Knowledge Recommendation Systems with Machine Intelligence Algorithms : People and Innovations / Jaroslaw Protasiewicz.

Format:

Book

Author/Creator:

Protasiewicz, Jaroslaw, author.

Series:

Studies in Computational Intelligence Series ; Volume 1101.

Studies in Computational Intelligence Series ; Volume 1101

Language:

English

Subjects (All):

Artificial intelligence.

Computer algorithms.

Physical Description:

1 online resource (139 pages)

Edition:

First edition.

Place of Publication:

Cham, Switzerland : Springer, [2023]

Summary:

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.

Contents:

Intro

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

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

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

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

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

7 Conclusions

7.1 Knowledge Recommendation

7.2 Novelty and Originality

7.3 Further Development.

Notes:

Includes bibliographical references.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Protasiewicz, Jarosław Knowledge Recommendation Systems with Machine Intelligence Algorithms

ISBN:

3-031-32696-2

OCLC:

1401763072