Machine Learning and Lightweight Semantics to Improve Enterprise Search and Knowledge Management
Enterprise search and knowledge management tools are amongst the most widely used tools within enterprises. Despite the breadth of tasks they are used for, and the large number of users they are used by, current tools supporting enterprise search are fairly generic and provide the same results to everyone. This chapter describes approaches using machine learning and lightweight semantics to make generic knowledge management tools context and task sensitive with the goal of increasing the productivity of knowledge workers. We focus on two key enterprise problems: (1) Enterprise search and (2) Collaborative Document Development. We describe our work on augmenting an enterprise search tool with context mining, process mining, and visualization technologies that use a combination of bag of words and lightweight semantic representations, making users more productive and efficient. We also describe our efforts on two enterprise tools to help improve the effectiveness and efficiency of knowledge workers collaboratively creating documents. All three of these were tested extensively at Accenture and two have been deployed and made available to over 150,000 Accenture employees. We present evaluation results using usage logs as well as questionnaires showing that these prototypes are effective at making consultants at Accenture more efficient as well as helping them find better information while being easier to use than existing enterprise tools.
KeywordsKnowledge Worker Graphic Search Document Development Graphic Classification Membership Score
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement IST-2007-215040.
- Cumby C, Ghani R (2011) A machine learning based system for semi-automatically redacting documents. Proceedings of the 23rd innovative applications of artificial intelligence conference, IAAI 2011, San Francisco, CAGoogle Scholar
- Cumby C, Probst K, Ghani R (2009) Retrieval and ranking of entities for enterprise knowledge management tasks. Semantic search workshop at WWW2009, Madrid, SpainGoogle Scholar
- Djordjevic D, Ghani R (2010) Graphics classification for enterprise knowledge management. ICDM workshops 2010, Sydney, Australia, pp 562–569Google Scholar
- Jansen BJ, Spink A (2006) How are we searching the world wide web? A comparison of nine search engine transaction logs. Inf Process Manage, 42(1). Formal methods for information retrieval, Jan 2006, pp 248–263Google Scholar
- Joachims T, Granka L, Pan B, Hembrooke H, Radlinski F, Gay G (2007) Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans Inf Syst 25(2), Article 7 (Apr 2007)Google Scholar
- Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University PressGoogle Scholar
- Mukherjee R, Mao J (2004) Enterprise search: tough stuff. Queue2(2) (Apr 2004)Google Scholar
- Settles B (2009) Active learning literature survey. Computer sciences technical report 1648, University of Wisconsin-MadisonGoogle Scholar
- Wang X, Zhai CX (2007) Learn from web search logs to organize search results. Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, Amsterdam, NetherlandsGoogle Scholar