Relevance ranking for proximity full-text search based on additional indexes with multi-component keys

TL;DR

This paper explores how multi-component key indexes improve the speed of proximity full-text search, especially for high-frequency words, while maintaining comparable relevance ranking quality to traditional inverted indexes.

Contribution

It demonstrates that multi-component key indexes can significantly accelerate search without compromising relevance ranking quality.

Findings

Search speed increased up to 130 times for high-frequency words.

Relevance ranking results are very close to those from ordinary inverted indexes.

Multi-component key indexes effectively balance speed and relevance quality.

Abstract

The problem of proximity full-text search is considered. If a search query contains high-frequently occurring words, then multi-component key indexes deliver an improvement in the search speed compared with ordinary inverted indexes. It was shown that we can increase the search speed by up to 130 times in cases when queries consist of high-frequently occurring words. In this paper, we investigate how the multi-component key index architecture affects the quality of the search. We consider several well-known methods of relevance ranking, where these methods are of different authors. Using these methods, we perform the search in the ordinary inverted index and then in an index enhanced with multi-component key indexes. The results show that with multi-component key indexes we obtain search results that are very close, in terms of relevance ranking, to the search results that are obtained by means of ordinary inverted indexes.