Searching names: increasing relevance for proximate matches in multivalued field
I'm trying to get a name field working reasonably in Elasticsearch, and am having trouble finding guidance. Please help me, Internet!
My documents have several authors, and so a multi-valued name field. Let's say I have a search for paul f tompkins, and two documents: {"authors": ["Paul Tompkins", "Dietrich Kohl"]} and {"authors": ["Paul Wang", "Darlene Tompkins"]}.
My search will retrieve both documents easily enough, but both will have the same score from the authors query. I'd like the fact that I matched multiple terms within the same item of the authors array to boost the score of the first document.
How can I do that? The two techniques that I know of for boosting for proximity are shingles (which I believe would generate the paul_f and f_tompkins shingles, neither of which match) and a phrase query with slop (which would fail because the f token isn't there).
Ideally I'd want something like a phrase slop query with minimum_should_match: I give it four words, it matches if at least two are present in the same array element, with each additional matching term in the same array element raising the score. I couldn't figure out how to do that.
(It wouldn't work for me to have client-side logic that tries to strip the f out of the query -- this is a simplified example, but assume that I also want to be able handle a queries like paul francis tompkins or paul f tompkins there will be blood.)
elasticsearch search full-text-search
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
add a comment |
I'm trying to get a name field working reasonably in Elasticsearch, and am having trouble finding guidance. Please help me, Internet!
My documents have several authors, and so a multi-valued name field. Let's say I have a search for paul f tompkins, and two documents: {"authors": ["Paul Tompkins", "Dietrich Kohl"]} and {"authors": ["Paul Wang", "Darlene Tompkins"]}.
My search will retrieve both documents easily enough, but both will have the same score from the authors query. I'd like the fact that I matched multiple terms within the same item of the authors array to boost the score of the first document.
How can I do that? The two techniques that I know of for boosting for proximity are shingles (which I believe would generate the paul_f and f_tompkins shingles, neither of which match) and a phrase query with slop (which would fail because the f token isn't there).
Ideally I'd want something like a phrase slop query with minimum_should_match: I give it four words, it matches if at least two are present in the same array element, with each additional matching term in the same array element raising the score. I couldn't figure out how to do that.
(It wouldn't work for me to have client-side logic that tries to strip the f out of the query -- this is a simplified example, but assume that I also want to be able handle a queries like paul francis tompkins or paul f tompkins there will be blood.)
elasticsearch search full-text-search
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
add a comment |
I'm trying to get a name field working reasonably in Elasticsearch, and am having trouble finding guidance. Please help me, Internet!
My documents have several authors, and so a multi-valued name field. Let's say I have a search for paul f tompkins, and two documents: {"authors": ["Paul Tompkins", "Dietrich Kohl"]} and {"authors": ["Paul Wang", "Darlene Tompkins"]}.
My search will retrieve both documents easily enough, but both will have the same score from the authors query. I'd like the fact that I matched multiple terms within the same item of the authors array to boost the score of the first document.
How can I do that? The two techniques that I know of for boosting for proximity are shingles (which I believe would generate the paul_f and f_tompkins shingles, neither of which match) and a phrase query with slop (which would fail because the f token isn't there).
Ideally I'd want something like a phrase slop query with minimum_should_match: I give it four words, it matches if at least two are present in the same array element, with each additional matching term in the same array element raising the score. I couldn't figure out how to do that.
(It wouldn't work for me to have client-side logic that tries to strip the f out of the query -- this is a simplified example, but assume that I also want to be able handle a queries like paul francis tompkins or paul f tompkins there will be blood.)
elasticsearch search full-text-search
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
I'm trying to get a name field working reasonably in Elasticsearch, and am having trouble finding guidance. Please help me, Internet!
My documents have several authors, and so a multi-valued name field. Let's say I have a search for paul f tompkins, and two documents: {"authors": ["Paul Tompkins", "Dietrich Kohl"]} and {"authors": ["Paul Wang", "Darlene Tompkins"]}.
My search will retrieve both documents easily enough, but both will have the same score from the authors query. I'd like the fact that I matched multiple terms within the same item of the authors array to boost the score of the first document.
How can I do that? The two techniques that I know of for boosting for proximity are shingles (which I believe would generate the paul_f and f_tompkins shingles, neither of which match) and a phrase query with slop (which would fail because the f token isn't there).
Ideally I'd want something like a phrase slop query with minimum_should_match: I give it four words, it matches if at least two are present in the same array element, with each additional matching term in the same array element raising the score. I couldn't figure out how to do that.
