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AI in Knowledge Management: Revolutionizing Access and Insight
Author: Dr. Evelyn Reed, PhD, Senior Research Scientist at the Institute for Information Science, specializing in AI applications in organizational knowledge management and data analytics. Dr. Reed has over 15 years of experience researching and implementing AI-driven solutions for Fortune 500 companies, focusing on knowledge discovery, retrieval, and dissemination.
Publisher: Springer Nature, a leading global research, educational, and professional publisher with a strong reputation for publishing high-quality content on artificial intelligence, data science, and management information systems. Their extensive network of experts and rigorous peer-review process ensures the accuracy and reliability of their publications.
Editor: Dr. Michael Davies, Professor of Information Systems at the University of California, Berkeley. Dr. Davies has authored several influential books on knowledge management and is a recognized authority in the field of organizational learning and technology. His expertise guarantees the article's theoretical and practical rigor.
Keywords: AI in knowledge management, knowledge management systems, AI-powered knowledge management, knowledge discovery, AI-driven knowledge retrieval, intelligent knowledge graphs, knowledge automation, AI and KM, machine learning in KM, deep learning in KM
1. Introduction: The Evolving Landscape of Knowledge Management
Knowledge management (KM) has evolved significantly since its inception. Initially focusing on manual processes like document sharing and expert interviews, KM now leverages technology to improve efficiency and effectiveness. The integration of Artificial Intelligence (AI) in knowledge management marks a pivotal shift, transforming how organizations create, store, access, and utilize their intellectual capital. This article delves into the historical context of AI in knowledge management, exploring its current applications and future potential.
2. Historical Context: From Expert Systems to Advanced Analytics
The early applications of AI in KM date back to the 1980s with the emergence of expert systems. These systems aimed to codify the knowledge of human experts into rule-based programs to automate decision-making. While limited by their rigid structure and reliance on explicit knowledge, expert systems laid the groundwork for future advancements.
The advent of the internet and the proliferation of digital data created new opportunities for AI in knowledge management. The increasing availability of computational power and sophisticated algorithms fueled the development of machine learning (ML) and deep learning (DL) techniques, enabling more nuanced and adaptive knowledge management systems. These advancements allow for automatic content tagging, intelligent search functionalities, and personalized knowledge recommendations.
3. Current Relevance: AI-Powered Knowledge Management in Action
Today, AI is transforming multiple aspects of knowledge management:
3.1 Knowledge Discovery and Retrieval: AI algorithms, particularly natural language processing (NLP) and machine learning, are revolutionizing how organizations discover and retrieve relevant knowledge. These algorithms can analyze unstructured data (emails, documents, chat logs) to identify key insights, relationships, and patterns. Intelligent search engines, powered by AI, can understand the user's intent and deliver more accurate and context-relevant results.
3.2 Knowledge Organization and Representation: AI is instrumental in building intelligent knowledge graphs, which represent knowledge in a structured, interconnected way. These graphs enable more sophisticated knowledge retrieval and facilitate the discovery of hidden relationships between different pieces of information.
3.3 Knowledge Automation: AI-powered automation tools streamline various KM processes, such as content creation, categorization, and knowledge sharing. AI chatbots can answer employee queries, while automated workflows can expedite the process of onboarding new employees and disseminating critical information.
3.4 Personalized Knowledge Recommendations: AI algorithms can analyze user preferences and behavior to provide personalized recommendations for relevant knowledge resources. This personalized approach ensures that employees receive the most relevant information at the right time, improving efficiency and decision-making.
3.5 Predictive Analytics in KM: AI can analyze historical data to predict future knowledge needs and identify potential knowledge gaps within an organization. This predictive capability allows for proactive knowledge management, ensuring that organizations have the right knowledge at the right time to meet their strategic objectives.
4. Challenges and Considerations
Despite its significant benefits, the adoption of AI in knowledge management presents several challenges:
Data quality and bias: The effectiveness of AI algorithms depends heavily on the quality and completeness of the data. Biased data can lead to inaccurate or unfair outcomes.
