An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Soft behaviour modelling of user communities

A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model

Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education

This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA's capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled Virtual Teaching Assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with Learning Management Systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.Comment: 29 pages, 10 figures, 9659 word

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Some Research Questions and Results of UC3M in the E-Madrid Excellence Network

32 slides.-- Contributed to: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010.-- Presented by C. Delgado Kloos.Proceedings of: 2010 IEEE Global Engineering Education Conference (EDUCON), Madrid, Spain, 14-16 April, 2010Universidad Carlos III de Madrid is one of the six main participating institutions in the eMadrid excellence network, as well as its coordinating partner. In this paper, the network is presented together with some of the main research lines carried out by UC3M. The remaining papers in this session present the work carried out by the other five universities in the consortium.The Excellence Network eMadrid, “Investigación y Desarrollo de Tecnologías para el e-Learning en la Comunidad de Madrid” is being funded by the Madrid Regional Government under grant No. S2009/TIC-1650. In addition, we acknowledge funding from the following research projects: iCoper: “Interoperable Content for Performance in a Competency-driven Society” (eContentPlus Best Practice Network No. ECP-2007-EDU-417007), Learn3: Hacia el Aprendizaje en la 3ª Fase (“Plan Nacional de I+D+I” TIN2008-05163/ TSI), Flexo: “Desarrollo de aprendizaje adaptativo y accesible en sistemas de código abierto” (AVANZA I+D, TSI-020301- 2008-19), España Virtual (CDTI, Ingenio 2010, CENIT, Deimos Space), SOLITE (CYTED 508AC0341), and “Integración vertical de servicios telemáticos de apoyo al aprendizaje en entornos residenciales” (Programa de creación y consolidación de grupos de investigación de la Universidad Carlos III de Madrid).Publicad

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

SOME CONCEPTUAL PROPERTIES FOR KNOWLEDGE MANAGEMENT SYSTEMS DESIGN

Knowledge Management Systems (KMS) are important tools by which organizations can better useinformation and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) isdifficult to define because it encompasses a range of concepts, management tasks, technologies, and organizationalpractices, all of which come under the umbrella of the information management. Semantic approaches alloweasier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated byrelational databases and document-centric information technologies, procedural algorithmic programmingparadigms, and stack architecture. A key driver of global economic growth in the coming decade is the build-out ofbroadband telecommunications and the deployment of intelligent services bundling. This paper introduces themain characteristics of an Intelligent Knowledge Management System as a multi-agent system used in a LearningControl Problem (IKMSLCP). We describe an intelligent KM framework, allowing the observer (a human agent)to learn from experience