Introduction to the Minitrack on AI, Machine Learning, IoT, and Analytics: Exploring the Implications for Knowledge Management and Innovation

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

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Machine Learning and Lightweight Semantics to Improve Enterprise Search and Knowledge Management

Context and Semantics for Knowledge Management ◽

10.1007/978-3-642-19510-5_9 ◽

2011 ◽

pp. 171-188 ◽

Cited By ~ 1

Author(s):

Rayid Ghani ◽

Divna Djordjevic ◽

Chad Cumby

Keyword(s):

Machine Learning ◽

Knowledge Management ◽

Enterprise Search

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The World Bank: Knowledge Management and Machine Learning

The KM Cookbook ◽

10.29085/9781783304332.014 ◽

2019 ◽

pp. 77-88

Keyword(s):

Machine Learning ◽

Knowledge Management ◽

World Bank ◽

The World Bank ◽

The World

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Towards Artificial Intelligence. Sociological Reflections on the Relationship Man - Organization - Device

System Safety Human - Technical Facility - Environment ◽

10.2478/czoto-2019-0116 ◽

2019 ◽

Vol 1 (1) ◽

pp. 912-920

Author(s):

Małgorzata Suchacka ◽

Nicole Horáková

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Knowledge Management ◽

Learning Process ◽

Social Dimensions ◽

Human Organization ◽

Interdisciplinary Nature ◽

The Relationship

AbstractThe main goal of the study will be to pay attention to technologization of the learning process and its social dimensions in the context of artificial intelligence. The reflection will mainly cover selected theories of learning and knowledge management in the organization and its broadly understood environment. Considering the sociological dimensions of these phenomena is supposed to lead to the emphasis on the importance of the security of the human-organization-device relationship. Due to the interdisciplinary nature of the issue, the article will include references to the concept of artificial intelligence and machine learning. Difficult questions will arise around the ideas and will become the conclusion of the considerations.

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The influence of machine learning-based knowledge management model on enterprise organizational capability innovation and industrial development

PLoS ONE ◽

10.1371/journal.pone.0242253 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242253

Author(s):

Zhigang Zhou ◽

Yanyan Liu ◽

Hao Yu ◽

Lihua Ren

Keyword(s):

Machine Learning ◽

Data Mining ◽

Knowledge Management ◽

Knowledge Acquisition ◽

Industrial Development ◽

Management Model ◽

Data Mining Algorithm ◽

Organizational Capability ◽

Positive Correlation ◽

Engineering Cost

The aims are to explore the construction of the knowledge management model for engineering cost consulting enterprises, and to expand the application of data mining techniques and machine learning methods in constructing knowledge management model. Through a questionnaire survey, the construction of the knowledge management model of construction-related enterprises and engineering cost consulting enterprises is discussed. First, through the analysis and discussion of ontology-based data mining (OBDM) algorithm and association analysis (Apriori) algorithm, a data mining algorithm (ML-AR algorithm) on account of ontology-based multilayer association and machine learning is proposed. The performance of the various algorithms is compared and analyzed. Second, based on the knowledge management level, analysis and statistics are conducted on the levels of knowledge acquisition, sharing, storage, and innovation. Finally, according to the foregoing, the knowledge management model based on engineering cost consulting enterprises is built and analyzed. The results show that the reliability coefficient of this questionnaire is above 0.8, and the average extracted value is above 0.7, verifying excellent reliability and validity. The efficiency of the ML-AR algorithm at both the number of transactions and the support level is better than the other two algorithms, which is expected to be applied to the enterprise knowledge management model. There is a positive correlation between each level of knowledge management; among them, the positive correlation between knowledge acquisition and knowledge sharing is the strongest. The enterprise knowledge management model has a positive impact on promoting organizational innovation capability and industrial development. The research work provides a direction for the development of enterprise knowledge management and the improvement of innovation ability.

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Using Knowledge Management and Machine Learning to Identify Victims of Human Sex Trafficking

Knowledge Management, Innovation, and Entrepreneurship in a Changing World - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-7998-2355-1.ch014 ◽

2020 ◽

pp. 360-389

Author(s):

Jessica Whitney ◽

Marisa Hultgren ◽

Murray Eugene Jennex ◽

Aaron Elkins ◽

Eric Frost

Keyword(s):

Machine Learning ◽

Social Media ◽

Knowledge Management ◽

Action Research ◽

Text Mining ◽

Research Methodology ◽

Sex Trafficking ◽

System Development ◽

Management Approach ◽

Unexpected Outcome

Social media and the interactive Web have enabled human traffickers to lure victims and then sell them faster and in greater safety than ever before. However, these same tools have also enabled investigators in their search for victims and criminals. Authors used system development action research methodology to create and apply a prototype designed to identify victims of human sex trafficking by analyzing online ads. The prototype used a knowledge management approach of generating actionable intelligence by applying a set of strong filters based on an ontology to identify potential victims. Authors used the prototype to analyze a dataset generated from online ads from southern California and used the results of this process to generate a revised prototype that included the use of machine learning and text mining enhancements. An unexpected outcome of the second dataset was the discovery of the use of emojis in an expanded ontology.

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