Semrush Keyword Research, Augmented with KeywordGraph: From Keyword Magic to Topic Clusters

Export from Semrush Keyword Magic Tool

Step 1 · 5 minutes inside Semrush

In Semrush, open the Keyword Magic Tool and type the seed phrase. The tool returns the full keyword family with volume, keyword difficulty (KD), CPC, intent, and SERP features for each query.

Apply a minimum-volume filter to drop the long-tail noise — 50–100 monthly searches is the usual cutoff. Optionally filter by intent (Informational, Navigational, Commercial, Transactional) if the publishing plan is intent-segmented.

For broader coverage, also pull the related Questions and Related Keywords reports for the same seed. The Topic Research tool can surface adjacent topic ideas worth pulling into the same CSV.

Export the filtered table as a CSV with UTF-8 encoding. The columns that matter downstream are keyword, volume, and KD (Semrush’s Keyword Difficulty score, 0–100).

Import the CSV into KeywordGraph

Step 2 · 3 minutes inside KeywordGraph

In KeywordGraph, start a CSV import. On the column-mapping step, choose the keyword column as the column to analyze. This is the column the co-occurrence graph will be built from.

On the next step, mark KD and volume as filter columns. Semrush’s KD is on a 0–100 scale comparable to Ahrefs’s Difficulty; the filter slider works the same way.

The import takes a minute or two depending on row count. The output is a knowledge graph where each unique entity in the keyword strings is a node and each co-occurrence of two entities inside the same keyword string is an edge.

Read the topical clusters

Step 3 · 15–30 minutes of interpretation

The graph surfaces clusters automatically. Each cluster is a candidate page or page group. Node centrality identifies which entity sits at the center of each cluster — the candidate for the canonical article. Surrounding nodes become perimeter pages or sections within the central article.

A useful refinement: remove the dominant seed nodes from the graph (the words that appear in every keyword) and re-run the cluster detection. The clusters recompute against the surrounding context and reveal tighter, more publishable topics.

Drilling into any cluster shows the specific keyword combinations inside — useful for confirming the cluster name, spotting outliers, and identifying combinations that bridge to adjacent clusters.

Filter by KD and volume to find the overlap

Step 4 · 10 minutes

With the filter columns set up in step 2, apply both sliders to surface the high-volume / low-competition segments. A useful default:

• KD: 30 to 59 — low enough to avoid the highest-competition head terms; high enough to skip uncontested noise.
• Volume: 100 to 1,200 — meaningful demand without jumping into the saturated head-term band.

Selecting the overlap of both filters reveals the keyword combinations that deserve the first wave of publishing. Combinations in this overlap typically belong on perimeter pages that close coverage gaps in the cluster — the highest-leverage publishing targets for new content.

Lower the KD band further and the surface shifts toward easier wins with smaller volume. Raise it and the surface shifts toward more competitive head-term-adjacent queries.