Knowledge recommendation is an timely subject that is encountered frequently in research and information services. A compelling and urgent need exists for such systems: the modern economy is in dire need of highly-skilled professionals, researchers, and innovators, who create opportunities to gain competitive advantage and assist in the management of financial resources and available goods, as well as conducting fundamental and applied research more effectively.This book takes readers on a journey into the world of knowledge recommendation, and of systems of knowledge recommendation that use machine intelligence algorithms. It illustrates knowledge recommendation using two examples. The first is the recommendation of reviewers and experts who can evaluate manuscripts of academic articles, or of research and development project proposals. The second is innovation support, which involves bringing science and business together by recommending information that pertains to innovations, projects, prospective partners, experts, and conferences meaningfully.The book also describes the selection of the algorithms that transform data into information and then into knowledge, which is then used in the information systems. More specifically, recommendation and information extraction algorithms are used to acquire data, classify publications, identify (disambiguate) their authors, extract keywords, evaluate whether enterprises are innovative, and recommend knowledge.This book comprises original work and is unique in many ways. The systems and algorithms it presents are informed by contemporary solutions described in the literature - including many compelling, novel, and original aspects. The new and promising directions the book presents, as well as the techniques of machine learning applied to knowledge recommendation, are all original.

Presents in a systematic and prudently designed way the main topics of intensive knowledge-based technology Offers authoritative treatment on the area of knowledge recommendation and systems of knowledge recommendation Provides recent research on knowledge recommendation systems with machine intelligence algorithms

Autorentext

The education and career of Jaros aw Protasiewicz as an experienced researcher, lecturer, and IT professional are connected deeply with computer science and artificial intelligence. Jaros aw acquired his master's degree at the Biäystok Technical University, Poland, by presenting his thesis, The detection of changes in the parameters of a mathematical model of a physical object using neural networks. He later defended his doctoral dissertation, The use of neural networks for the analysis of the power market in Poland, at the Systems Research Institute of the Polish Academy of Sciences. Both theses concerned artificial neural networks. Jaros aw's research interests include software design and development, artificial intelligence, and machine learning. His scientific career has long been interwoven with the IT industry. He has extensive IT experience as a software developer, designer, and project manager. Since 2005, Jaros aw has been employed by the National Information Processing Institute (OPI PIB) in Warsaw, Poland, where he initially served as a software developer and designer. Then, as an associate professor, he established and managed the Laboratory of Intelligent Information Systems-the largest laboratory of the institute. Currently, he serves as the head of OPI PIB. Jaros aw is also an experienced academic teacher who is responsible for lectures, laboratory classes, and supervision of students' final projects in software development and machine learning at the Warsaw School of Information Technology, Poland.

Inhalt

1.Introduction.- 2.Literature review.- 3.Recommending reviewers and experts.- 4.Supporting innovativeness and information sharing.- 5.Selected algorithmic developments.- 6.Knowledge recommendation in practice.- 7.Conclusions.