Apache lucene relevance models software#
It also provides features like highlighting, flexible faceting, and result grouping.Īpache Lucene an open source program by Apache Software Foundation. Some of the pluggable ranking models used by Apache Lucene are OkapiBM25 and Vector Space Model. It allows searching and updating simultaneously to provide the best results. To elevate accuracy in the search function, it has multiple-index search features with merges results. The option of field searching is available and you can start the search even by the name of author, title, and other search terms. In fact, to support the most accurate search, it features power queries including proximity queries, phrase queries, range queries and wild card queries. In terms of Algorithm, it has ranked highest with best results returning first. Also, its incremental indexing is faster than batch indexing.Ģ-Efficient and accurate search algorithms It also has a very small requirement of RAM, maximum 1MB. As the speed increases, so does the overall performance. It has great parameters to stand out from the crowd and efficiency is what, that makes it likable of all business players. It only takes sub-seconds to result a query and this makes it very effective solution for any organization. This benefit is due to its Java framework.
In terms of speed, there is none that can match Apache Lucene. There are many specific features of Apache Lucene let us discuss its 3 most powerful advantages: 1-Speed and high performance indexing Why is Lucene the number one searched technology? Some of them are CNET, APPLE, Linkedin, IBM, Monster, MySpace, etc. It also has great contributors, contributing from all around the world. Due to its high performance, scalability and relevancy, many bigshot organizations like IBM, AOL and Comcast Interactive Media bank upon it.Ĭurrently, it is the powerful tool, running successfully all over the world.
Apache lucene relevance models full#
The basic language behind this open source search technology is Java Script and it is all compatible with all the applications which require a full text search, especially, in cross-platform. If I use the BM25Similarity, the printout is as follows:Ġ.75188845 = weight(Synonym(morphology_term_original_name:neoplasm^0.7 morphology_term_original_name:tumor^0.8 morphology_term_original_name:tumour^0.6) in 0), result of:Ġ.75188845 = score(freq=0.8), computed as boost * idf * tf from:ġ.3862944 = idf, computed as log(1 + (N – n + 0.5) / (n + 0.5)) from:ġ = n, number of documents containing termĥ = N, total number of documents with fieldĠ.Apache Lucene – High Performance GuaranteedĪpache Lucene is a high performance search and information retrieval technology.
Query: +Synonym(morphology_term_original_name_key:neoplasm^0.7 morphology_term_original_name_key:tumor^0.8 morphology_term_original_name_key:tumour^0.6)ġ.0 = weight(Synonym(morphology_term_original_name:neoplasm^0.7 morphology_term_original_name:tumor^0.8 morphology_term_original_name:tumour^0.6) in 0), result of:ġ.0 = score(BooleanWeight), computed from: I’m using StandardAnalyzer for search, and my SynonymGraphFilter has default configuration as in your example. Here’s my debug output and some additional info: Org/apache/lucene/search/similarities/BM25Similarity.java:219īM25Scorer(float boost, float k1, float b, Explanation idf, float avgdl, float cache) can be changed after the index has been Now, from a very quick look to the Similarity classes, BM25Similarity has support for boosting : In Lucene you can get it using : .IndexSearcher#explain(.Query, int) Do you mind debugging the score and pasting here the output ?
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