Boost Local SEO with Entity Linking: A Case Study

The digital landscape for local businesses has transformed, with search engines now prioritizing semantic understanding to deliver accurate results. This shift means that simply mentioning a location or service is no longer enough; search algorithms need to grasp the context and relationships between topics to establish a site’s authority. For companies operating in multiple markets, this presents both a challenge and a significant opportunity. Misinterpreted place names or service offerings can direct potential customers to the wrong page, but a strategic approach to entity linking provides the clarity needed to excel in local search results.

This semantic clarity is achieved by treating core topics, known as entities, as multidimensional concepts rather than simple keywords. By properly defining these entities within content and through structured data, businesses can communicate precisely what they offer and where. As noted in recent industry discussions, schema markup transforms plain text into structured data that machines can interpret confidently, which is fundamental for performance in AI-driven search. Our work with Brightview Senior Living, a client with over 47 locations, demonstrates the power of this approach. Entity linking enabled them to scale their SEO effectively, gaining clarity, authority, and improved local performance across dozens of distinct markets.

Understanding the role of entities is crucial for modern local SEO. Search engines now evaluate what entities are mentioned on a page, how they relate to a user’s query, and whether the content provides meaningful context. An entity can be a location, service, product, person, or any concept with a definable meaning. Identifying an entity is just the beginning; search systems also need to understand its context. This is where schema markup properties become invaluable, as they help disambiguate what an entity truly represents. When optimizing a page, you describe its primary entity using the schema.org vocabulary, leveraging properties to give search engines a structured way to comprehend it.

For instance, describing a physical location involves defining it as a LocalBusiness entity. You would use schema properties to detail the business, its service area, and other attributes that mirror the page’s content. After defining the entity, the next step is entity linking, which comes in two forms: internal and external. Internal entity linking connects to other relevant entities within your own website, while external entity linking connects entities on your site to their definitions in authoritative external knowledge bases like Wikipedia or Wikidata. This is accomplished using schema properties such as `sameAs` or `mentions`. External linking is particularly powerful, as it provides search engines with explicit, trusted definitions, reducing ambiguity and enhancing relevance for rankings and AI summaries.

For local search optimization, place-based entity linking delivers exceptional impact. Brightview’s marketing team faced the complex task of managing performance for more than 47 community pages, each with unique names, local contexts, and service mixes. Search engines frequently misinterpreted these pages, especially when a location name was shared with a larger city elsewhere. A classic case was Phoenix, Maryland, being confused with Phoenix, Arizona, which jeopardized visibility for critical searches like “assisted living near me.” To address this, they adopted a future-proof strategy centered on semantic clarity through entity-first SEO.

Their solution involved systematic place-based and topical entity linking at scale. First, they disambiguated place names on every community page. Using schema markup, they employed properties like `mentions` to identify the specific place referenced and `areaServed` to clarify the geographic region. Critically, they used `sameAs` to link each location entity to authoritative sources such as Wikipedia, directly resolving confusion over similarly named places and strengthening signals for geo-modified queries. Second, they mapped key services as entities. Core terms like “assisted living” were linked to authoritative definitions using `sameAs`, helping Brightview appear more consistently for high-intent, non-branded searches early in the customer journey. This established their content as authoritative on these topics, expanding visibility beyond brand-dependent queries.

Finally, they scaled entity linking across all content types, community pages, blog posts, and informational resources. This created a connected content knowledge graph that reinforced Brightview’s authority on relevant topics and locations. The outcome was a website where search engines could clearly understand each page’s subject, its represented location, and its relation to the organization’s broader expertise. This clarity made it easier for AI systems to return correct answers when users searched for care options in specific areas.

The results were significant and measurable. Brightview experienced stronger performance in non-branded search, which is vital for attracting users still evaluating providers. By clearly defining service entities, they achieved a 25% increase in clicks and a 30% increase in impressions for non-branded queries featuring “assisted living.” Their community pages also saw higher discoverability for high-intent local searches, with a 16% year-over-year increase in clicks and a 26% increase in impressions, despite industry-wide declines. Perhaps most notably, their click-through rate remained stable relative to benchmarks as AI Overviews changed SERP dynamics. Their SEO consultant highlighted that robust schema markup implementation was a direct driver of this sustained performance, demonstrating causation in their competitive results.

To apply entity linking strategically, begin by identifying the entities that define your authority. Focus on locations you want to rank for, core service offerings, product categories, and topics where you seek recognition. Consistently linking these entities signals your expertise to search engines. Next, aim to build a connected content knowledge graph. This framework shows the relationships between your locations, offerings, and resources, helping machines infer meaning and deliver accurate results that influence conversions. For businesses with multiple locations, prioritizing place-based entity linking is essential. It provides explicit signals about which location a page refers to, the services available there, and the geographic region it serves, directly boosting visibility for geo-modified and near-me queries.

Furthermore, entity linking prepares your content for AI search experiences. When locations, services, and concepts are linked to authoritative sources, AI systems can return more precise answers and are more likely to reference your content correctly. Brightview’s case study illustrates that entity linking is a practical, high-impact method for strengthening local search performance. By clarifying locations, services, and key concepts, you help search engines and AI systems understand exactly what your content represents. This not only improves semantic accuracy but also builds a foundation for long-term authority, making it one of the most actionable strategies for succeeding in the future of semantic and AI-driven search.

(Source: Search Engine Journal)