Artificial Intelligence in Knowledge Management: Overview and Trends
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paper_title: Knowledge representation and ontologies

paper_content:

Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. [1]. The domain of interest can cover any part of the real world or any hypothetical system about which one desires to represent knowledge for com–putational purposes. A knowledge-based system maintains a knowledge base, which stores the symbols of the computational model in the form of statements about the domain, and it performs reasoning by manipulating these symbols. Applications can base their decisions on answers to domain-relevant questions posed to a knowledge base.
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paper_title: Decision support through knowledge management: the role of the artificial intelligence

paper_content:

Knowledge management (KM) has recently received considerable attention in the computer information systems community and is continuously gaining interest by industry, enterprises and government. Decision support and KM processes are interdependent activities in many organizations. In all cases, decision makers always combine different types of data and knowledge available in various forms in the organization. One of the key – but also criticized – building blocks for advancing this field of knowledge management and consequently supporting the decision making is artificial intelligence (AI). In this framework, this paper aims to improve understanding of AI towards knowledge management. It examines and discusses both the potential and the limitations of basic AI technologies in terms of their capability to support the KM process and shares thoughts and estimations on further research on the development of the next generation decision support environments.
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paper_title: Combining inductive and deductive inference in knowledge management tasks

paper_content:

This paper indicates how different logic programming technologies can underpin an architecture for distributed knowledge management in which higher throughput in information supply is achieved by a (semi-)automated solution to the more challenging problem of knowledge creation. The paper first proposes working definitions of the notions of data, knowledge and information in purely logical terms, and then shows how existing technologies can be combined into an inference engine, referred to as a knowledge, information and data engine (KIDE), integrating inductive and deductive capabilities. The paper then briefly introduces the notion of virtual organizations and uses the set-up stage of virtual organizations to exemplify the value-adding potential of KIDEs in knowledge management contexts.
---
paper_title: Decision support through knowledge management: the role of the artificial intelligence

paper_content:

Knowledge management (KM) has recently received considerable attention in the computer information systems community and is continuously gaining interest by industry, enterprises and government. Decision support and KM processes are interdependent activities in many organizations. In all cases, decision makers always combine different types of data and knowledge available in various forms in the organization. One of the key – but also criticized – building blocks for advancing this field of knowledge management and consequently supporting the decision making is artificial intelligence (AI). In this framework, this paper aims to improve understanding of AI towards knowledge management. It examines and discusses both the potential and the limitations of basic AI technologies in terms of their capability to support the KM process and shares thoughts and estimations on further research on the development of the next generation decision support environments.
---
paper_title: Decision support through knowledge management: the role of the artificial intelligence

paper_content:

Knowledge management (KM) has recently received considerable attention in the computer information systems community and is continuously gaining interest by industry, enterprises and government. Decision support and KM processes are interdependent activities in many organizations. In all cases, decision makers always combine different types of data and knowledge available in various forms in the organization. One of the key – but also criticized – building blocks for advancing this field of knowledge management and consequently supporting the decision making is artificial intelligence (AI). In this framework, this paper aims to improve understanding of AI towards knowledge management. It examines and discusses both the potential and the limitations of basic AI technologies in terms of their capability to support the KM process and shares thoughts and estimations on further research on the development of the next generation decision support environments.
---
paper_title: Knowledge management and environmental decision support systems

paper_content:

Artificial Intelligence (AI) researches have begun to use AI techniques and methodologies for developing Knowledge Management Systems (KMS) for different domains and technologies. Expert systems are considered one of the well know techniques for knowledge management and can be aid in solving problems in specific domain. Case-based reasoning has been successfully used for managing knowledge of implicit time. Data mining is a new methodology for searching and discover hidden pattern in very huge data bases. On the other hand, an effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Recently, a new discipline called Environmental Informatics integrates environmental science with computer science is emerging. This paper discusses the role of AI technologies; namely, expert systems and data mining, in environmental KMS.
---
paper_title: Decisional DNA and the Smart Knowledge Management System: Knowledge Engineering and Knowledge Management applied to an Intelligent Platform

paper_content:

