting approach is to compare the graph of the search results (SERP or informational supply) to the graph of the keywords related to the search query (search intent or informational demand). 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.

For instance, in the example for "seo tools", we can see that while people search for "keyword generator", this is not the keyword combination that comes up on the first pages of Google search results. If we create content related to "seo tools" that also targets "keyword generator", we'll signal to Google that we are covering an underserved topic and that will push our content higher.

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.

Competitor Research

Content gap analysis can also be used for competitor research. 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. For instance, if we search for "content gap analysis", we will find the usual top websites: Backlinko, Semrush, Ahrefs, SearchEngineLand, and Ubersuggest. We can then visualize the content of these web pages and find the gaps in their content. For instance, there's a gap between "traffic evaluation" (checking how well the domain is ranking) and "data visualization" (visualizing the results):

This indicates that all those pages are missing out on connecting the topic of data visualization to keyword research and traffic evaluation. We can use the built-in AI to generate content that targets this gap (making sure to cover the main topical clusters as well). 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.

Improving Ahrefs and Semrush Content Gap Analysis

InfraNodus graph can be used to visualize keyword gap analysis results for Ahrefs and Semrush and improve the quality of the insights you're getting. For instance, you can export the CSV table with Ahref content gap analysis results and then visualize it in InfraNodus to find patterns of keywords that your competitors rank for. In the example below, we use "content gap analysis" as the search query, perform content gap analysis in AHrefs to get a list of the keywords the top 5 websites rank for. Then we remove the actual keywords from the graph to see the other top keyword combinations that competitors rank for:

We can see, that "Semrush keyword gap tool" and "Ahrefs content gap analysis" analysis are bringing them additional traffic. Therefore, we could target those keyword combinations to improve our positioning for the "content gap analysis" query.

Try It Yourself

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