Elasticsearch: The Definitive Guide

Chapter 15. Proximity Matching

Standard full-text search with TF/IDF treats documents, or at least each field within a document, as a big bag of words. The match query can tell us whether that bag contains our search terms, but that is only part of the story. It can’t tell us anything about the relationship between words.

Consider the difference between these sentences:

• Sue ate the alligator.

• The alligator ate Sue.

• Sue never goes anywhere without her alligator-skin purse.

A match query for sue alligator would match all three documents, but it doesn’t tell us whether the two words form part of the same idea, or even the same paragraph.

Understanding how words relate to each other is a complicated problem, and we can’t solve it by just using another type of query, but we can at least find words that appear to be related because they appear near each other or even right next to each other.

Each document may be much longer than the examples we have presented: Sue and alligator may be separated by paragraphs of other text. Perhaps we still want to return these documents in which the words are widely separated, but we want to give documents in which the words are close together a higher relevance score.

This is the province of phrase matching, or proximity matching.

Tip

In this chapter, we are using the same example documents that we used for the match query.

Phrase Matching

In the same way that the match query is the go-to query for standard full-text search, the match_phrase ...