Data security and privacy: Handling sensitive organizational knowledge requires robust security measures to protect against unauthorized access and data breaches.
Integration with existing systems: Integrating AI-powered KM tools with existing legacy systems can be complex and require significant investment.
Change management: Successfully implementing AI in KM necessitates a cultural shift within the organization, requiring employees to adapt to new ways of working.
Ethical considerations: The use of AI in KM raises ethical considerations related to transparency, accountability, and potential biases.
5. Future Trends in AI-Driven Knowledge Management
The future of AI in knowledge management looks promising. We can expect advancements in:
Explainable AI (XAI): Making AI decision-making processes more transparent and understandable.
Federated learning: Enabling organizations to collaborate on knowledge management initiatives without sharing sensitive data.
Hyperautomation: Automating even more complex knowledge management tasks.
Enhanced human-AI collaboration: Creating more seamless interactions between humans and AI systems in the knowledge management process.
6. Conclusion
AI is revolutionizing knowledge management, enabling organizations to unlock the full potential of their intellectual capital. By addressing the challenges and embracing the opportunities, organizations can leverage AI to create more efficient, effective, and insightful knowledge management systems. The integration of AI in knowledge management is not merely a technological upgrade; it’s a fundamental shift towards a more data-driven and intelligent approach to managing organizational knowledge. The future of work relies heavily on harnessing the power of AI to effectively manage and utilize knowledge, leading to significant competitive advantages and improved organizational performance.
FAQs
1. What are the key benefits of using AI in knowledge management? AI improves knowledge discovery, retrieval, organization, automation, personalization, and predictive capabilities, leading to enhanced efficiency, better decision-making, and improved employee productivity.
2. What are the different types of AI used in knowledge management? NLP, machine learning, deep learning, and knowledge graph technologies are commonly used.
3. How can organizations overcome the challenges of implementing AI in knowledge management? Through careful data preparation, robust security measures, phased implementation, and comprehensive employee training.
4. What is the role of human expertise in AI-driven knowledge management? Human expertise remains crucial for oversight, validation, ethical considerations, and complex problem-solving that AI may not fully handle.
5. What are the ethical implications of using AI in knowledge management? Concerns include data bias, privacy, transparency, and potential job displacement; careful consideration and mitigation strategies are necessary.
6. How can organizations measure the ROI of AI in knowledge management? By tracking key metrics like knowledge retrieval time, employee satisfaction, decision-making speed, and the reduction in knowledge silos.
7. What are some examples of AI-powered knowledge management tools? Many vendors offer solutions, including those specializing in intelligent search, knowledge graphs, and chatbot integrations.
8. What are the future trends in AI and knowledge management? Expect advancements in explainable AI (XAI), federated learning, hyperautomation, and enhanced human-AI collaboration.
9. How can small and medium-sized enterprises (SMEs) benefit from AI in knowledge management? SMEs can leverage cloud-based AI solutions and focus on specific areas where AI can provide the greatest value, such as improving customer service or streamlining internal processes.
Related Articles:
1. "AI-Powered Knowledge Graphs: Transforming Enterprise Knowledge Management": This article explores the architecture and applications of knowledge graphs in enterprise settings, highlighting the role of AI in their creation and utilization.
2. "Natural Language Processing (NLP) for Knowledge Management: Extracting Insights from Unstructured Data": This piece focuses on how NLP techniques can be used to analyze textual data and extract valuable insights for KM purposes.
3. "The Ethical Implications of AI in Knowledge Management: Ensuring Fairness and Transparency": This article discusses the ethical considerations surrounding the use of AI in KM, such as bias, privacy, and accountability.
4. "Machine Learning Algorithms for Intelligent Knowledge Retrieval: A Comparative Analysis": This article compares different machine learning algorithms used for improving knowledge retrieval efficiency and accuracy.
5. "Building an AI-Driven Knowledge Management System: A Step-by-Step Guide": This offers a practical guide for organizations looking to implement AI-powered KM solutions.
6. "The Future of Work and AI-Enhanced Knowledge Management": This article examines the impact of AI on the future of work and its implications for knowledge management.
