Inverted Index
An inverted index is a core data structure used for full-text retrieval, widely applied in search engines and information retrieval systems. Its fundamental principle involves mapping each term in a document collection to the list of documents containing that term, inverting the forward index structure from "document to terms" to "term to documents." Inverted indexes substantially improve keyword search efficiency and serve as a foundational technology for fast full-text search in modern search engines.
Core Facts
Timeline (last 90 days)
The core of a search index is usually an inverted index, which maps each term to a list of documents containing that term (posting list), along with information such as term frequency and positions.
Full-text search typically relies on an inverted index at its core, and MiniSearch adopts this same approach on the browser side, with the index built within the page and kept resident in memory.
Keyword search fails in synonym and translation scenarios because there is no overlap at the letter level, and the inverted index cannot find a match.
Vector retrieval captures semantic relevance rather than literal matching, which is its essential difference from keyword search.
Full-text search typically relies on inverted index technology to achieve millisecond-level retrieval response.
An inverted index maps terms to the list of documents containing those terms and is the core data structure of full-text search engines.
All Facts (6)
Vector retrieval captures semantic relevance rather than literal matching, which is its essential difference from keyword search.
75%UnverifiedThe core of a search index is usually an inverted index, which maps each term to a list of documents containing that term (posting list), along with information such as term frequency and positions.
50%UnverifiedFull-text search typically relies on an inverted index at its core, and MiniSearch adopts this same approach on the browser side, with the index built within the page and kept resident in memory.
50%UnverifiedKeyword search fails in synonym and translation scenarios because there is no overlap at the letter level, and the inverted index cannot find a match.
50%UnverifiedFull-text search typically relies on inverted index technology to achieve millisecond-level retrieval response.
50%UnverifiedAn inverted index maps terms to the list of documents containing those terms and is the core data structure of full-text search engines.
50%