不爲也 比不能也

We’ve mentioned that, by default, results are returned in descending order of relevance. But what is relevance? How is it calculated?기본적으로 결과는 relevance를 기준으로, 내림차순으로 반환된다고 언급한 바 있다. 그런데, relevance가 무엇이고, 어떻게 계산되는가?The relevance score of each document is represented by a positive floating-point number called the _score. The higher the _score, the more relevant the document.각 document의 relevance ..

Now that we have covered the simple case of searching for structured data, it is time to explore full-text search: how to search within full-text fields in order to find the most relevant documents.지금까지 구조화된 데이터를 위한, 간단한 검색을 살펴봤다. 이제 full-text 검색(full-text search) 을 탐험할 시간이다. 가장 적합한 document를 찾기 위해, full-text field를 검색하는 방법을 알아 보자.The two most important aspects of full-text search are as follows..

Now we come to a common pattern: cross-fields entity search. With entities like person, product, or address, the identifying information is spread across several fields. We may have a person indexed as follows:이제 일반적인 패턴(여러 field에 대한 항목 검색 - cross fields entity search)을 시작해 보자. 사람(person), 제품(product), 주소(address) 같은 항목의 식별 정보는, 여러 field에 걸쳐 저장한다. 특정인의 정보는 아래처럼 색인될 것이다.{ "firstname": "Peter", "l..

All three of the preceding problems stem from most_fields being field-centric rather than term-centric: it looks for the most matching fields, when really what we’re interested in is the most matching terms.위의 세 가지 문제점 모두는, most_fields 가 단어 중심(term-centric) 이라기 보다는, field 중심(field-centric) 이라는 사실에서 기인한다. 실제로 관심을 가지는 것은 대부분이 일치하는 단어(terms) 인데, 대부분이 일치하는 fields 를 검색한다.The best_fields type is also ..

Lucene (and thus Elasticsearch) uses the Boolean model to find matching documents, and a formula called the practical scoring function to calculate relevance. This formula borrows concepts from term frequency/inverse document frequency and the vector space model but adds more-modern features like a coordination factor, field length normalization, and term or query clause boosting.Lucene과 Elastic..

For multiterm queries, Lucene takes the Boolean model, TF/IDF, and the vector space model and combines them in a single efficient package that collects matching documents and scores them as it goes.다중 단어 query의 경우, Lucene은 Boolean model, TF/IDF 그리고, vector space model을 가지고, 일치하는 document를 수집하고, score를 계산하는, 하나의 효율적인 패키지로 그들을 조합한다.A multiterm query like다중 단어 query는GET /my_index/doc/_search { "que..

Sometimes we just don’t care about TF/IDF. All we want to know is that a certain word appears in a field. Perhaps we are searching for a vacation home and we want to find houses that have as many of these features as possible:TF/IDF에 관계없이, 어떤 field에 특정 단어가 나타나는지 알고 싶을 경우가 있다. 휴가용 별장을 검색하고 있고, 가능한 한 많은 기능(wifi, 정원, 풀장 등)을 가진 집을 찾고 있다면 말이다.WiFiGardenPoolThe vacation home documents look something l..

Before we move on from relevance and scoring, we will finish this chapter with a more advanced subject: pluggable similarity algorithms. While Elasticsearch uses the Lucene’s Practical Scoring Function as its default similarity algorithm, it supports other algorithms out of the box, which are listed in the Similarity Modules documentation.relevance와 score 계산에 대한 설명을 마치기 전에, 이 장에서, 더 고급 주제인 이용 가능..