7. "AI and Knowledge Sharing: Fostering Collaboration and Innovation": This article explores how AI can facilitate knowledge sharing and collaboration within organizations.
8. "Case Study: Implementing AI in Knowledge Management at a Fortune 500 Company": A real-world example of AI implementation in a large organization, showcasing its benefits and challenges.
9. "Overcoming Barriers to AI Adoption in Knowledge Management: A Practical Approach": This article provides strategies and best practices for overcoming common challenges associated with integrating AI into KM initiatives.
| ai in knowledge management: Knowledge Management Irma Becerra-Fernandez, Rajiv Sabherwal, Richard Kumi, 2024-02-23 Knowledge Management: Systems and Processes in the AI Era, Third Edition, is aimed at students and managers who seek detailed insights into contemporary knowledge management (KM). It explains the concepts, theories, and technologies that provide the foundation for knowledge management; the systems and structures that constitute KM solutions; and the processes for developing, deploying, and evaluating these KM solutions. This book serves as a complete introduction to the subject of knowledge management, incorporating technical and social aspects, as well as concepts, practical examples, traditional KM approaches, and emerging topics. This third edition has been revised and expanded to include more coverage of emergent trends such as cloud computing, online communities, crowdsourcing, and artificial intelligence. Aimed at advanced undergraduate, postgraduate, and MBA students who are seeking a comprehensive perspective on knowledge management, Knowledge Management is also complemented by online support for lecturers including suggested solutions to the many review questions and application exercises contained within the book. |
| ai in knowledge management: AI-empowered Knowledge Management Soumi Majumder, Nilanjan Dey, 2022-02-23 This book is focused on AI-empowered knowledge management to improve processes, implementation of technology for providing easy access to knowledge and the impact of knowledge management to promote the platform for generation of new knowledge through continuous learning. The book discusses process of knowledge management which includes entirety of the creation, distribution, and maintenance of knowledge to achieve organizational objectives. It also covers knowledge management tools which enable and enhance knowledge creation, codification, and transfer within business firms thereby reducing the burden of work and allowing application of resources and effective usage towards practical tasks. An immense growth of artificial intelligence in business organizations has occurred and AI-empowered knowledge management practice is leading towards growth and development of the organization. |
| ai in knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, M. Özgür Kayalica, Mieczyslaw Lech Owoc, 2021-07-03 This book features a selection of extended papers presented at the 8th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021, held in Yokohama, Japan, in January 2021, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2020.* The 14 revised and extended papers presented together with an invited talk were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and discuss methodological, technical and organizational aspects of artificial intelligence used for knowledge management. *The workshop was held virtually. |
| ai in knowledge management: Artificial Intelligence and Knowledge Management Akira Hanako, 2016-05-24 Artificial intelligence and knowledge management have transformed the process of knowledge circulation and database management in different business enterprises and corporate organizations. Some of the significant topics discussed in the chapters of this book are AI planning strategies and tools, AI tools for information processing, data mining, knowledge-based systems, etc. It explores the innovative concepts and advancements in these emerging fields. The book is an invaluable source of knowledge for students and researchers involved in this field at various levels. |
| ai in knowledge management: Digital Technology Advancements in Knowledge Management Gyamfi, Albert, Williams, Idongesit, 2021-06-18 Knowledge management has always been about the process of creating, sharing, using, and applying knowledge within and between organizations. Before the advent of information systems, knowledge management processes were manual or offline. However, the emergence and eventual evolution of information systems created the possibility for the gradual but slow automation of knowledge management processes. These digital technologies enable data capture, data storage, data mining, data analytics, and data visualization. The value provided by such technologies is enhanced and distributed to organizations as well as customers using the digital technologies that enable interconnectivity. Today, the fine line between the technologies enabling the technology-driven external pressures and data-driven internal organizational pressures is blurred. Therefore, how technologies are combined to facilitate knowledge management processes is becoming less standardized. This results in the question of how the current advancement in digital technologies affects knowledge management processes both within and outside organizations. Digital Technology Advancements in Knowledge Management addresses how various new and emerging digital