- Elasticsearch - The Definitive Guide:
- Foreword
- Preface
- Getting Started
- You Know, for Search…
- Installing and Running Elasticsearch
- Talking to Elasticsearch
- Document Oriented
- Finding Your Feet
- Indexing Employee Documents
- Retrieving a Document
- Search Lite
- Search with Query DSL
- More-Complicated Searches
- Full-Text Search
- Phrase Search
- Highlighting Our Searches
- Analytics
- Tutorial Conclusion
- Distributed Nature
- Next Steps
- Life Inside a Cluster
- Data In, Data Out
- What Is a Document?
- Document Metadata
- Indexing a Document
- Retrieving a Document
- Checking Whether a Document Exists
- Updating a Whole Document
- Creating a New Document
- Deleting a Document
- Dealing with Conflicts
- Optimistic Concurrency Control
- Partial Updates to Documents
- Retrieving Multiple Documents
- Cheaper in Bulk
- Distributed Document Store
- Searching—The Basic Tools
- Mapping and Analysis
- Full-Body Search
- Sorting and Relevance
- Distributed Search Execution
- Index Management
- Inside a Shard
- You Know, for Search…
- Search in Depth
- Structured Search
- Full-Text Search
- Multifield Search
- Proximity Matching
- Partial Matching
- Controlling Relevance
- Theory Behind Relevance Scoring
- Lucene’s Practical Scoring Function
- Query-Time Boosting
- Manipulating Relevance with Query Structure
- Not Quite Not
- Ignoring TF/IDF
- function_score Query
- Boosting by Popularity
- Boosting Filtered Subsets
- Random Scoring
- The Closer, The Better
- Understanding the price Clause
- Scoring with Scripts
- Pluggable Similarity Algorithms
- Changing Similarities
- Relevance Tuning Is the Last 10%
- Dealing with Human Language
- Aggregations
- Geolocation
- Modeling Your Data
- Administration, Monitoring, and Deployment
WARNING: This documentation covers Elasticsearch 2.x. The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Proximity Matching
editProximity Matching
editStandard 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.
In this chapter, we are using the same example documents that we used for
the match query.