Experience has made species to survive and cultures to prevail, as experience makes organizations to succeed. Thus, capturing the experience of every decision taken in an explicit representation form is highlighted as the utmost importance in knowledge engineering presented in this book. Decisional DNA knowledge representation allows building the experiential fingerprints of an organization by implementing a model for transforming information into knowledge. The Smart Knowledge Management System (SKMS) is a self-learning and intelligent knowledge management hybrid platform developed to help decision makers in their daily operation. Technologies such as expert systems, simulation, statistical tools, knowledge-based systems, and multiple AI technologies are integrated into the SKMS allowing the combination of different perspectives for acquiring the required explicit decisional experiential knowledge by the means of Decisional DNA. The presented tools offer useful insights to professionals, students and researchers interested in knowledge engineering and knowledge management, but more generally, in the artificial intelligence and information systems fields.
---
paper_title: Improving knowledge management programs using marginal utility in a metric space generated by conceptual graphs

paper_content:

Knowledge management has emerged as a field of endeavor that blends a systems approach with methods drawn from organizational management and learning. In contrast, knowledge representation, a branch of artificial intelligence, is grounded in formal methods. Research in the separate behavioral and the structural disciplines—knowledge management and knowledge engineering—has not traditionally cross-pollinated. This has prevented the development of many practical practices useful in organizations. Organization managers—line and senior—lack guidance in where to direct improvement efforts targeted at specific groups of company knowledge workers. Demonstrated here is Knowledge Improvement Measurement Space (KIMS), a model providing a solution to that improvement problem. It employs marginal utility theory in a metric space, with formal reasoning via software agents realized in Sowa's conceptual graphs, operating over a knowledge management conceptual structure. These components allow repeated evaluation of knowledge improvement measurements. Knowledge representation technology was applied to organize and encourage knowledge sharing, to achieve competitive advantage, and to measure progress toward that achievement. The KIMS re-entrant process, a method of using the KIMS model, was shown to consist of metrics data calculated by executing joined conceptual graphs, consolidated into a distance variable to be estimated via a Minkowski metrics space. The metric space was shown to be equivalent to a marginal utility, which may be evaluated to determine the new level of knowledge capability. The procedure may be repeated until knowledge management goals are achieved. The solution took into account the body of knowledge related to human understanding and learning, and formal methods of knowledge organization. These were shown to include surface ontologies based in a knowledge management program, principles of business strategy, and organizational learning. KIMS was validated through a demonstration based on empirical data collected over a five-year program in a large aerospace company during its progress in applying the Software Engineering Institute Capability Maturity Model.
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paper_title: Knowledge value chain: an effective tool to measure knowledge value

paper_content:

Knowledge value is a significant issue in knowledge management, but its related problems are still challenging. This paper aims at discussing how knowledge value changes in the knowledge evolution process and develops a knowledge value chain (KVC) to measure knowledge value. By applying the notions of knowledge state and knowledge maturity, the knowledge finite state machine (KFSM) and knowledge maturity model (KMM) are introduced to characterise the KVC. Based on these concepts, knowledge value is measured by calculating the difference between two maturity states rather than by direct calculation. This point of view of knowledge value, the construction of KVC and the association of knowledge value and knowledge maturity are insightful for both researchers and practitioners.
---
paper_title: Knowledge management and environmental decision support systems

paper_content:

Artificial Intelligence (AI) researches have begun to use AI techniques and methodologies for developing Knowledge Management Systems (KMS) for different domains and technologies. Expert systems are considered one of the well know techniques for knowledge management and can be aid in solving problems in specific domain. Case-based reasoning has been successfully used for managing knowledge of implicit time. Data mining is a new methodology for searching and discover hidden pattern in very huge data bases. On the other hand, an effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Recently, a new discipline called Environmental Informatics integrates environmental science with computer science is emerging. This paper discusses the role of AI technologies; namely, expert systems and data mining, in environmental KMS.
---
paper_title: Concept maps and case-based reasoning: a perspective for the intelligent teaching/learning systems

paper_content:

The use of present pedagogical methods and Information and Communication Technologies produce a new quality that favors the task of generating, transmitting and sharing knowledge. That is the case of the pedagogical effect that produces the use of the Concept Maps, which are considered a learning technique as a way to increase meaningful learning in the sciences. It is also used for the knowledge management as an aid to personalize the learning process, to exchange knowledge, and to learn how to learn. Concept Mapping provides a framework for making this internal knowledge explicit in a visual form that can easily be examined and shared. Concept Maps are relevant since they can be retrieved or adapted to new problems. In the other hand, Case-Based Reasoning as a technique in Artificial Intelligence plays an important role in knowledge retrieval and reuse of memories. In this paper the authors present a new approach to elaborate Intelligent Teaching/Learning Systems, where the techniques of Concept Maps and Artificial Intelligence are combined, using the Case-Based Reasoning as theoretical framework for the Student Model. The proposed model has been implemented in the computational system HESEI, which has been successfully used in the teaching/learning process by laymen in the Computer Science field.
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paper_title: The Knowledge Creating Company

paper_content:

