Practical/Lab Query Entity Extraction with Google NLP and Entity ML-enabled data analysis – Lesson Preview
Turn messy keyword lists into structured, decision-ready insight. In this hands-on lab, you’ll extract entities from search queries with Google’s Natural Language API, then combine that output with lightweight machine learning to uncover relationships, clusters, and opportunities across a large keyword universe. You’ll see both a no-code Google Sheets workflow and a faster, programmatic Google Colab path and ready-to-run scripts.
Why this matters for Marketing and SEO professionals now: entity-level understanding powers better topic selection, sharper content briefs, and scalable on-page optimization. The lesson shows how to move beyond single-keyword metrics by calculating dataset-level prominence, blending entity results with your master keyword sheet (volume, competitiveness, CPC), and visualizing how entities connect, so you can prioritize what to cover next and where to deepen topical authority.
You’ll also explore clustering and graph techniques to group semantically related entities and map proximity between topics. Finally, you’ll tap the Google Knowledge Graph Search API to check what’s “in graph” and pull related entities and metadata for enrichment, all with copy-ready code and clear guardrails.
What you’ll learn (why it matters)
- Run entity extraction at scale because manual tagging doesn’t scale.
- Blend entities with keyword KPIs because opportunity lives at the intersection.
- Score dataset-level prominence because per-query salience can mislead.
- Visualize relationships & clusters because content plans need structure.
- Leverage Knowledge Graph data because “in-graph” entities inform coverage.
Key concepts (with mini-definitions)
- Entity Extraction (NER) — supervised ML that identifies real-world entities in text.
- Salience (prominence) — API score of an entity’s importance within a text sample.
- Entity Mentions/Variations — different surface forms the API links to the same entity.
- TF-IDF — converts entity text into weighted vectors for feature representation.
- Cosine Similarity — measures closeness between vectors to infer related entities.
- Similarity Thresholding — filters weak links to keep only meaningful relationships.
- K-Means Clustering — groups entities by semantic similarity without labels.
- PCA (Dimensionality Reduction) — projects vectors to 2D for clearer visualization.
Tools mentioned
Google Natural Language API, Google Knowledge Graph Search API, Google Sheets, Google Apps Script, Google Colab, Python, TF-IDF, cosine similarity, NetworkX, Plotly, K-Means, PCA and MLforSEO no-code template.
Practice & readings
- Follow the MLforSEO no-code Sheets template to extract entities and sentiment.
- Run the linked Google Colab to batch process ~thousands of queries and auto-download CSVs.
- Read the MLforSEO blog on mapping keywords to topics (K-Means, SBERT, BERTopic, Fuzzy Matching).
Key insights & takeaways
- Sheets is fine for small tests; switch to Colab for speed and scale.
- Per-query salience is limited for short queries; build a dataset-level prominence metric.
- Merge entity output with volume/competition to surface true opportunities.
- Graphs and clusters reveal proximity—grow topical authority near what you already own.
- Human review still matters; enrich automated results with expert judgment.
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