Semantic Maps

December 21, 2023
Sébastien MOUGEL
Sébastien MOUGEL (CEO Beyond1)

AI and Software Engineer : LinkedIn

Photo by Sébastien MOUGEL - SEOCopilot

Content semantic maps are a new way of visually exploring the content of a website. They enable you to visualize at a glance pages with similar content. Pages with a similar theme will be brought closer together on the map. Conversely, unrelated pages will be far apart. As with Google Maps, layers can be added to analyze an additional content-related component (conversion, click-through rate, position, etc.).

Content semantic maps (December 2023) : and (electric car manufacturers)
Each dot represents a page of content
These maps are interactive: by hovering over each point, you can visualize: a title, a url, etc.
Small groups of content (semantic clusters) can be observed The themes covered by Polestar are much more numerous.

What are the use cases?

Identify pages with similar themes

Visualize page clusters (semantic cocoon).

If you're running a website with user-generated content or a product catalog, semantic maps allow you to check content category by content category whether your ranking is correct. Example: An e-commerce website can add a product visual to each content page and decide to display the semantic map for a product category. If you see a drill in the swimming pool category, you can easily identify the source of the error. In the case of an online blog post, categorical or tag-based rankings can sometimes contain errors that are difficult to detect, and semantic maps are a way of dealing with them. Using the notion of proximity between each content item, you can decide whether you want to treat a theme more broadly (by broadening the semantic field: by expanding content on related subjects or derived keywords).

Detect whether a content theme is over- or under-represented

For an SEO consultant: you're in charge of carrying out a content audit of a website. Unfortunately, your time resources are limited, and you usually carry out analyses by content sample. In this case, the semantic map saves you time by visualizing the points of attention that need to be brought to your clients' attention: underdeveloped themes, irrelevant content or content that needs to be repositioned. You can also detect pages that you can link together via internal net linking.

Content semantic map (December 2023): and (Connected hardware: watches, scales)
The semantic field covered by fitbit is very broad. Nevertheless, the area of blue dots not covered could be an opportunity to be seized?

Simplify community moderation

For a community manager: You manage a community that produces content on your website through forums, comments or participative content. Semantic maps enable you to identify the general content of published content. By coupling this mapping with information on whether or not content has been reviewed by a moderator, you can decide which content areas to audit.

For the manager of a web site with user-generated or externally-sourced content: In the case of User Generated Content (comments, forums, participative content: wikipedia etc.) or content integrated from an external source (product catalog integration, spare parts, business data), it can be difficult to apprehend the volumes of content generated: problems of category classification etc. Here, semantic analysis enables you to better analyze product content and no longer depend on a manual classification system.

Identify the type of content that converts / generates sales

By combining semantic analysis and conversion data from your pages (sales, quote requests, application downloads) you can visualize the pages that generate the most conversions. You can also combine them with a heat map to analyze the content areas that are achieving your objectives. This data becomes actionable, as you can decide whether you want to increase your content production on a given theme or, conversely, halt your investments.

View conversion rate by page
View sales volume per page

Prerequisite: Be able to track your conversions page by page. (SEOCopilot allows you to easily integrate a conversion measurement tool).

Analyze competitor strategy (content and product positioning)

Visualization of the content of 3 competing sites.
There seems to be more similar content between the blue and red sites.
The competitor in green is relatively distant from the other 2 in terms of content.

By superimposing a conversion layer, you get some very interesting article ideas.

Another strategic aspect of marketing: monitoring competitors' content. On a single map, you can color-code the content produced by your competitors. This makes a considerable contribution, as you can observe the competitive positioning in terms of content (Does your client stand out with a premium positioning of its offer? Does it target a more specific user problem?) Can you surpass this competitor through an intensive content production effort for a given theme? Do you see any gaps that you can fill?

Off-topic content detection

Another benefit of semantic analysis is the detection of off-topic content. In a scenario where you don't have complete control over the production of your content (User Generated Content), or where you're managing the production of a large quantity of content, you can use semantic maps to identify an outlier and the page concerned. In addition to semantic maps, it is possible to automate this type of processing, with considerable cost savings.

Find content ideas through search query / content alignment

We can also visualize what we call the "search space". A search space corresponds to the visualization of the semantic space corresponding to queries formulated by Internet users. You can obtain these queries by extracting your data from Google Search Console or other search engines. By superimposing this search space with the semantic space of your content, you should in theory obtain an ideal superposition of the two spaces. This will not be the case, however, as the content space is smaller than the search space. By observing these semantic distances, you can identify new keyword opportunities on which you can position yourself. By combining factors such as generic queries and long tail, you can quickly assess how easy it is to position yourself on a keyword.

How to create semantic maps?

Semantic maps are generated from the content of a website. It is therefore necessary to be able to access the website content in order to extract all relevant information from the site in question. Content is sometimes located behind content firewalls. Please keep an eye on your crawl speeds, otherwise your IP address may be blocked.

To produce this type of map, your technical team needs to be able to use LLM technology.This technology enables content to be represented in vectorized form.Based on these vectors, you can use traditional machine learning techniques to perform additional processing (clustering, semantic search, etc.). Performing this type of processing at scale and on a large volume of data may require special technical infrastructures: GPU-equipped servers, large amounts of RAM, specific drivers.What's more, each LLM has its own specificities: does it work in a multi-language environment? Is vector representation appropriate in this semantic universe? The profiles required for this type of development are data scientists and AI engineers with knowledge of NLP (Natural Language Processing).

To add layers to these semantic maps, you need to attach additional data to each content unit, such as conversion data. With conversion data, for example, you can associate each point with a color or size representing, for example, a sales volume, a turnover or a conversion rate.

These maps can be displayed on an interactive web page, allowing intuitive navigation through the data. For large volumes of data, it will be necessary to implement filtering criteria to simplify visualization.

Disadvantages and limitations

Semantic maps are an intuitive new way of navigating through a mass of content. However, navigating through large volumes of data can be tricky. There are a number of ways of overcoming this, including filtering criteria and sampling. Clustering techniques can enable you to visualize the "big" masses of content first, and then switch to a more detailed display.

The choice of the model is also an important consideration. It must be adapted to the content you wish to map. Some LLMs may be unsuitable for specific domains: healthcare, legal, financial (just as GPT chat may have shortcomings in specific domains). LLMs specifically adapted to your language or re-trained in your field of application can greatly improve results.

The technology used to represent the semantic map is of critical importance. The visualization of these vectors requires sometimes complex parameter settings that require a mathematical understanding of the underlying parameters. Calling on the services of an expert can make all the difference.


Semantic content maps are a new tool for website content management teams, marketing teams and community managers. They enable new uses (identifying content categorization anomalies, identifying content writing opportunities, competitive and strategic intelligence). Like Google Maps, they can be enhanced with additional layers to combine semantics and business data (conversion metrics, moderation, etc.), making them a formidable weapon for guiding your strategic choices.

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