I have been reading up on ElasticSearch and couldn't find an answer for how to do the following:
Say, you have some records with, "study" in the title and a user uses the word "studying" instead of "study". How would you set up ElasticSearch to match this?
Thanks, Alex
ps: Sorry, if this is a duplicate. Wasn't sure what to search for!
You might be interested in this: http://www.elasticsearch.org/guide/reference/query-dsl/flt-query/
For eg: I have indexed book titles and on this query:
{
"query": {
"bool": {
"must": [
{
"fuzzy": {
"book": {
"value": "ringing",
"min_similarity": "0.3"
}
}
}
]
}
}
}
I got
{
"took" : "1",
"timed_out" : "false",
"_shards" : {
"total" : "5",
"successful" : "5",
"failed" : "0"
}
"hits" : {
"total" : "1",
"max_score" : "0.19178301",
"hits" : [
{
"_index" : "library",
"_type" : "book",
"_id" : "3",
"_score" : "0.19178301",
"_source" : {
"book" : "The Lord of the Rings",
"author" : "J R R Tolkein"
}
}
]
}
}
which is the only correct result..
You could apply stemming to your documents, so that when you index studying, you are beneath indexing study. And when you query you do the same, so that when you search for studying again, you'll be searching for study and you'll find a match, both looking for study and studying.
Stemming depends of course on the language and there are different techniques, for english snowball is fine. What happens is that you lose some information when you index data, since as you can see you cannot really distinguish between studying and study anymore. If you want to keep that distinction you could index the same text in different ways using a multi_field and apply different text analysis to it. That way you could search on multiple fields, both the non stemmed version and stemmed version, maybe giving different weights to them.