Japanese companies, masters of manufacturing, have also been leaders in the creation, management, and use of knowledge-especially the tacit and often subjective insights, intuitions, and ideas of employees.
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paper_title: The Artificial Intelligence in Personal Knowledge Management

paper_content:

With the development of the society, it is necessary to apply artificial intelligence to personal knowledge management. This paper researches on it. At first, this paper introduces the meaning of personal knowledge management and the meaning of artificial intelligence. And then introduce the problems in personal knowledge management at present. There are three problems:(1) The problem of information overload. (2) The problem of unstructured information. (3) The problem of tacit knowledge. At third, in order to solve these problems, introduce the application of artificial intelligence in personal knowledge management. The applications are: (1)The intelligent search of knowledge. (2) The automatic classification of knowledge. (3) The conversion of tacit knowledge. This paper explains the application detailed. Finally, summarize that the application of artificial intelligence in personal knowledge management is still at the initial stage, but it has a beautiful future.
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paper_title: ICT Perceptions and Meanings: Implications for Knowledge Transfer

paper_content:

Driven by global competition and economic pressures, organizations are increasingly interested in transferring and leveraging local expertise at the global level. While many of the challenges of knowledge transfer (KT) have been discussed in the literature (e.g., incentives, cognitive limitations), the challenge of KT in distributed, or "virtual", settings and the role of information and communication technologies (ICTs) have received limited attention. While any given ICT may be described in terms of one's perceptual awareness of its capabilities (e.g., richness, interactivity), it may also be described relative to the meanings an individual attaches to it, i.e., the idea of it or its purpose, rather than capability. We propose that understanding both perceptions and meanings, particularly as new ICTs are introduced, is critical to understanding selection and use by KT participants, and ultimately outcomes. In this paper, we conceptually explore the implications of meanings and perceptions on KT in virtual settings.
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paper_title: The Artificial Intelligence in Personal Knowledge Management

paper_content:

With the development of the society, it is necessary to apply artificial intelligence to personal knowledge management. This paper researches on it. At first, this paper introduces the meaning of personal knowledge management and the meaning of artificial intelligence. And then introduce the problems in personal knowledge management at present. There are three problems:(1) The problem of information overload. (2) The problem of unstructured information. (3) The problem of tacit knowledge. At third, in order to solve these problems, introduce the application of artificial intelligence in personal knowledge management. The applications are: (1)The intelligent search of knowledge. (2) The automatic classification of knowledge. (3) The conversion of tacit knowledge. This paper explains the application detailed. Finally, summarize that the application of artificial intelligence in personal knowledge management is still at the initial stage, but it has a beautiful future.
---
paper_title: Concept maps and case-based reasoning: a perspective for the intelligent teaching/learning systems

paper_content:

The use of present pedagogical methods and Information and Communication Technologies produce a new quality that favors the task of generating, transmitting and sharing knowledge. That is the case of the pedagogical effect that produces the use of the Concept Maps, which are considered a learning technique as a way to increase meaningful learning in the sciences. It is also used for the knowledge management as an aid to personalize the learning process, to exchange knowledge, and to learn how to learn. Concept Mapping provides a framework for making this internal knowledge explicit in a visual form that can easily be examined and shared. Concept Maps are relevant since they can be retrieved or adapted to new problems. In the other hand, Case-Based Reasoning as a technique in Artificial Intelligence plays an important role in knowledge retrieval and reuse of memories. In this paper the authors present a new approach to elaborate Intelligent Teaching/Learning Systems, where the techniques of Concept Maps and Artificial Intelligence are combined, using the Case-Based Reasoning as theoretical framework for the Student Model. The proposed model has been implemented in the computational system HESEI, which has been successfully used in the teaching/learning process by laymen in the Computer Science field.
---
paper_title: The Tyranny of Tacit Knowledge: What Artificial Intelligence Tells us About Knowledge Representation

paper_content:

