RepositoryWebrefPreprocessingNameEntityScores

AI Overview😉

  • Potential purpose of module in simple language: This module appears to be responsible for scoring and ranking name variants (e.g., different ways of referring to the same entity) based on their relevance and evidence from various sources. It calculates two types of scores: prior score (unnormalized measure of evidence) and volume-based score (quantifiable measure from a specific source).
  • How it could impact search results: This module could impact search results by influencing the ranking of search results that contain different name variants of the same entity. For example, if a user searches for "Apple" (the company), the module would help determine which results to show first based on the evidence and relevance of different name variants (e.g., "Apple Inc.", "Apple Computers", etc.). This could lead to more accurate and relevant search results.
  • How a website may change things to be more favorable for this function: To be more favorable for this module, a website could: (1) use consistent and accurate naming conventions for entities, (2) provide clear and concise descriptions of entities, (3) use structured data (e.g., schema.org) to help search engines understand the relationships between entities and their name variants, and (4) ensure that their content is high-quality, relevant, and trustworthy, which could increase the evidence score for their entity mentions.

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GoogleApi.ContentWarehouse.V1.Model.RepositoryWebrefPreprocessingNameEntityScores (google_api_content_warehouse v0.4.0)

Abstract, source independent scores. Next available tag: 7

Attributes

  • priorScore (type: float(), default: nil) - An unnormalized measure of how much evidence we have that this name variant refers to the key entity. Should be comparable to all scores from the same source for: - other entities having the same name variant - the open world score computed for this name variant
  • volumeBasedScore (type: float(), default: nil) - Prior score come from source that is quantifiable. artificial_score = prior_score - volume_based_score.

Summary

Types

t()

Functions

decode(value, options)

Unwrap a decoded JSON object into its complex fields.

Types

Link to this type

t()

@type t() ::
  %GoogleApi.ContentWarehouse.V1.Model.RepositoryWebrefPreprocessingNameEntityScores{
    priorScore: float() | nil,
    volumeBasedScore: float() | nil
  }

Functions

Link to this function

decode(value, options)

@spec decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.