technologies can support knowledge management processes within organizations or outside organizations. Case studies and practical tips based on research on the emerging possibilities for knowledge management using these technologies is discussed within the chapters of this book. It both builds on the available literature in the field of knowledge management while providing for further research opportunities in this dynamic field. This book highlights topics such as human-robot interaction, big data analytics, software development, keyword extraction, and artificial intelligence and is ideal for technology developers, academics, researchers, managers, practitioners, stakeholders, and students who are interested in the adoption and implementation of new digital technologies for knowledge creation, sharing, aggregation, and storage. |
| ai in knowledge management: Knowledge Management for Leadership and Communication Jon-Arild Johannessen, 2020-03-11 With the establishment of the innovation economy, the Fourth Industrial Revolution is becoming a reality. As this occurs, new forms of leadership arise, generated by the interaction between leadership functions and neurology. This innovative book asks the question: what are the key value creation processes in the innovation economy? |
| ai in knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Danielle Boulanger, 2019-09-11 This book features a selection of extended papers presented at the 5th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2017, held in Melbourne, VIC, Australia, in August 2017, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2017. The 11 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai in knowledge management: Artificial Intelligence for Knowledge Management Mieczysław Lech Owoc, Maciej Pondel, 2021-08-05 This book features a selection of extended papers presented at the 7th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2019, held in Macao, China, in August 2019, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2019. The 8 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai in knowledge management: AI-Empowered Knowledge Management in Education Sayan Chakraborty, |
| ai in knowledge management: Managerial Issues in Digital Transformation of Global Modern Corporations Esakki, Thangasamy, 2021-06-25 Efficient management of managerial tasks by capable managers is essential in order to grow and remain competitive in today’s global business market. On the other hand, digital transformation enables organizations to better compete with their global counterparts. In the process of digital transformation, many firms find it difficult to acquire qualified leadership with adequate knowledge and competence to drive success. Without integrating the dual edges of managerial competence and digital evolution, it is next to impossible for a firm to both survive and grow. Managerial Issues in Digital Transformation of Global Modern Corporations is a critical scholarly publication that examines current challenges in the digital transformation of modern business corporations from a managerial and leadership perspective. Featuring a wide range of topics such as digital transformation, marketing, and global business, this book is ideal for corporate executives, managers, IT specialists, entrepreneurs, business administrators, industry practitioners, academicians, researchers, policymakers, and students from various relevant disciplines that include economics, information and technology, business administration, management science, and commerce. |
| ai in knowledge management: Secure Knowledge Management In The Artificial Intelligence Era Ram Krishnan, H. Raghav Rao, Sanjay K. Sahay, Sagar Samtani, Ziming Zhao, 2022-02-22 This book constitutes the refereed proceedings of the 9th International Conference On Secure Knowledge Management In Artificial Intelligence Era, SKM 2021, held in San Antonio, TX, USA, in 2021. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 30 submissions. They were organized according to the following topical sections: intrusion and malware detection; secure knowledge management; deep learning for security; web and social network. |
| ai in knowledge management: Understanding, Implementing, and Evaluating Knowledge Management in Business Settings Merlo, Tereza Raquel, 2022-06-24 Although there are numerous publications in the field of knowledge management (KM), there are still gaps in the literature regarding the aspects of KM that reflect new technology adoption and a deeper analysis discussing the interlinked process between KM and data analytics in business process improvement. It is essential for business leaders to understand the role and responsibilities of leaders for the adoption and consolidation of a KM system that is effective and profitable. Understanding, Implementing, and Evaluating Knowledge Management in Business Settings provides a comprehensive approach to KM concepts and practices in corporations and business organizations. Covering topics such as information overload, knowledge sharing adoption, and collective wisdom, this premier reference source is a comprehensive and essential resource for business executives, managers, IT specialists and consultants, libraries, students, entrepreneurs, researchers, and academicians. |
