Knowledge Recommendation Systems with Machine Intelligence Algorithms : People and Innovations

Foreword
Acknowledgements
Contents
Acronyms
1 Introduction
	1.1 Why Knowledge Recommendation is Needed
	1.2 What is Knowledge Recommendation?
	1.3 The Road Map of this Book
	References
2 Literature Review
	2.1 A Quantitative Analysis of Knowledge Recommendation
	2.2 Support for the Selection of Reviewers and Experts
	2.3 Support for Innovation
	2.4 Selected Algorithms
	2.5 Summary
	References
3 Recommending Reviewers and Experts
	3.1 Reviewing Problems
		3.1.1 The Purpose of Reviewing
		3.1.2 Reviewing Methods
		3.1.3 Disruptions to the Reviewing Process
		3.1.4 Why Automate the Selection of Reviewers?
		3.1.5 Assumptions of the Recommendation System
	3.2 The IT Reviewer and Expert Recommendation System
		3.2.1 System Architecture and System Processes
		3.2.2 Data Acquisition Module
		3.2.3 Knowledge Retrieval Module
		3.2.4 Recommendation Module
	3.3 Recommendation Algorithm
		3.3.1 Keywords' Cosine Similarity
		3.3.2 A Full-text Index
		3.3.3 The Combination of Two Measures
	3.4 Validation of the Recommendation System
		3.4.1 A Simple Example of the Recommendation Algorithm
		3.4.2 Implementation of the Complete Algorithm
	3.5 Summary
	References
4 Supporting Innovativeness  and Information Sharing
	4.1 Innovativeness
		4.1.1 Innovation
		4.1.2 Open Innovation and Innovativeness Strategies
		4.1.3 An IT System to Support Innovativeness
	4.2 A System that Supports Innovativeness
		4.2.1 An Outline of the System
		4.2.2 Data Acquisition and Information Extraction
		4.2.3 Recommendations
		4.2.4 Recommendations in Practice
		4.2.5 Recommendations Distribution
	4.3 Summary
	References
5 Selected Algorithmic Developments
	5.1 Data Extraction and Crawling
		5.1.1 The Data Extraction Algorithm
		5.1.2 The Crawling Algorithm
		5.1.3 Data Acquisition in Practice
	5.2 Classification of Publications
		5.2.1 Problem Definition
		5.2.2 Classification Algorithms and Procedures
		5.2.3 Flat Versus Hierarchical Classification
		5.2.4 Monolingual Versus Multilingual Classification
		5.2.5 Classification of Publications in Practice
	5.3 Disambiguation of Authors
		5.3.1 Disambiguation Framework
		5.3.2 A Rule-Based Algorithm
		5.3.3 Clustering by Using Heuristic Similarity
		5.3.4 Clustering Using Similarity Estimated by Classifiers
		5.3.5 Disambiguation of Authors in Practice
	5.4 Keyword Extraction
		5.4.1 Polish Keyword Extractor
		5.4.2 Keyword Extraction in Practice
	5.5 Evaluation of Enterprises' Innovativeness
		5.5.1 A Model of Evaluation of Enterprises' Innovativeness
		5.5.2 Model Evaluation
	5.6 Summary
	References
6 Knowledge Recommendation in Practice
	6.1 The Reviewer and Expert Recommendation System
		6.1.1 System Architecture and Technology
		6.1.2 System User Interfaces
		6.1.3 Selected Statistics
	6.2 Inventorum, the Innovation Support System
		6.2.1 System Architecture and Technology
		6.2.2 System User Interfaces
		6.2.3 Selected Statistics
	6.3 Summary
	References
7 Conclusions
	7.1 Knowledge Recommendation
	7.2 Novelty and Originality
	7.3 Further Development