LSI Keywords Examples by Industry: 2026 Entity Replacement Playbook

Let me save you some time: if you're still hunting for "LSI keywords" to sprinkle into your content like it's 2014, you're optimizing for a version of Google that no longer exists. Latent Semantic Indexing was a document retrieval technique from the late 1980s. Google's own John Mueller has said publicly that Google doesn't use LSI. What Google does use -- BERT, MUM, the Knowledge Graph, and entity understanding -- is far more powerful, and it requires a fundamentally different approach.

But here's the thing: the intent behind LSI keyword research was always sound. You wanted to prove topical depth. You wanted to show Google you weren't just keyword-stuffing a single phrase. That goal hasn't changed. The method has. In 2026, the replacement is entity-based semantic optimization -- mapping the people, places, concepts, products, and relationships that define a topic, then weaving them into content that both AI overviews and traditional SERPs can parse.

I've spent the last two years building content strategies for clients across SaaS, e-commerce, healthcare, and real estate using entity mapping instead of LSI keyword lists. The difference in ranking velocity is stark. This article is the playbook I wish I'd had when I started.

Why LSI Keywords Are Dead (And What Replaced Them)

LSI was developed in 1988 by researchers at Bell Labs. It uses singular value decomposition on a term-document matrix to find statistical co-occurrence patterns. Google's index processes trillions of documents in real time. LSI doesn't scale to that level -- it was designed for static document collections of a few thousand items.

What Google actually uses in 2026:

• BERT and MUM for understanding natural language queries and content at the passage level
• The Knowledge Graph (launched 2012, now containing billions of entities) for understanding relationships between things
• Neural matching for connecting fuzzy concepts to pages even without exact keyword matches
• Entity recognition and disambiguation to distinguish "Apple" the company from "apple" the fruit based on surrounding context

As Pandu Nayak, VP of Search at Google, put it: "Neural matching helps us understand fuzzier representations of concepts in queries and pages, and match them to one another. It looks at an entire query or page rather than just keywords."

So when someone says "use LSI keywords," what they actually mean in 2026 is: "include semantically related entities that prove topical authority." Let's call it what it is and do it properly.

Entities vs. LSI Keywords: The Actual Difference

This distinction matters more than you think.

Aspect	Old LSI Approach	Entity-Based Approach (2026)
What you're finding	Statistically co-occurring words	Named things with Knowledge Graph entries
How Google uses it	It doesn't (not at scale)	Entity disambiguation, Knowledge Graph matching, AI Overviews
Example for "Python"	code, programming, language, script	Guido van Rossum, CPython, PyPI, PEP 8, Django, Flask
Placement strategy	Keyword density across page	Structured data, entity-rich passages, topical clusters
Content result	Often feels like a thesaurus exploded	Reads like an expert wrote it because it mentions real things
Impact on AI Overviews	Minimal	High -- entities are how LLMs organize knowledge
Measurement	Keyword tracking tools	Entity coverage analysis, Knowledge Graph API

The "Mars test" is a good illustration. Old approach: include words like "planet," "solar system," "red," "orbit." Entity approach: include "Mars rover Perseverance," "Olympus Mons," "Jezero Crater," "NASA JPL," "Elon Musk's Mars colonization plans," "areology." See the difference? One proves you've read about the topic. The other proves you know the topic.

The Entity Extraction Workflow

Here's the workflow I use for every piece of content we produce at Social Animal. It takes about 30 minutes per topic but the ROI is enormous.

Step 1: SERP Entity Mining

1. Search your target keyword in Google
2. Open the top 5 ranking pages
3. Run each URL through Google's Natural Language API (or use a tool like Surfer SEO or InLinks)
4. Extract every entity Google identifies, along with its salience score
5. Compile the entities that appear across 3+ of the top 5 results

These are your "must-have" entities -- the ones Google expects to see on a page about this topic.

Step 2: Knowledge Graph Expansion

# Quick Knowledge Graph API query
curl "https://kgsearch.googleapis.com/v1/entities:search?query=YOUR_TOPIC&key=YOUR_API_KEY&limit=10&indent=True"

This returns entities Google formally recognizes. Look at the description, detailedDescription, and @type fields. These tell you how Google categorizes and understands the entity.

Step 3: People Also Ask Mining

Don't just read the PAA questions -- look at what entities appear in the answers. If Google's AI-generated answers for your topic consistently mention specific brands, people, concepts, or places, those are entities you need to cover.