| ai in knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, 2020-07-15 This book features a selection of extended papers presented at the 6th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2018, held in Stockholm, Sweden, in July 2018, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2018. The 11 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai in knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Danielle Boulanger, 2019 This book features a selection of extended papers presented at the 5th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2017, held in Melbourne, VIC, Australia, in August 2017, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2017. The 11 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai in knowledge management: Knowledge Management, Innovation, and Entrepreneurship in a Changing World Jennex, Murray Eugene, 2020-03-27 In today’s world of business, gaining an advantage of competitors is a focal point for organizations and a driving force in the economy. New practices are being studied and implemented constantly by rivaling companies. Many industries have begun putting emphasis on intensive knowledge practices, with the belief that implementing cutting-edge learning practices will fuel research and innovation within the company. Understanding this dynamic method of management is critical for managers and executives who wish to propel the success of their organizations. Knowledge Management, Innovation, and Entrepreneurship in a Changing World is a collection of pioneering research on the methods of gaining organizational advantages based on knowledge innovation and management. While highlighting topics including human-robot teaming, organizational learning, and e-collaboration, this book will explore the sustainable links between knowledge management influences and organizational capability. This book is ideally designed for managers, strategists, economists, policymakers, entrepreneurs, business professionals, researchers, students, and academics seeking research on recent trends in innovative economics and business technologies. |
| ai in knowledge management: Using AI for Knowledge Management and Business Process Reengineering Rose Gamble, 1998 |
| ai in knowledge management: Knowledge Management in Practice Anthony J. Rhem, 2016-08-19 This evidence-based book provides the framework and guidelines that professionals need for working with the contemporary explosion of data that is creating opportunities and challenges to all phases of our society and commerce. –Larry R. Medsker, Research Professor in Physics and Data Science, The George Washington University Knowledge Management in Practice is a resource on how knowledge management (KM) is implemented. It provides specific KM methods, tips, techniques, and best practices to gain competitive advantage and the most from investing in KM. It examines how KM is leveraged by first responders, the military, healthcare providers, insurance and financial services companies, legal firms, human resources departments, merger and acquisition (M&A) firms, and research institutions. Essential KM concepts are explored not only from a foundational perspective but also from a practical application. These concepts include capturing and codifying tacit and explicit knowledge, KM methods, information architecture, search, KM and social media, KM and Big Data, and the adoption of KM. Readers can visit the book’s companion website, KM Mentor (www.KMMentor.com), where they can access: Presentations by industry leaders on a variety of topics KM templates and instruction on executing KM strategy, performing knowledge transfer, and KM assessments and audits KM program and project implementation guidance Insights and reviews on KM tools Guidance on implementing and executing various KM Methods Specialized KM publications A private secure collaboration community for members to discuss ideas and get expert answers and advice |
| ai in knowledge management: A Research Agenda for Knowledge Management and Analytics Jay Liebowitz, 2021-01-29 Leveraging the knowledge gained from Knowledge Management and from the growing fields of Analytics and Artificial Intelligence (AI), this Research Agenda highlights the research gaps, issues, applications, challenges and opportunities related to Knowledge Management (KM). Exploring synergies between KM and emerging technologies, leading international scholars and practitioners examine KM from a multidisciplinary perspective, demonstrating the ways in which knowledge sharing worldwide can be enhanced in order to better society and improve organisational performance. |
| ai in knowledge management: UML for Developing Knowledge Management Systems Anthony J. Rhem, 2005-11-21 UML for Developing Knowledge Management Systems provides knowledge engineers the framework in which to identify types of knowledge and where this knowledge exists in an organization. It also shows ways in which to use a standard recognized notation to capture, or model, knowledge to be used in a knowledge management system (KMS). This volume |
