The concept encompassing entities, their attributes, and the specific values (variables) associated with those attributes.
The EAV Model organizes keyword semantics by identifying Entities, their Attributes, and the Variables (values) associated with those attributes. The EAV model allows marketers to discover patterns in search behavior, facilitating a programmatic approach to keyword research where keyword lists are generated by completing these structural patterns. Effectively implementing the EAV model requires a deep understanding of the existing informational landscape (competing pages) and the ability to produce original content to fill identified information gaps.
Sources & References
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AI Overview
AI-generated summaries of highly informational, low-intent queries, offering quick answers to users, or generally, a…
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Artificial Intelligence (AI)
The overarching concept related to the design and study of intelligent systems. Early systems relied…
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Augmented Search Queries
Queries that expand or modify the original user query to improve search accuracy and relevance…
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Bag of Words
A type of semantic representation of data, which can be extracted from page contents.
B
Bigram
A sequence of two adjacent words.
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Deep Learning
A part of machine learning; Generative AI models like ChatGPT and LLM-based chatbots fall within…
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Dimensionality Reduction
A process that reduces data, such as high-dimensional vectors, for visualization while preserving semantic structure…
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Embedding
A numerical representation capturing the meaning of a document or data. Also referred to as…
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Entity
A representation of real-world objects (people, products, places, concepts) that hold value from an SEO…
E
Entity Attribute (EAV Model)
Defining properties or characteristics of an entity (e.g., location, niche) used in the EAV model…
E
Entity Variables (EAV)
Specific values an entity attribute can take (e.g., London, Paris for the Location attribute).
F
Feature Extraction
The process of converting entities into numerical representations based on term importance (e.g., using TF-IDF).