Published:
by Wayne Smith
Keyword proximity is a long-standing on-page ranking factor—one of SEO’s tenured signals that continues to matter even in the era of entity-based and AI-aware search. Originally designed to measure how closely query terms appear together in text, proximity remains relevant within modern contexts. Large language models (LLMs) and semantic search systems still rely on proximity within contextual relationships to help determine meaning and intent.
The reason is clear when we consider the limitations of simple keyword occurrence. A page containing the words “apple” and “pie” separately is not necessarily relevant for the phrase “apple pie.” Proximity differentiates between coincidental co-occurrence and genuine contextual connection.
AltaVista was among the first full-page search engines to expose keyword proximity as a user-controllable ranking factor. It appeared almost as an afterthought—tucked under advanced search options or invoked with the tilde symbol (~) to indicate that two words should appear near each other (e.g., “apple ~ pie”). Search engines that followed, however, embedded proximity directly into their algorithms—progressing from string-based matching toward phrase recognition. Over time, this evolved into the recognition of multiword concepts such as “apple pie” as single entities.
Today, search has shifted from “strings to things.” Keyword proximity remains a factor, but its interpretation has evolved. In AI and LLM systems, semantic proximity—or vector-based representation—extends beyond literal word distance. It measures the conceptual closeness between terms, allowing meaning to persist even when different words are used instead of the original query phrase.
Words inside blocks vs nodes inside blocks
Traditional on-page content practices continue to perform well under both entity-based search and LLM interpretation. In many ways, a keyword, entity, and node function similarly—they each represent identifiable concepts or objects within a document. However, there are nuanced differences.
The transformation toward entity-based search began with the manual creation of large-scale entity databases. One of the most influential efforts was Metaweb’s Freebase, an open-source knowledge base built through human curation. Google acquired Freebase in July 2010, integrating its structured data into what became the Knowledge Graph. Today, entities are no longer handcrafted at scale—they are emergent byproducts of large language models (LLMs) and machine-learned embeddings, which infer entity boundaries and relationships directly from text.
Entity: Word proximity (How Keywords Form Conceptual Units)
For entities such as “apple pie” or “Eiffel Tower,” the component words function as siblings within a single conceptual unit. In earlier search models, proximity signaled this relationship explicitly—words that frequently appeared together were likely part of the same concept.
While traditional search models relied on explicit word adjacency to detect entities, modern AI systems extend this logic by interpreting semantic relationships across more flexible contexts.
LLM: Semantic proximity within a block of text (Understanding Context Beyond Exact Words)
Modern AIs extend traditional keyword proximity logic: they can infer that a phrase like “a pie made with apples” refers to the same “apple pie” entity, even though the exact words differ.
This shift from literal proximity to semantic proximity—from physical distance between words to conceptual distance within vector space—marks the evolution from keyword-based relevance to meaning-based understanding.
It should be noted that while semantic proximity or vectored entity representation can be observed and does increase visibility when detectable, not all pages receive this level of processing. Basic on-page and off-page optimization must be in place before semantic-level processing can take effect.
Semantic proximity is a forward-thinking strategy, applied after addressing core site health issues such as:
- Thin content
- Soft 404 errors
- Content cannibalization
Optimizing for semantic proximity is most effective for pages that already have above-the-fold search visibility and want to leverage that foundation for Answer Engine Optimization (AEO) and advanced AI-aware search visibility.
There is also a balancing act involved: content that is not well understood provides less visibility, and in some cases, less content may perform better. While semantic proximity is present in all AI interpretation, its measurable impact on rankings typically emerges only once a page achieves baseline visibility and technical clarity.
Entity-Level Keyword Proximity — Implementation Examples
In modern SEO, implementing entity-based keyword proximity is straightforward when related keywords or entities are grouped logically within a content block.
For example, consider the following paragraph, where the entities Keyword1 and Keyword2 appear together, establishing proximity in a way that is friendly for AI interpretation:
Entity keyword proximity can also be implemented within a list, where related entities are grouped as sibling elements:
The bottom line is that entities or keywords matching the search query should appear as close as possible within the block to maximize proximity signals.
Basic AI Layer (RankBrain, Hummingbird, BERT, MUM, etc.) — Keyword Proximity
Foundational AI models such as RankBrain or BERT introduced limited forms of semantic proximity—recognizing patterns of meaning beyond exact keywords. LLMs expand this further, interpreting meaning at the conceptual and contextual level. This layer primarily evaluates query intent and document relevance, recognizing entities and basic semantic relationships. Semantically related keywords within a web page are analyzed to assess topical depth and the quality of understanding conveyed by the content.
The LEDE, TLDR, or opening block of text benefits from keyword proximity and gains additional relevance when related entities are grouped within the same block. Subsequent statements, such as “Entity1 is …,” are also evaluated. Although these systems are not as advanced as LLMs, well-structured content blocks can attract closer inspection and may be selected for featured snippets.
It should also be noted that Google may occasionally apply foundational-level AI to rewrite web page descriptions or generate AI overviews that leverage semantically proximate language to convey meaning.
The core principle of entity or keyword proximity remains the same at this layer. However, instead of focusing on exact keyword matches, the emphasis has shifted toward meaning—semantic proximity—a shift that becomes more pronounced at the LLM level.