| ai in knowledge management: Knowledge Management and AI in Society 5.0 Manlio Del Giudice, Veronica Scuotto, Armando Papa, 2023 Society 5.0 points toward a human-centred approach by the use of modern, advanced technologies and artificial intelligence. This book explores and offers an overview of knowledge management embraced in the current scenario of Society 5.0, shedding light on its importance in a society that is increasingly digital and interconnected. The book enhances current managerial and economic research by offering the human side of knowledge management (KM) intertwined with the use of artificial intelligences (AIs). Each chapter explores KM from different perspectives, including entrepreneurship, innovation, marketing, and strategy, in a theoretical and practical way. They include insights from both practitioners and scholars, enriched by practical tools that can be used during laboratories, workshops and tutorials. The book presents evidence on how to manage KM and develop new knowledge in different subjects, with the aim of overcoming conventional KM strategy and show how business and society are connected with power of subjective human knowledge creation. Offering both new insights, research and practical guidance, this book will appeal to academics and students of knowledge management as well as digital transformation practitioners looking for ways to transition their organizations from knowledge economy to digital economy-- |
| ai in knowledge management: Knowledge Management Herwig Rollett, 2012-12-06 A compact guide to knowledge management, this book makes the subject accessible without oversimplifying it. Organizational issues like strategy and culture are discussed in the context of typical knowledge management processes. The focus is always on pointing out all the issues that need to be taken into account in order to make knowledge management a success. The book then goes on to explore the role of information technology as an enabler of knowledge management relating various technologies to the knowledge management processes, showing the reader what can, and what cannot, be achieved through technology. Throughout the book, references to lessons learned from past projects underline the arguments. Managers will find this book a valuable guide for implementing their own initiatives, while researchers and system designers will find plenty of ideas for future work. |
| ai in knowledge management: Artificial Intelligence in Society OECD, 2019-06-11 The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises. |
| ai in knowledge management: The AI Advantage Thomas H. Davenport, 2019-08-06 Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review. |
| ai in knowledge management: AI-Empowered Knowledge Management in Education Sayan Chakraborty, Bitan Misra, Nilanjan Dey, 2024-06-22 This book explains basic ideas behind several methods used in artificial intelligence-based knowledge management techniques. It also shows how these techniques are applied in practical contexts in different education sectors. The book discusses AI-based knowledge management applications, AI-empowered knowledge management in primary and higher education, and technical and ethical challenges and opportunities. |
| ai in knowledge management: Artificial Intelligence in Management Andrzej Wodecki, 2020-11-27 Autonomous systems are on the frontiers of Artificial Intelligence (AI) research, and they are slowly finding their business applications. Driven mostly by Reinforcement Learning (RL) methods (one of the most difficult, but also the most promising modern AI algorithms), autonomous systems help create self-learning and self-optimising systems, ranging from simple game-playing agents to robots able to efficiently act in completely new environments. Based on in-depth study of more than 100 projects, Andrzej Wodecki explores RL as a key component of modern digital technologies, its real-life applications to activities in a value chain and the ways in which it impacts different industries. |
| ai in knowledge management: Knowledge Management and Virtual Organizations Yogesh Malhotra, 2000-01-01 Annotation Twenty essays present current research on knowledge management as related to effective design of new organization forms. The first section of the book covers frameworks, models, analyses, case studies and research on the integration of knowledge management within virtual organizations, virtual teams and virtual communities of practice. Themes covered in this section include business model innovation; design of virtual organization forms; net-based models; techniques for enabling knowledge capture, sharing and transfer; and collaboration and competition at intra- and inter-organizational levels. The focus of the second half is on key success factors that are important for realizing virtual models of business transformation. Topics include the role of organizational control systems, the role of internal and external employees and customers in creation of organizational knowledge, and information quality issues. Annotation c. Book News, Inc., Portland, OR (booknews.com). |
| ai in knowledge management: Handbook of Research on Artificial Intelligence and Knowledge Management in Asia’s Digital Economy Ordóñez de Pablos, Patricia, Zhang, Xi, Almunawar, Mohammad Nabil, 2022-11-11 Artificial intelligence (AI) and knowledge management can create innovative digital solutions and business opportunities in Asia from circular and green economies to technological disruption, innovation, and smart cities. It is essential to understand the impact and importance of AI and knowledge management within the digital economy for future development and for fostering the best practices within 21st century businesses. The Handbook of Research on Artificial Intelligence and Knowledge Management in Asia’s Digital Economy offers conceptual frameworks, empirical studies, and case studies that help to understand the latest developments in artificial intelligence and knowledge management, as well as its potential for digital transformation and business opportunities in Asia. Covering topics such as augmented reality. Convolutional neural networks, and digital transformation, this major reference work generates enriching debate on the challenges and opportunities for economic growth and inclusion in the region among business executives and leaders, IT managers, policymakers, government officials, students and educators of higher education, researchers, and academicians. |
