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NetOwl

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NetOwl
IndustrySoftware
HeadquartersFalls Church, Virginia
ProductsNetOwl Extractor, NetOwl NameMatcher, NetOwl EntityMatcher, NetOwl TextMiner
Websitehttps://www.netowl.com/

NetOwl is a suite of multilingual text and identity analytics products that analyze big data in the form of text data – reports, web, social media, etc. – as well as structured entity data about people, organizations, places, and things.

NetOwl utilizes artificial intelligence (AI)-based approaches, including natural language processing (NLP), machine learning (ML), and computational linguistics, to extract entities, relationships, and events; to perform sentiment analysis; to assign latitude/longitude to geographical references in text; to translate names written in foreign languages; and to perform name matching and identity resolution.[1][2][3] NetOwl's uses include semantic search and discovery,[4] geospatial analysis,[5] intelligence analysis,[6] content enrichment,[7] compliance monitoring,[8] cyber threat monitoring,[9] risk management,[10] and bioinformatics.[11]

History

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The first NetOwl product was NetOwl Extractor, which was initially released in 1996.[12] Since then, Extractor has added many new capabilities, including relationship and event extraction, categorization, name translation, geotagging, and sentiment analysis, as well as entity extraction in other languages. Other products were added later to the NetOwl suite, namely TextMiner, NameMatcher, and EntityMatcher.

NetOwl has participated in several 3rd party-sponsored text and entity analytics software benchmarking events. NetOwl Extractor was the top-scoring named entity extraction system at the DARPA-sponsored Message Understanding Conference MUC-6 and the top-scoring link and event extraction system in MUC-7.[13][14] It was also the top-scoring system at several of the NIST-sponsored Automatic Content Extraction (ACE) evaluation tasks.[15] NetOwl NameMatcher was the top-scoring system at the MITRE Challenge for Multicultural Person Name Matching.[1]

Products

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The NetOwl suite includes, among others, the following text and entity analytics products:

Text Analytics

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NetOwl Extractor performs entity extraction from unstructured texts using natural language processing (NLP), machine learning (ML), and computational linguistics. Extractor also performs semantic relationship and event extraction as well as geotagging of text.[3][5] It is used for a variety of data sources including both traditional sources (e.g., news, reports, web pages, email) and social media (e.g., Twitter, Facebook, chats, blogs).[8] It runs on a variety of Big Data analytics platforms, including Apache Hadoop and LexisNexis’s High-Performance Computer Cluster (HPCC) technology.[7] It has been integrated with a number of 3rd party analytical tools such as Esri ArcGIS and Google Earth/Maps.[5]

Identity Analytics

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NetOwl NameMatcher and EntityMatcher perform name matching and identity resolution for large multicultural and multilingual entity databases using machine learning (ML) and computational linguistics approaches.[1][2] They are used for applications such as anti-money laundering (AML), watch lists, regulatory compliance, fraud detection, etc.

See also

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References

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  1. ^ a b c "SRA International." Washington Post. Retrieved 2013-07-02.
  2. ^ a b Zelenko, Dmitry, and Chinatsu Aone. “Discriminative Methods for Transliteration.” In Proceedings of 2006 Conference Empirical Applications of Natural Language Processing (2006). Retrieved 2013-05-20.
  3. ^ a b Maybury, Mark (2012). Multimedia Information Extraction, Hoboken, New Jersey: John Wiley & Sons, Inc., p. 18. Retrieved 2013-07-02.
  4. ^ Jackson, Peter; Moulinier, Isabelle (2002). Natural Language Processing for Online Applications: Text Retrieval, Extraction, and Categorization, Philadelphia: John Benjamins B.V., p. 117, ISBN 90-272-4989-X. Retrieved 2013-07-02.
  5. ^ a b c Smith, Susan. “Notes from the GEOINT 2007 Symposium.” GISCafe (2007-10-29). Retrieved 2013-07-02.
  6. ^ Indurkhya, Nitin et al. (2005). Text Mining: Predictive Methods for Analyzing Unstructured Information, New York: Springer, p. 154-155, ISBN 0-387-95433-3. Retrieved 2013-07-02.
  7. ^ a b Guess, Angela (2012-01-19). "LexisNexis Releases New Version of Lexis Advance". semanticweb.com. Retrieved 2013-07-28.
  8. ^ a b Aone, Chinatsu, et al. “Assentor®: an NLP-based Solution to E-mail Monitoring.” In Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (2000), pp. 945-540. Retrieved 2013-05-20.
  9. ^ "SRA’s Dusty Rhoads and Jim McClave: How to strengthen your company’s cybersecurity". ExecutiveBiz (2009-07-02). Retrieved 2013-08-06.
  10. ^ “Nasdaq on the prowl.” CNN Money (1997-09-17). Retrieved 2013-07-02.
  11. ^ Zaremba, Sam, et al. “Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens.” BMC Bioinformatics, 10:177 (2009-06-10). Retrieved 2013-05-20.
  12. ^ Hudgins-Bonafield, Christine. “Filtering Knowledge On The Net Just Got Simpler.” Archived 2003-03-30 at the Wayback Machine NetWork Computing (1996-05-31). Retrieved 2013-09-03.
  13. ^ Sundheim, Beth M. "Overview of Results of the MUC-6 Evaluation." Archived 2006-12-12 at the Wayback Machine In Proceedings of the 6th Conference on Message Understanding (1995), pp. 13-31. Retrieved 2013-05-20.
  14. ^ Aone, Chinatsu, et al. "SRA: Description Of The IE2 System Used For MUC-7." Archived 2006-12-09 at the Wayback Machine In Proceedings of the 7th Message Understanding Conference (1998). Retrieved 2013-05-20.
  15. ^ The ACE 2005 (ACE'05) Evaluation Plan. Retrieved 2013-05-20.
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