1
我想用Elasticsearch为中文实现一个简单的演示。 但是，搜索结果的相关性存在一些问题。关于Elasticsearch关于搜索结果的相关问题
我创建的映射一个新的指标：
{
"tag": {
"mappings": {
"tag": {
"properties": {
"name": {
"type": "text",
"analyzer": "standard"
},
"note": {
"type": "text",
"analyzer": "standard"
},
"status": {
"type": "integer"
},
"synonyms": {
"type": "text",
"analyzer": "standard"
}
}
}
}
}
}
和请求机身采用了查询 “美国”：
{
"query" : {
"bool" : {
"must" : {
"multi_match" : {
"query" : "美国",
"fields" : [ "name", "synonyms" ]
}
},
"filter" : {
"term" : {
"status" : 2
}
}
}
}
}
有两条记录 “中国” 和 “美国” 匹配查询。但是“中国”的记录得分更高。响应JSON是以下几点：
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0.7373906,
"hits": [ {
"_index": "tag",
"_type": "tag",
"_id": "5482361185636870",
"_score": 0.7373906,
"_source": {
"status": 2,
"name": "中国",
"note": "",
"synonyms": []
}
}, {
"_index": "tag",
"_type": "tag",
"_id": "5474649504748034",
"_score": 0.53484553,
"_source": {
"status": 2,
"name": "美国",
"note": "",
"synonyms": []
}
} ]
}
}
“中国”的记录了0.7373906，但“美国”的记录只得到了0.53484553。
的结果解释：
{
"hits": [
{
"_shard": "[tag][0]",
"_node": "Wh9qH0bcTAaVNrsP1Aiyxg",
"_index": "tag",
"_type": "tag",
"_id": "5482361185636870",
"_score": 0.7373906,
"_source": {
"status": 2,
"name": "中国",
"note": "",
"synonyms": []
},
"_explanation": {
"value": 0.73739064,
"description": "sum of:",
"details": [
{
"value": 0.73739064,
"description": "sum of:",
"details": [
{
"value": 0.73739064,
"description": "max of:",
"details": [
{
"value": 0.73739064,
"description": "sum of:",
"details": [
{
"value": 0.73739064,
"description": "weight(name:国 in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.73739064,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.6931472,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5)/(docFreq + 0.5)) from:",
"details": [
{
"value": 1,
"description": "docFreq",
"details": []
},
{
"value": 2,
"description": "docCount",
"details": []
}
]
},
{
"value": 1.0638298,
"description": "tfNorm, computed as (freq * (k1 + 1))/(freq + k1 * (1 - b + b * fieldLength/avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3,
"description": "avgFieldLength",
"details": []
},
{
"value": 2.56,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
]
},
{
"value": 0,
"description": "match on required clause, product of:",
"details": [
{
"value": 0,
"description": "# clause",
"details": []
},
{
"value": 1,
"description": "status:[2 TO 2], product of:",
"details": [
{
"value": 1,
"description": "boost",
"details": []
},
{
"value": 1,
"description": "queryNorm",
"details": []
}
]
}
]
}
]
},
{
"value": 0,
"description": "match on required clause, product of:",
"details": [
{
"value": 0,
"description": "# clause",
"details": []
},
{
"value": 1,
"description": "*:*, product of:",
"details": [
{
"value": 1,
"description": "boost",
"details": []
},
{
"value": 1,
"description": "queryNorm",
"details": []
}
]
}
]
}
]
}
},
{
"_shard": "[tag][4]",
"_node": "Wh9qH0bcTAaVNrsP1Aiyxg",
"_index": "tag",
"_type": "tag",
"_id": "5474649504748034",
"_score": 0.51623213,
"_source": {
"status": 2,
"name": "美国",
"note": "",
"synonyms": []
},
"_explanation": {
"value": 0.51623213,
"description": "sum of:",
"details": [
{
"value": 0.51623213,
"description": "sum of:",
"details": [
{
"value": 0.51623213,
"description": "max of:",
"details": [
{
"value": 0.51623213,
"description": "sum of:",
"details": [
{
"value": 0.25811607,
"description": "weight(name:美 in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.25811607,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.2876821,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5)/(docFreq + 0.5)) from:",
"details": [
{
"value": 1,
"description": "docFreq",
"details": []
},
{
"value": 1,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.89722675,
"description": "tfNorm, computed as (freq * (k1 + 1))/(freq + k1 * (1 - b + b * fieldLength/avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 2,
"description": "avgFieldLength",
"details": []
},
{
"value": 2.56,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
},
{
"value": 0.25811607,
"description": "weight(name:国 in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.25811607,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.2876821,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5)/(docFreq + 0.5)) from:",
"details": [
{
"value": 1,
"description": "docFreq",
"details": []
},
{
"value": 1,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.89722675,
"description": "tfNorm, computed as (freq * (k1 + 1))/(freq + k1 * (1 - b + b * fieldLength/avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 2,
"description": "avgFieldLength",
"details": []
},
{
"value": 2.56,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
]
},
{
"value": 0,
"description": "match on required clause, product of:",
"details": [
{
"value": 0,
"description": "# clause",
"details": []
},
{
"value": 1,
"description": "status:[2 TO 2], product of:",
"details": [
{
"value": 1,
"description": "boost",
"details": []
},
{
"value": 1,
"description": "queryNorm",
"details": []
}
]
}
]
}
]
},
{
"value": 0,
"description": "match on required clause, product of:",
"details": [
{
"value": 0,
"description": "# clause",
"details": []
},
{
"value": 1,
"description": "*:*, product of:",
"details": [
{
"value": 1,
"description": "boost",
"details": []
},
{
"value": 1,
"description": "queryNorm",
"details": []
}
]
}
]
}
]
}
}
]
}
谢谢你看看。是否有另一种方法通过更改设置，映射或其他方式来解决此问题，因为我发现DFS查询然后提取可能会导致性能下降。 – LCB
如果您的索引很小，且不需要可扩展性，则可以使用单个分片创建索引。如果指数较大，则术语频率不会有显着差异，并且您通常不会遇到此问题 – Random