| ai in knowledge management: Semantic Systems. The Power of AI and Knowledge Graphs Maribel Acosta, Philippe Cudré-Mauroux, Maria Maleshkova, Tassilo Pellegrini, Harald Sack, York Sure-Vetter, 2019-11-04 This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies. |
| ai in knowledge management: Knowledge Management Jay Liebowitz, 2001-03-28 Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acknowledge the power that KE methodologies, techniques, and tools hold for enh |
| ai in knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Danielle Boulanger, 2017-04-10 This book features a selection of papers presented at the Third IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2015, held in Buenos Aires, Argentina, in July 2015, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2015. The 9 revised and extended papers were carefully reviewed and selected from 15 submissions. They present new research and innovative aspects in the field of knowledge management such as knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai in knowledge management: Working Knowledge Thomas H. Davenport, Laurence Prusak, 2000-04-26 This influential book establishes the enduring vocabulary and concepts in the burgeoning field of knowledge management. It serves as the hands-on resource of choice for companies that recognize knowledge as the only sustainable source of competitive advantage going forward. Drawing from their work with more than thirty knowledge-rich firms, Davenport and Prusak--experienced consultants with a track record of success--examine how all types of companies can effectively understand, analyze, measure, and manage their intellectual assets, turning corporate wisdom into market value. They categorize knowledge work into four sequential activities--accessing, generating, embedding, and transferring--and look at the key skills, techniques, and processes of each. While they present a practical approach to cataloging and storing knowledge so that employees can easily leverage it throughout the firm, the authors caution readers on the limits of communications and information technology in managing intellectual capital. |
| ai in knowledge management: Successes and Failures of Knowledge Management Jay Liebowitz, 2016-06-17 Successes and Failures of Knowledge Management highlights examples from across multiple industries, demonstrating where the practice has been implemented well—and not so well—so others can learn from these cases during their knowledge management journey. Knowledge management deals with how best to leverage knowledge both internally and externally in organizations to improve decision-making and facilitate knowledge capture and sharing. It is a critical part of an organization's fabric, and can be used to increase innovation, improve organizational internal and external effectiveness, build the institutional memory, and enhance organizational agility. Starting by establishing KM processes, measures, and metrics, the book highlights ways to be successful in knowledge management institutionalization through learning from sample mistakes and successes. Whether an organization is already implementing KM or has been reluctant to do so, the ideas presented will stimulate the application of knowledge management as part of a human capital strategy in any organization. - Provides keen insights for knowledge management practitioners and educators - Conveys KM lessons learned through both successes and failures - Includes straightforward, jargon-free case studies and research developed by the leading KM researchers and practitioners across industries |
| ai in knowledge management: The Complete Idiot's Guide to Knowledge Management Melissie Clemmons Rumizen, 2002 Discusses management models and concepts, strategies for sharing knowledge, and ways to implement the concept within a company. |
| ai in knowledge management: Concepts and Advances in Information Knowledge Management Kelvin Joseph Bwalya, Nathan Mwakoshi Mnjama, Peter Mazebe II Mothataesi Sebina, 2014-04-11 Effective information and knowledge resource management is a driver of competiveness. Many developing countries have put mechanisms in place that seek to match knowledge-based economies, where information has become the fuel for responsiveness, innovation, and competition. Concepts and Advances in Information Knowledge Management brings out emerging and current discussion from the sub-fields of information management in this environment. This title consists of sections on key aspects of information knowledge management and addresses knowledge management, library studies, archives and records management, and information systems. - Presents research aimed at harmonizing theory and practice of general information management paradigms - Gives insight into the place of archives, records management, and information technology impacting socio-economic value chains - Disseminates theoretical and applied models, and information management system architecture emerging from cloud computing and retrieval systems |
