Case-Based Reasoning on E-Community Knowledge

International audienceThis paper presents MKM, a meta-knowledge model to manage knowledge reliability, in order to extend a CBR system so that it can reason on partially reliable, non expert, knowledge from the Web. Knowledge reliability is considered from the point of view of the decision maker using the CBR system. It is captured by the MKM model including notions such as belief, trust, reputation and quality, as well as their relationships and rules to evaluate knowledge reliability. We detail both the model and the associated approach to extend CBR. Given a problem to solve for a specific user, reliability estimation is used to filter knowledge with high reliability as well as to rank the results produced by the CBR system, ensuring the quality of results

How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability

International audienceThis paper shows that performing case-based reasoning (CBR) on knowledge coming from an e-community is improved by taking into account knowledge reliability. MKM (meta-knowledge model) is a model for managing reliability of the knowledge units that are used in the reasoning process. For this, MKM uses meta-knowledge such as belief, trust and reputation, about knowledge units and users. MKM is used both to select relevant knowledge to conduct the reasoning process, and to rank results provided by the CBR engine according to the knowledge reliability. An experiment in which users perform a blind evaluation of results provided by two systems (with and without taking into account reliability, i.e. with and without MKM) shows that users are more satisfied with results provided by the system implementing MKM

Resources and users in the tagging process: approaches and case studies

In this contribution we propose a comparison between two distinct approaches to the annotation of digital resources. The former, top-down, is rooted in the cathedral model and is based on an authoritative, centralized definition of the adopted mark-up language; the latter, bottom-up, refers to the bazaar model and is based on the contributions of a community of users. These two approaches are analyzed taking into account both their descriptive potential and the constraints they impose on the reasoning process of recommender systems, with special reference to user profiling. Three case studies are described, with reference to research projects that apply these approaches in the contexts of e-learning and knowledge management

Editor’s Note

This special issue “Artificial Intelligence and Social Application” includes extended versions of selected papers from Artificial Intelligence and Education area of the 13th edition of the Ibero-American Conference on Artificial Intelligence, held in Cartagena de Indias - Colombia, November, 2012. The issue includes, thus, five selected papers, describing innovative research work, on Artificial Intelligence in Education area including, among others: Recommender Systems, Learning Objects, Intelligent Tutoring Systems, Multi-Agent Systems, Virtual Learning Environments, Case-based reasoning and Classifiers Algorithms. This issue also includes six papers in the Interactive Multimedia and Artificial Intelligence areas, dealing with subjects such as User Experience, E-Learning, Communication Tools, Multi-Agent Systems, Grid Computing. IBERAMIA 2012 was the 13th edition of the Ibero-American Conference on Artificial Intelligence, a leading symposium where the Ibero-American AI community comes together to share research results and experiences with researchers in Artificial Intelligence from all over the world. The papers were organized in topical sections on knowledge representation and reasoning, information and knowledge processing, knowledge discovery and data mining, machine learning, bio-inspired computing, fuzzy systems, modelling and simulation, ambient intelligence, multi-agent systems, human-computer interaction, natural language processing, computer vision and robotics, planning and scheduling, AI in education, and knowledge engineering and applications

Cross-Cultural E-Mentor Roles in Facilitating Inquiry-Based Online Learning

This paper discusses how cross-cultural e-mentoring facilitated inquiry-based learning through community and knowledge building and the multiple roles e-mentors played in fostering transformative learning in protégées. A series of professional development programmes on “online tutoring and mentoring” were conducted by the Distance Education Modernization Project (DEMP) implemented by the Ministry of Higher Education in Sri Lanka. These programmes were conducted in the form of a blended online course using the MOODLE learning management system. The main aim of these programmes was to develop the capacity of faculty and other professionals who would be responsible for designing and delivering online programmes. One of the online activities in this course was to develop the capacity of the participants to facilitate inquiry-based learning using cross-cultural e-mentors. In each round of training, participants were divided into three groups (about 8-11 participants in each) to solve a social problem, using three inquiry-based learning formats: problem solving, role-play and case-based reasoning. The e-mentors were graduate students at the University of New Mexico, in the United States. Their goals were to facilitate the interactive activity and help the Sri Lanakan protégées solve the problem through negotiation of meaning in an online environment. The transcripts of participants in 3 rounds of training and their interactions with e-mentors were analysed. Results showed that the cross-cultural e-mentors demonstrated different strategies to help protégés to find solutions, help them build the online community and to construct knowledge by building on each other’s posts. Their contributions range from 15% - 41% of the total posts. They exhibited multiple-roles; pedagogical, managerial, technical, social, collaborative and inspirational. Protégés acknowledged that e-mentors transformed their perspectives on the social problems they dealt with, and methods of online learning

A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL

Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT and GPT-4 have significantly impacted the AI community, including Text-to-SQL tasks. Some evaluations and analyses on LLMs show their potential to generate SQL queries but they point out poorly designed prompts (e.g. simplistic construction or random sampling) limit LLMs' performance and may cause unnecessary or irrelevant outputs. To address these issues, we propose CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5 for precise control over case-relevant and case-irrelevant knowledge in Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for GPT-3.5, which involves (1) adaptively retrieving cases according to the question intention by de-semantizing the input question, and (2) an adaptive fallback mechanism to ensure the informativeness of the prompt, as well as the relevance between cases and the prompt. In the de-semanticization phase, we designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e detector(mining implicit semantics in hyperbolic space), TextAlign(discovering explicit matches), and Positector (part-of-speech detector). SDRE semantically and syntactically generates in-context exemplar annotations for the new case. On the three cross-domain datasets, our framework outperforms the state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%, respectively

Development and implementation of clinical guidelines : an artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/2011"

JointZone: users' view of an adaptive online learning resource for rheumatology

This paper describes an online learning resource for rheumatology that was designed for a wide constituency of users including primarily undergraduate medical students and health professionals. Although the online resources afford an informal learning environment, the site was pedagogically designed to comply with the general recommendations of the Standing Committee on Training and Education of EULAR (European League Against Rheumatism) for a rheumatology core curriculum. Any Internet user may freely browse the site content with optional registration providing access to adaptive features that personalize the user’s view, for example, providing a reading history and targeted support based on scores from completed case studies. The site has now been available since early 2003, and an online survey of site registrants indicates that well structured pedagogical materials that reflect a learners’ dominant ‘community of practice’ appear to be a successful aid to informal learning

Knowledge Acquisition for Content Selection

An important part of building a natural-language generation (NLG) system is knowledge acquisition, that is deciding on the specific schemas, plans, grammar rules, and so forth that should be used in the NLG system. We discuss some experiments we have performed with KA for content-selection rules, in the context of building an NLG system which generates health-related material. These experiments suggest that it is useful to supplement corpus analysis with KA techniques developed for building expert systems, such as structured group discussions and think-aloud protocols. They also raise the point that KA issues may influence architectural design issues, in particular the decision on whether a planning approach is used for content selection. We suspect that in some cases, KA may be easier if other constructive expert-system techniques (such as production rules, or case-based reasoning) are used to determine the content of a generated text.Comment: To appear in the 1997 European NLG workshop. 10 pages, postscrip