Step 4: Relationship Mapping

Draw a simple entity map:

[Core Topic Entity]
    ├── [Related Person Entities]
    ├── [Related Product/Tool Entities]
    ├── [Related Concept Entities]
    ├── [Related Organization Entities]
    └── [Related Location Entities]

This becomes your content brief. You're not just listing keywords -- you're mapping the knowledge structure Google expects.

Step 5: Gap Analysis

Compare your entity map against competing pages. Which entities do they miss? Those gaps are your opportunity to demonstrate deeper expertise (E-E-A-T in action).

Industry-by-Industry Entity Examples

Here's where it gets practical. For each industry, I'll show you what the old "LSI keyword" approach looked like, and what the entity-based replacement looks like in 2026.

SaaS / B2B Technology

Target query: "project management software"

Old LSI Keywords	2026 Entity Replacements
task management	Asana, Monday.com, Jira, ClickUp (Product entities)
team collaboration	Agile methodology, Scrum framework, Kanban (Concept entities)
productivity tools	Gantt chart, critical path method, WBS (Method entities)
workflow automation	Zapier, Make.com, n8n (Tool entities)
cloud-based	AWS, SOC 2 compliance, SSO (Technology/Standard entities)

Why this works: When your content mentions specific products, methodologies, and standards, Google recognizes you're writing from expertise. An article that references "SOC 2 compliance" and "critical path method" signals a fundamentally different level of authority than one that just says "productivity tools" and "workflow automation."

E-Commerce / Retail

Target query: "sustainable fashion brands"

Old LSI Keywords	2026 Entity Replacements
eco-friendly clothing	Patagonia, Eileen Fisher, Reformation (Brand entities)
organic fabric	GOTS certification, OEKO-TEX Standard 100 (Certification entities)
ethical manufacturing	Fair Trade, B Corp certification (Organization/Standard entities)
sustainable materials	Tencel (Lenzing AG), organic cotton, recycled polyester (Material entities)
fast fashion	Shein, Zara, UNEP Fashion Charter (Brand/Organization entities)

Healthcare / Medical

Target query: "type 2 diabetes treatment"

Old LSI Keywords	2026 Entity Replacements
blood sugar management	HbA1c test, continuous glucose monitor, Dexcom G7 (Product/Test entities)
insulin resistance	Metformin, GLP-1 receptor agonists, Ozempic (Drug entities)
diabetes diet	glycemic index, ADA dietary guidelines (Concept/Organization entities)
weight management	BMI, bariatric surgery, NAFLD (Metric/Procedure/Condition entities)
diabetes complications	diabetic retinopathy, nephropathy, peripheral neuropathy (Condition entities)

YMYL note: Healthcare content requires extra entity precision. Google's quality raters specifically look for medically accurate entity references in YMYL topics.

Real Estate

Target query: "buying a house in Austin Texas"

Old LSI Keywords	2026 Entity Replacements
home buying process	FHA loan, conventional mortgage, Fannie Mae (Product/Organization entities)
Austin neighborhoods	South Congress, East Austin, Mueller, Domain (Location entities)
property market	Austin Board of Realtors, MLS, Zillow Zestimate (Organization/Product entities)
closing costs	title insurance, escrow, TRID disclosure (Concept/Regulation entities)
property taxes	Travis County Appraisal District, homestead exemption (Organization/Legal entities)

Legal Services

Target query: "personal injury lawyer"

Old LSI Keywords	2026 Entity Replacements
accident attorney	negligence, tort law, statute of limitations (Legal concept entities)
compensation claims	comparative fault, economic damages, HIPAA (Concept/Regulation entities)
injury settlement	mediation, arbitration, contingency fee (Process entities)
car accident lawyer	NHTSA, police report, UM/UIM coverage (Organization/Document entities)

Financial Services

Target query: "retirement planning"

Old LSI Keywords	2026 Entity Replacements
retirement savings	401(k), Roth IRA, SEP IRA, Vanguard, Fidelity (Product/Organization entities)
investment strategy	S&P 500, target-date fund, asset allocation (Index/Concept entities)
financial advisor	CFP certification, fiduciary duty, RIA (Certification/Concept entities)
social security	SSA, FRA (full retirement age), COLA adjustment (Organization/Concept entities)
estate planning	revocable trust, power of attorney, probate (Legal concept entities)

Travel & Hospitality

Target query: "things to do in Tokyo"