| ai in knowledge management: Knowledge Management Systems Ronald Maier, 2013-03-14 Information and knowledge have fundamentally transformed the way business and social institutions work. Knowledge management promises concepts and instruments that help organizations to provide an environment supportive of knowledge generation, sharing and application. Information and communication technology (ICT) is often regarded as the enabler for the effective and especially the efficient implementation of knowledge management. The book presents an almost encyclopedic treatise of the many important facets, concepts and theories that have influenced knowledge management and integrates them into a general knowledge management framework consisting of strategy, organization, systems and economics. The book also contains the state of practice of knowledge management on the basis of a comprehensive empirical study, and concludes with four scenarios of the successful application of ICT in knowledge management initiatives. |
| ai in knowledge management: Knowledge Management Case Book Thomas H. Davenport, Gilbert J. B. Probst, 2000-12-27 With a Foreword by Dr. Heinrich von Pierer President and CEO of Siemens AG While theoretical perspectives on knowledge management abound, there is clearly a lack of shared practical applications and experiences. This book provides a perspective on knowledge management at Siemens - an internationally recognised benchmark. Tom Davenport and Gilbert Probst bring together instructive case studies from different areas of this major transnational corporation that reflect the rich insights gained from years of experience in practising knowledge management. The Knowledge Management Case Book provides a comprehensive account of how organisational knowledge assets can be managed effectively. Specific emphasis is given to the development of generic lessons that can be learned from Siemens' experience. The book also offers a roadmap to building a 'mature knowledge enterprise', thereby enhancing our understanding of the steps that need to be taken in order to sustain competitive dominance in the knowledge economy. |
| ai in knowledge management: Knowledge Engineering and Management Guus Schreiber, 2000 The disciplines of knowledge engineering and knowledge management are closely tied. Knowledge engineering deals with the development of information systems in which knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with knowledge as a key resource in modern organizations. Managing knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges. |
| ai in knowledge management: Lean AI Lomit Patel, 2020-01-30 How can startups successfully scale customer acquisition and revenue growth with a Lean team? Out-of-the-box acquisition solutions from Facebook, Google, and others provide a good start, but the companies that can tailor those solutions to meet their specific needs, objectives, and goals will come out winners. But that hasn’t been an easy task—until now. With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company. You’ll learn how to adapt, customize, and personalize cross-channel user journeys to help your company attract and retain customers—to usher in the new age of Autonomous Marketing. Learn how AI and automation can support the customer acquisition efforts of a Lean Startup Dive into Customer Acquisition 3.0, an initiative for gaining and retaining customers Explore ways to use AI for marketing purposes Understand the key metrics for determining the growth of your startup Determine the right strategy to foster user acquisition in your company Manage the increased complexity and risk inherent in AI projects |
| ai in knowledge management: Knowledge, Skill and Artificial Intelligence Bo Göranzon, Ingela Josefson, 2012-12-06 Issues raised by the Theory of Knowledge, a central theme in the development of Artificial Intelligence, are the main topic of this book. The major questions are: How is the expert's knowledge to be elicited, what are the limits and possibilities? How can skill be developed and maintained in a more and more computerized and abstract working life? This last question is also closely related to the discussion on programs for education and training in society and working life. Long term effects on skill formation in working life in relation to new technology are a very important area of research. Case studies form the basis for philosophical reflections with the main concept of tacit knowledge as the central issue of skill and new technology. To a great extent the discussion is based on current case studies of professional groups with experience in advanced computer technology. The contributions of this book demonstrate the complicated nature of human knowledge. They introduce different theoretical perspectives on the issue of knowledge acquisition and elicitation. |
| ai in knowledge management: Recent Advances in Intelligent Systems and Smart Applications Mostafa Al-Emran, Khaled Shaalan, Aboul Ella Hassanien, 2020-06-26 This book explores the latest research trends in intelligent systems and smart applications. It presents high-quality empirical and review studies focusing on various topics, including information systems and software engineering, knowledge management, technology in education, emerging technologies, and social networks. It provides insights into the theoretical and practical aspects of intelligent systems and smart applications. |
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