(It wouldn't work for me to have client-side logic that tries to strip the f out of the query -- this is a simplified example, but assume that I also want to be able handle a queries like paul francis tompkins or paul f tompkins there will be blood.)
elasticsearch search full-text-search
elasticsearch search full-text-search
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
asked Nov 22 '18 at 19:33
michaelmichael
1,5682109
1,5682109
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
The reason both of the docs have same score is because the author field is array of text values. If we change the way we store authors we can get the desired result. To do so lets make authors as a nested type. So we have the following mapping:
"mappings": {
"_doc": {
"properties": {
"authors": {
"type": "nested",
"properties": {
"name": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
}
}
Note: Sub field raw can be used for some other scenarios and has no relation to the solution.
Now lets index the docs as below:
Doc 1:
{
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
Doc 2:
{
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
Lets query them as follows:
{
"explain": true,
"query": {
"nested": {
"path": "authors",
"query": {
"query_string": {
"query": "paul l tompkins",
"fields": [
"authors.name"
]
}
}
}
}
}
Result:
"hits": {
"total": 2,
"max_score": 1.3862944,
"hits": [
{
"_index": "test",
"_type": "_doc",
"_id": "1",
"_score": 1.3862944,
"_source": {
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
},
{
"_index": "test",
"_type": "_doc",
"_id": "2",
"_score": 0.6931472,
"_source": {
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
}
]
}
NOTE: In the query I have also used explain:true. This gives the explanation of the score calculation.(I have not included the explain output above as it is very long. You can try it though).
When we look at the scoring mechanism, we can see the difference while querying on nested field and when querying on an array. Broadly speaking since nested fields are stored as separate docs, Doc 1 is scored higher because the child doc 1 i.e. :
{
"name": "Paul Tompkins"
}
will have higher score because both the terms paul and tompkins are in the same child doc.
In case of array all the names belong same field and not as separate child docs and hence the difference.
This way we can achieve the desired result.
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
add a comment |
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1 Answer
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active
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1 Answer
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active
oldest
votes
active
oldest
votes
active
oldest
votes
The reason both of the docs have same score is because the author field is array of text values. If we change the way we store authors we can get the desired result. To do so lets make authors as a nested type. So we have the following mapping:
"mappings": {
"_doc": {
"properties": {
"authors": {
"type": "nested",
"properties": {
"name": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
}
}
Note: Sub field raw can be used for some other scenarios and has no relation to the solution.
Now lets index the docs as below:
Doc 1:
{
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
Doc 2:
{
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
Lets query them as follows:
{
"explain": true,
"query": {
"nested": {
"path": "authors",
"query": {
"query_string": {
"query": "paul l tompkins",
"fields": [
"authors.name"
]
}
}
}
}
}
Result:
"hits": {
"total": 2,
"max_score": 1.3862944,
"hits": [
{
"_index": "test",
"_type": "_doc",
"_id": "1",
"_score": 1.3862944,
"_source": {
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
},
{
"_index": "test",
"_type": "_doc",
"_id": "2",
"_score": 0.6931472,
"_source": {
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
}
]
}
NOTE: In the query I have also used explain:true. This gives the explanation of the score calculation.(I have not included the explain output above as it is very long. You can try it though).
When we look at the scoring mechanism, we can see the difference while querying on nested field and when querying on an array. Broadly speaking since nested fields are stored as separate docs, Doc 1 is scored higher because the child doc 1 i.e. :
{
"name": "Paul Tompkins"
}
will have higher score because both the terms paul and tompkins are in the same child doc.
In case of array all the names belong same field and not as separate child docs and hence the difference.
This way we can achieve the desired result.
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
add a comment |
The reason both of the docs have same score is because the author field is array of text values. If we change the way we store authors we can get the desired result. To do so lets make authors as a nested type. So we have the following mapping:
"mappings": {
"_doc": {
"properties": {
"authors": {
"type": "nested",
"properties": {
"name": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
}
}
Note: Sub field raw can be used for some other scenarios and has no relation to the solution.
