![]() Inversed Document Frequency is used to select the best field for a term rather than the most important term in a multi term query. Let’s assume all other factors(term frequency, average document length,…) are a tie, Document Frequency will be the discriminant in deciding the best field match for each candidate document that needs to be ranked.Īnd currently, it works the opposite a user would intuitively expect: comics issues where batman was the villain of the story will sky-rocket to the top. using machine learning principles, fuzzy matching, and more. New search capabilities: Solr 3.4 includes improved query support, including the ability to sort by function queries, as well as improved analysis and new input. So the score of each document will be the highest score among the potential heroes:batman match and the potential villains:batman match. It gathered around 600 Lucene and Solr enthusiasts from 26 countries, including many of the. This results in a disjunction query: (heroes:batman | villains:batman) So the rarer in the corpus a query term is, the more important is considered for the overall query.īut using it in Disjunctions queries brings a nasty effect:ĭocument Frequency is used to select the best field that matches a term (even a single term).Īnd it does that in a counter-intuitive way: the field where the term appears the less is considered the best. ![]() This algorithm used the document frequency of a term to basically estimate how important a term is among the other query terms. īM25 is the scoring algorithm used by Apache Solr and Lucene. What does currently affect which field scores best? BM25 scoring. if you want to search for multiple values at the same time in fields keyword tokenized.Sow=false only one field contributes to the score (the one that scores best) The coord factor was used to normalize the contribution of each boolean clause to 1/n where n was the number of query terms involved. You should prefer this strategy if generally, you prefer documents that match more query terms to be favored.ĭoes this mean that having more terms will always win over fewer terms? NO, not necessarilyīecause a match of one field in a super rare field, may still dominate because there’s no coord factor anymore Sow=true each term contributes to the score once (if it matches twice, the field that scores best is taken) if your text analysis produces from the input text a different amount of tokens per field, the difference kicks in: if you want documents containing more query terms(not necessarily in the same field) to score generally higher you should use sow=true.createQuery () Copy In step 3, we'll wrap the Lucene query into a Hibernate query:. If your text analysis produces for all query fields, the exact same amount of tokens, you won’t see any difference in setting sow=true or sow=false In step 2, we will create a Lucene query via the Hibernate query DSL: . The default Solr query syntax used to search an index uses a superset of the Lucene query syntax.The following considerations assume the tie parameter is set to the default 0.įurther considerations on the topic follow. onField("productName").Given the evident advantages of setting sow=false, why does this parameter exists? i.e. onField("description").sentence("face id") .FuzzyQuery public class FuzzyQuery extends MultiTermQuery Implements the fuzzy search query. onField("productName").matching("apple") However, if both queries match, the match will have a higher relevance compared to if only one query matches: Query combinedQuery = queryBuilder ![]() However, the names are different to emphasize that they also have an impact on the relevance.įor example, a SHOULD between two queries is similar to boolean OR: if one of the two queries has a match, this match will be returned. The aggregations are similar to the boolean ones AND, OR and NOT.
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