Polanyi's tacit knowledge captures the idea "we can know more than we can tell." Many researchers in the knowledge management community have used the idea of tacit knowledge to draw a distinction between that which cannot be formally represented (tacit knowledge) and knowledge which can be so represented (explicit knowledge). I argue that the deference that knowledge management researchers give to tacit knowledge hinders potentially fruitful work for two important reasons. First, the inability to explicate knowledge does not imply that the knowledge cannot be formally represented. Second, assuming the inability to formalize tacit knowledge as it exists in the minds of people does not exclude the possibility that computer systems might perform the same tasks using alternative representations. By reviewing work from artificial intelligence, I will argue that a richer model of cognition and knowledge representation is needed to study and build knowledge management systems.
---
paper_title: Information Extraction in Semantic Wikis

paper_content:

This paper deals with information extraction technologies supporting semantic annotation and logical organization of textual content in semantic wikis. We describe our work in the context of the KiWi project which aims at developing a new knowledge management system motivated by the wiki way of collaborative content creation that is enhanced by the semantic web technology. The specific characteristics of semantic wikis as advanced community knowledge-sharing platforms are discussed from the perspective of the functionality providing automatic suggestions of semantic tags. We focus on the innovative aspects of the implemented methods. The interfaces of the user-interaction tools as well as the back-end web services are also tackled. We conclude that though there are many challenges related to the integration of information extraction into semantic wikis, this fusion brings valuable results.
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paper_title: Wikis: 'From Each According to His Knowledge'

paper_content:

Wikis offer tremendous potential to capture knowledge from large groups of people, making tacit, hidden content explicit and widely available. They also efficiently connect those with information to those seeking it.
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paper_title: Hierarchical Document Classification Based on a Backtracking Algorithm

paper_content:

Hierarchical document classification refers to assigning one or more suitable categories from a hierarchical category space to a document. This paper proposes a new hierarchical document classification method based on a backtracking algorithm. Utilizing the relationships between categories in category tree, a suitable threshold for every category is found to determine whether a document could be classified into the category. And the backtracking algorithm in our hierarchical classification approach effectively solves the problem that a misclassification at higher level directly leads to the misclassification at a lower level. Moreover, feature set is selected by integrating information gain with hierarchy information, which accords with the characteristic of a category tree. Experiments show that the method performs well when enough training documents are given.
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paper_title: A Hierarchical Classification Model for Document Categorization

paper_content:

We propose a novel hierarchical classification method for documents categorization in this paper. The approach consists of multiple levels of classification for different hierarchies. Regularized Least Square (RLS)binary classifiers are applied in the middle levels of the hierarchy to classify documents into smaller set of categories and K-nearest-neighbor (KNN) multi-class classifiers are used at the bottom to classify documents into final classes. Experiments on large-scale real world tax documents show that the proposed hierarchical approach outperforms traditional flat classification method.
---
paper_title: The Artificial Intelligence in Personal Knowledge Management

paper_content:

With the development of the society, it is necessary to apply artificial intelligence to personal knowledge management. This paper researches on it. At first, this paper introduces the meaning of personal knowledge management and the meaning of artificial intelligence. And then introduce the problems in personal knowledge management at present. There are three problems:(1) The problem of information overload. (2) The problem of unstructured information. (3) The problem of tacit knowledge. At third, in order to solve these problems, introduce the application of artificial intelligence in personal knowledge management. The applications are: (1)The intelligent search of knowledge. (2) The automatic classification of knowledge. (3) The conversion of tacit knowledge. This paper explains the application detailed. Finally, summarize that the application of artificial intelligence in personal knowledge management is still at the initial stage, but it has a beautiful future.
---
paper_title: Knowledge management and environmental decision support systems

paper_content:

Artificial Intelligence (AI) researches have begun to use AI techniques and methodologies for developing Knowledge Management Systems (KMS) for different domains and technologies. Expert systems are considered one of the well know techniques for knowledge management and can be aid in solving problems in specific domain. Case-based reasoning has been successfully used for managing knowledge of implicit time. Data mining is a new methodology for searching and discover hidden pattern in very huge data bases. On the other hand, an effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Recently, a new discipline called Environmental Informatics integrates environmental science with computer science is emerging. This paper discusses the role of AI technologies; namely, expert systems and data mining, in environmental KMS.
---
paper_title: Concept maps and case-based reasoning: a perspective for the intelligent teaching/learning systems

paper_content:

