Doug Cutting originally wrote Lucene in 1999.[6] Lucene was his fifth search engine. He had previously written two while atXerox PARC, one atApple, and a fourth atExcite.[7] It was initially available for download from its home at theSourceForge web site. It joined the Apache Software Foundation'sJakarta family of open-source Java products in September 2001 and became its own top-level Apache project in February 2005. The name Lucene is Doug Cutting's wife's middle name and her maternal grandmother's first name.[8]
Lucene formerly included a number of sub-projects, such as Lucene.NET,Mahout,Tika andNutch. These three are now independent top-level projects.
In March 2010, theApache Solr search server joined as a Lucene sub-project, merging the developer communities.
While suitable for any application that requires full textindexing and searching capability, Lucene is recognized for its utility in the implementation ofInternet search engines and local, single-site searching.[10][11]
Lucene includes a feature to perform a fuzzy search based onedit distance.[12]
Lucene has also been used to implement recommendation systems.[13] For example, Lucene's 'MoreLikeThis' Class can generate recommendations for similar documents. In a comparison of the term vector-based similarity approach of 'MoreLikeThis' with citation-based document similarity measures, such asco-citation and co-citation proximity analysis, Lucene's approach excelled at recommending documents with very similar structural characteristics and more narrow relatedness.[14] In contrast, citation-based document similarity measures tended to be more suitable for recommending more broadly related documents,[14] meaning citation-based approaches may be more suitable for generatingserendipitous recommendations, as long as documents to be recommended contain in-text citations.
Lucene itself is just an indexing and search library and does not containcrawling and HTMLparsing functionality. However, several projects extend Lucene's capability:
^Kamphuis, Chris; de Vries, Arjen P.; Boytsov, Leonid; Lin, Jimmy (2020), "Which BM25 do You Mean? A Large-Scale Reproducibility Study of Scoring Variants", in Jose, Joemon M.; Yilmaz, Emine; Magalhães, João; Castells, Pablo (eds.),Advances in Information Retrieval, Lecture Notes in Computer Science, vol. 12036, Cham: Springer International Publishing, pp. 28–34,doi:10.1007/978-3-030-45442-5_4,ISBN978-3-030-45441-8,PMC7148026
^Grand, Adrien; Muir, Robert; Ferenczi, Jim; Lin, Jimmy (2020), "From MAXSCORE to Block-Max Wand: The Story of How Lucene Significantly Improved Query Evaluation Performance", in Jose, Joemon M.; Yilmaz, Emine; Magalhães, João; Castells, Pablo (eds.),Advances in Information Retrieval, Lecture Notes in Computer Science, vol. 12036, Cham: Springer International Publishing, pp. 20–27,doi:10.1007/978-3-030-45442-5_3,ISBN978-3-030-45441-8,PMC7148045
^J. Beel, S. Langer, and B. Gipp, “The Architecture and Datasets of Docear’s Research Paper Recommender System,” in Proceedings of the 3rd International Workshop on Mining Scientific Publications (WOSP 2014) at the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2014), London, UK, 2014
^abM. Schwarzer, M. Schubotz, N. Meuschke, C. Breitinger,V. Markl, and B. Gipp,https://www.gipp.com/wp-content/papercite-data/pdf/schwarzer2016.pdf "Evaluating Link-based Recommendations for Wikipedia" in Proceedings of the 16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), New York, NY, USA, 2016, pp. 191-200.