Old LSI Keywords	2026 Entity Replacements
Tokyo attractions	Senso-ji, Meiji Shrine, teamLab Borderless (Location/Organization entities)
Japanese food	Tsukiji Outer Market, ramen, izakaya, kaiseki (Location/Food entities)
Tokyo transportation	Suica card, JR Pass, Shinjuku Station (Product/Location entities)
best time to visit	cherry blossom season (hanami), Golden Week (Event entities)
Tokyo neighborhoods	Shibuya, Shinjuku, Akihabara, Harajuku (Location entities)

The Modern Entity Replacement Playbook

Now that you've seen the industry examples, here's how to put this into practice across your content operation.

Step 1: Build Your Entity Vocabulary Per Topic Cluster

For every topic cluster on your site, create an entity reference document. This isn't a keyword list -- it's a knowledge base. Include:

• Core entities (must appear in every piece within the cluster)
• Supporting entities (should appear in most pieces)
• Differentiating entities (appear in specific pieces to add depth competitors miss)

Step 2: Structure Content for Entity Recognition

Google's NLP parses content at the passage level. Structure matters.

<!-- Bad: Entity buried in a wall of text -->
<p>There are many tools available for project management including various 
options that help teams collaborate better and be more productive...</p>

<!-- Good: Entity-rich, clearly structured -->
<h3>How Jira Compares to Linear for Sprint Planning</h3>
<p>Jira, developed by Atlassian, uses a Scrum board approach to sprint 
planning. Linear takes a different stance -- its cycle-based workflow 
reduces ceremony while maintaining velocity tracking through automated 
burndown charts.</p>

Notice how the second version contains multiple recognizable entities (Jira, Atlassian, Linear, Scrum, burndown charts) in a natural, readable way.

Step 3: Implement Schema Markup

This is where your headless CMS implementation matters enormously. Schema.org markup makes your entities machine-readable.

{
  "@context": "https://schema.org",
  "@type": "Article",
  "about": [
    {
      "@type": "Thing",
      "name": "Entity-based SEO",
      "sameAs": "https://en.wikipedia.org/wiki/Semantic_search"
    },
    {
      "@type": "Thing",
      "name": "Knowledge Graph",
      "sameAs": "https://en.wikipedia.org/wiki/Google_Knowledge_Graph"
    }
  ],
  "mentions": [
    {
      "@type": "Organization",
      "name": "Google",
      "sameAs": "https://www.google.com"
    }
  ]
}

The sameAs property is critical -- it ties your content's entities to their canonical definitions in Google's Knowledge Graph.

Step 4: Build Entity Authority Off-Page

This insight comes straight from Brian Dean's 2026 SEO playbook (referenced in recent YouTube content). It's not enough to mention entities on your pages. You need your brand to become an entity associated with your core topics.

How?

• Get mentioned in podcasts and YouTube videos alongside your target topic entities (the transcripts create entity co-occurrence signals)
• Appear in comparison posts and tool roundups (these are entity-dense pages that LLMs parse heavily)
• Contribute to Wikipedia and Wikidata for your brand (this is the foundation of Knowledge Graph entries)
• Use consistent phrasing when describing your brand's relationship to topic entities

Step 5: Optimize for AI Overviews

AI Overviews in Google now dominate informational queries. They're built on entity understanding. To get cited:

• Answer questions with entity-specific facts, not vague generalizations
• Use clear, parseable sentence structures (subject-verb-object with named entities)
• Include data points attached to entities ("Metformin reduces HbA1c by 1-1.5% on average" beats "medication can help lower blood sugar")

Tools That Actually Help in 2026

I've used all of these. Here's my honest take.

Tool	What It Does	Price (2026)	My Verdict
InLinks	Entity-based content optimization, automatic internal linking, schema generation	From $39/mo	Best pure entity tool. The entity mapping is genuinely useful.
Surfer SEO	NLP-based content scoring with entity analysis	From $99/mo	Good for content teams. Entity suggestions have improved significantly.
Clearscope	Content optimization with entity-aware scoring	From $170/mo	Premium but excellent. Reports show exactly which entities top pages cover.
Google NLP API	Raw entity extraction and sentiment analysis	Pay-per-use (~$1/1000 requests)	Best for building custom workflows. Requires developer resources.
Semrush Keyword Magic Tool	Keyword + entity research	From $139/mo	Good all-rounder, but entity features are secondary to keyword data.
MarketMuse	AI content planning with topic modeling	From $149/mo	Strong for content gap analysis at the entity level.