Now lets index the docs as below:
Doc 1:
{
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
Doc 2:
{
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
Lets query them as follows:
{
"explain": true,
"query": {
"nested": {
"path": "authors",
"query": {
"query_string": {
"query": "paul l tompkins",
"fields": [
"authors.name"
]
}
}
}
}
}
Result:
"hits": {
"total": 2,
"max_score": 1.3862944,
"hits": [
{
"_index": "test",
"_type": "_doc",
"_id": "1",
"_score": 1.3862944,
"_source": {
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
},
{
"_index": "test",
"_type": "_doc",
"_id": "2",
"_score": 0.6931472,
"_source": {
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
}
]
}
NOTE: In the query I have also used explain:true. This gives the explanation of the score calculation.(I have not included the explain output above as it is very long. You can try it though).
When we look at the scoring mechanism, we can see the difference while querying on nested field and when querying on an array. Broadly speaking since nested fields are stored as separate docs, Doc 1 is scored higher because the child doc 1 i.e. :
{
"name": "Paul Tompkins"
}
will have higher score because both the terms paul and tompkins are in the same child doc.
In case of array all the names belong same field and not as separate child docs and hence the difference.
This way we can achieve the desired result.
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
add a comment |
The reason both of the docs have same score is because the author field is array of text values. If we change the way we store authors we can get the desired result. To do so lets make authors as a nested type. So we have the following mapping:
"mappings": {
"_doc": {
"properties": {
"authors": {
"type": "nested",
"properties": {
"name": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
}
}
Note: Sub field raw can be used for some other scenarios and has no relation to the solution.
Now lets index the docs as below:
Doc 1:
{
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
Doc 2:
{
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
Lets query them as follows:
{
"explain": true,
"query": {
"nested": {
"path": "authors",
"query": {
"query_string": {
"query": "paul l tompkins",
"fields": [
"authors.name"
]
}
}
}
}
}
Result:
"hits": {
"total": 2,
"max_score": 1.3862944,
"hits": [
{
"_index": "test",
"_type": "_doc",
"_id": "1",
"_score": 1.3862944,
"_source": {
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
},
{
"_index": "test",
"_type": "_doc",
"_id": "2",
"_score": 0.6931472,
"_source": {
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
}
]
}
NOTE: In the query I have also used explain:true. This gives the explanation of the score calculation.(I have not included the explain output above as it is very long. You can try it though).
When we look at the scoring mechanism, we can see the difference while querying on nested field and when querying on an array. Broadly speaking since nested fields are stored as separate docs, Doc 1 is scored higher because the child doc 1 i.e. :
{
"name": "Paul Tompkins"
}
will have higher score because both the terms paul and tompkins are in the same child doc.
In case of array all the names belong same field and not as separate child docs and hence the difference.
This way we can achieve the desired result.
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
The reason both of the docs have same score is because the author field is array of text values. If we change the way we store authors we can get the desired result. To do so lets make authors as a nested type. So we have the following mapping:
"mappings": {
"_doc": {
"properties": {
"authors": {
"type": "nested",
"properties": {
"name": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
}
}
Note: Sub field raw can be used for some other scenarios and has no relation to the solution.
Now lets index the docs as below:
Doc 1:
{
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
Doc 2:
{
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
Lets query them as follows:
{
"explain": true,
"query": {
"nested": {
"path": "authors",
"query": {
"query_string": {
"query": "paul l tompkins",
"fields": [
"authors.name"
]
}
}
}
}
}
Result:
"hits": {
"total": 2,
"max_score": 1.3862944,
"hits": [
{
"_index": "test",
"_type": "_doc",
"_id": "1",
"_score": 1.3862944,
"_source": {
"authors": [
{
"name": "Paul Tompkins"
},
{
"name": "Dietrich Kohl"
}
]
}
},
{
"_index": "test",
"_type": "_doc",
"_id": "2",
"_score": 0.6931472,
"_source": {
"authors": [
{
"name": "Paul Wang"
},
{
"name": "Darlene Tompkins"
}
]
}
}
]
}
NOTE: In the query I have also used explain:true. This gives the explanation of the score calculation.(I have not included the explain output above as it is very long. You can try it though).
When we look at the scoring mechanism, we can see the difference while querying on nested field and when querying on an array. Broadly speaking since nested fields are stored as separate docs, Doc 1 is scored higher because the child doc 1 i.e. :
{
"name": "Paul Tompkins"
}
will have higher score because both the terms paul and tompkins are in the same child doc.
In case of array all the names belong same field and not as separate child docs and hence the difference.
This way we can achieve the desired result.
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
edited Nov 23 '18 at 6:23
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
answered Nov 23 '18 at 6:14
Nishant SainiNishant Saini
1,152818
1,152818
add a comment |
add a comment |
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