The use of present pedagogical methods and Information and Communication Technologies produce a new quality that favors the task of generating, transmitting and sharing knowledge. That is the case of the pedagogical effect that produces the use of the Concept Maps, which are considered a learning technique as a way to increase meaningful learning in the sciences. It is also used for the knowledge management as an aid to personalize the learning process, to exchange knowledge, and to learn how to learn. Concept Mapping provides a framework for making this internal knowledge explicit in a visual form that can easily be examined and shared. Concept Maps are relevant since they can be retrieved or adapted to new problems. In the other hand, Case-Based Reasoning as a technique in Artificial Intelligence plays an important role in knowledge retrieval and reuse of memories. In this paper the authors present a new approach to elaborate Intelligent Teaching/Learning Systems, where the techniques of Concept Maps and Artificial Intelligence are combined, using the Case-Based Reasoning as theoretical framework for the Student Model. The proposed model has been implemented in the computational system HESEI, which has been successfully used in the teaching/learning process by laymen in the Computer Science field.
---
paper_title: Improving knowledge management programs using marginal utility in a metric space generated by conceptual graphs

paper_content:

Knowledge management has emerged as a field of endeavor that blends a systems approach with methods drawn from organizational management and learning. In contrast, knowledge representation, a branch of artificial intelligence, is grounded in formal methods. Research in the separate behavioral and the structural disciplines—knowledge management and knowledge engineering—has not traditionally cross-pollinated. This has prevented the development of many practical practices useful in organizations. Organization managers—line and senior—lack guidance in where to direct improvement efforts targeted at specific groups of company knowledge workers. Demonstrated here is Knowledge Improvement Measurement Space (KIMS), a model providing a solution to that improvement problem. It employs marginal utility theory in a metric space, with formal reasoning via software agents realized in Sowa's conceptual graphs, operating over a knowledge management conceptual structure. These components allow repeated evaluation of knowledge improvement measurements. Knowledge representation technology was applied to organize and encourage knowledge sharing, to achieve competitive advantage, and to measure progress toward that achievement. The KIMS re-entrant process, a method of using the KIMS model, was shown to consist of metrics data calculated by executing joined conceptual graphs, consolidated into a distance variable to be estimated via a Minkowski metrics space. The metric space was shown to be equivalent to a marginal utility, which may be evaluated to determine the new level of knowledge capability. The procedure may be repeated until knowledge management goals are achieved. The solution took into account the body of knowledge related to human understanding and learning, and formal methods of knowledge organization. These were shown to include surface ontologies based in a knowledge management program, principles of business strategy, and organizational learning. KIMS was validated through a demonstration based on empirical data collected over a five-year program in a large aerospace company during its progress in applying the Software Engineering Institute Capability Maturity Model.
---
paper_title: Knowledge value chain: an effective tool to measure knowledge value

paper_content:

Knowledge value is a significant issue in knowledge management, but its related problems are still challenging. This paper aims at discussing how knowledge value changes in the knowledge evolution process and develops a knowledge value chain (KVC) to measure knowledge value. By applying the notions of knowledge state and knowledge maturity, the knowledge finite state machine (KFSM) and knowledge maturity model (KMM) are introduced to characterise the KVC. Based on these concepts, knowledge value is measured by calculating the difference between two maturity states rather than by direct calculation. This point of view of knowledge value, the construction of KVC and the association of knowledge value and knowledge maturity are insightful for both researchers and practitioners.
---
paper_title: The Tyranny of Tacit Knowledge: What Artificial Intelligence Tells us About Knowledge Representation

paper_content:

Polanyi's tacit knowledge captures the idea "we can know more than we can tell." Many researchers in the knowledge management community have used the idea of tacit knowledge to draw a distinction between that which cannot be formally represented (tacit knowledge) and knowledge which can be so represented (explicit knowledge). I argue that the deference that knowledge management researchers give to tacit knowledge hinders potentially fruitful work for two important reasons. First, the inability to explicate knowledge does not imply that the knowledge cannot be formally represented. Second, assuming the inability to formalize tacit knowledge as it exists in the minds of people does not exclude the possibility that computer systems might perform the same tasks using alternative representations. By reviewing work from artificial intelligence, I will argue that a richer model of cognition and knowledge representation is needed to study and build knowledge management systems.
---
paper_title: Decisional DNA and the Smart Knowledge Management System: Knowledge Engineering and Knowledge Management applied to an Intelligent Platform

paper_content:

Experience has made species to survive and cultures to prevail, as experience makes organizations to succeed. Thus, capturing the experience of every decision taken in an explicit representation form is highlighted as the utmost importance in knowledge engineering presented in this book. Decisional DNA knowledge representation allows building the experiential fingerprints of an organization by implementing a model for transforming information into knowledge. The Smart Knowledge Management System (SKMS) is a self-learning and intelligent knowledge management hybrid platform developed to help decision makers in their daily operation. Technologies such as expert systems, simulation, statistical tools, knowledge-based systems, and multiple AI technologies are integrated into the SKMS allowing the combination of different perspectives for acquiring the required explicit decisional experiential knowledge by the means of Decisional DNA. The presented tools offer useful insights to professionals, students and researchers interested in knowledge engineering and knowledge management, but more generally, in the artificial intelligence and information systems fields.
---
paper_title: Wikis: 'From Each According to His Knowledge'

paper_content:

Wikis offer tremendous potential to capture knowledge from large groups of people, making tacit, hidden content explicit and widely available. They also efficiently connect those with information to those seeking it.
---

Title: Artificial Intelligence in Knowledge Management: Overview and Trends

Section 1: INTRODUCTION
Description 1: Introduce the importance of knowledge management in organizations, the role of ICT and AI in managing knowledge, and the aim of the paper.

Section 2: OVERVIEW OF RESEARCH PAPERS (2001-2003)
Description 2: Summarize definitions of data, information, and knowledge, and review early research discussing KM tasks and AI methods like expert systems, neural networks, and intelligent agents.

Section 3: LATEST KM FRAMEWORKS
Description 3: Outline the latest knowledge management frameworks, highlighting the growing importance of AI in KM. Discuss the different types of learning within KM (human, organizational, machine learning).

Section 4: MEETING THE CHALLENGES
Description 4: Discuss the evolution of AI support in KM since the early 2000s, current integrated AI technologies for KM, measurement models for KM, and self-improving systems.

Section 4.A: Integrated framework for the use of AI technologies in KM tasks
Description 4.A: Provide examples of complex frameworks integrating multiple AI technologies in KM such as expert systems, data mining, and case-based reasoning.

Section 4.B: Measurements
Description 4.B: Discuss measurement-oriented culture in KM, including knowledge loss risk assessment and Knowledge Improvement Measurement Space (KIMS).

Section 4.C: Systems that improve their results
Description 4.C: Explain systems that employ techniques like Case-Based Reasoning (CBR) to improve performance by learning from experience.

Section 5: RECENT TRENDS IN KNOWLEDGE MANAGEMENT (2008-2010)
Description 5: Discuss recent trends in KM, including personal knowledge management and distributed knowledge work, and their support by AI technologies.

Section 5.A: Personal knowledge management
Description 5.A: Address the importance of personal KM, AI applications for tackling personal KM challenges like information overload, and intelligent search.

Section 5.B: Distributed knowledge work
Description 5.B: Describe the shift to virtual work in organizations, the increased demands on ICT for communication and collaboration, and KM challenges specific to distributed work.

Section 6: TOPICAL KM TASKS
Description 6: Explore the latest topical tasks in KM supported by AI technologies, including the representation of tacit knowledge, knowledge mining on the web, text categorization, knowledge sharing, and advanced search and retrieval systems.

Section 6.A: Efforts to represent tacit knowledge
Description 6.A: Discuss methods for representing tacit knowledge using expert systems, wikis, and concept maps.

Section 6.B: Knowledge mining on the web
Description 6.B: Explain approaches to mining knowledge on the web, including the creation of specialized glossaries and the role of blogging.

Section 6.C: Text categorization
Description 6.C: Review techniques for hierarchical and multi-label text categorization supported by AI, such as concept maps and specialized algorithms.

Section 6.D: Knowledge sharing
Description 6.D: Discuss the use of AI and ontologies for knowledge sharing within wireless sensor networks and other systems.

Section 6.E: Search and retrieval
Description 6.E: Analyze intelligent agent-based improvements in search and retrieval engines to enhance KM tasks.

Section 7: CONCLUSIONS
Description 7: Summarize the significance of AI in KM, highlight accomplishments and trends, and provide a reference collection for further studies on particular KM topics.