Skip the free "LSI keyword generator" tools. Most of them just scrape Google Autocomplete and call it LSI analysis. You're better off spending 10 minutes on the SERP entity mining workflow I described above.

How This Connects to Technical Implementation

Entity-based SEO isn't just a content strategy -- it has real technical implications for how you build your site.

If you're running a Next.js site, you can programmatically inject JSON-LD schema with entity references at build time. We do this for clients using a headless CMS + Next.js architecture where entities are stored as structured content types and automatically rendered as schema markup.

Astro sites have an even cleaner story here. Astro's island architecture means you can generate entity-rich static HTML that's immediately parseable by Google's crawlers, with zero JavaScript overhead for the entity-critical content.

The key technical requirements:

• Fast rendering of entity-rich content -- Google needs to see your entities on first crawl, not after JavaScript execution
• Structured data at scale -- if you have hundreds of pages, you need programmatic schema generation
• Internal linking based on entity relationships -- not just keyword matching, but linking pages that share entities in a meaningful way
• Clean HTML semantics -- <article>, <section>, proper heading hierarchy all help NLP parsing

This is exactly the kind of architecture we build at Social Animal. If you're curious about what this looks like for your specific situation, check out our pricing or reach out directly.

FAQ

Are LSI keywords still relevant for SEO in 2026?

The term "LSI keywords" is technically inaccurate -- Google has confirmed it doesn't use Latent Semantic Indexing. However, the concept behind it (adding semantically related terms to prove topical depth) is more relevant than ever. The modern version is entity-based optimization, where you include specific named entities (people, products, concepts, organizations) that Google's Knowledge Graph recognizes and associates with your topic.

What's the difference between LSI keywords and entities?

LSI keywords are statistically co-occurring terms found through matrix decomposition of a document set. Entities are real-world things -- people, places, organizations, concepts -- that exist in Google's Knowledge Graph with defined relationships. Entities carry meaning and relationships; LSI keywords are just statistical correlations. When you optimize for entities, you're speaking Google's actual language.

How do I find the right entities for my industry?

Start with Google's SERP. Search your target keyword, open the top 5 results, and note every specific name, product, concept, and organization mentioned across multiple pages. Then use the Google Knowledge Graph API or tools like InLinks or Clearscope to validate which ones Google formally recognizes as entities. Finally, check People Also Ask questions for additional entity signals.

Do entities affect AI Overview rankings?

Absolutely. AI Overviews are built on entity understanding. Google's AI synthesizes information from pages that clearly define and discuss entities relevant to a query. Pages with specific, entity-rich answers ("Metformin is typically the first-line treatment for Type 2 diabetes, recommended by the ADA") get cited far more than pages with generic statements ("medication can help manage diabetes").

Should I still use tools like LSIGraph or similar LSI keyword tools?

You can use them as one input in your research, but don't treat their output as your strategy. Most LSI tools essentially return Google Autocomplete suggestions or co-occurring terms from top-ranking pages. They're a blunt instrument. You'll get better results combining Google's NLP API for entity extraction with manual SERP analysis. Tools like InLinks and Clearscope are better investments because they explicitly model entities rather than keyword co-occurrence.

How many entities should I include per piece of content?

There's no magic number. Analyze the top 3 ranking pages for your target query and count their entities -- that's your baseline. In my experience, a thorough 2,000-word article typically references 15-30 distinct entities naturally. The key word is naturally. Don't force entity mentions. If you're genuinely covering a topic with depth, the entities appear organically because they're part of the knowledge structure.

Does entity optimization help with local SEO?

It's arguably more important for local SEO. Google's local algorithm heavily relies on entity understanding -- your business name, address, and category are all entities in the Knowledge Graph. When your Google Business Profile entities (business name, category, location) match the entities mentioned on your website and across the web (citations, reviews, local content), it creates strong entity coherence that boosts local rankings.

How does schema markup relate to entity SEO?

Schema markup is how you make your entities machine-readable. When you add JSON-LD structured data with @type, name, and sameAs properties, you're explicitly telling Google which entities your content discusses and linking them to their canonical Knowledge Graph entries. It removes ambiguity. Without schema, Google has to infer entities from context. With schema, you're confirming them directly. This is especially powerful for headless CMS setups where schema can be generated programmatically from structured content.