Knowledge discovery through creating formal contexts

Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called formal concept analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. This paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Examples are given of how formal contexts can be created using FcaBedrock and then analysed for knowledge discovery, using real datasets. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large datasets are easily converted into a standard FCA format

From Domain Models to Components - A Formal Transformation Approach Towards Dependable Software Development

Many academic, industrial, and government research units have unanimously acknowledged the importance of developing dependable software systems. At the same time they have also concurred on the difficulties and challenges to be surmounted in achieving the goal. The importance of domain analysis and linking domain models to software artifacts were also recognized by various researchers. However, no formal approach to domain analysis was attempted. The primary motivation for this thesis stems from this context. Component-based software engineering offers some attractive mechanisms to tackle the inherent complexity in developing dependable systems. Recently a formal approach has been put forth for such a development. This thesis provides a formal approach for domain analysis, and transforms the domain model to components desired by this development process. Formal Concept Analysis (FCA) is a mathematical theory for identifying and classifying concepts. This thesis taps its potential to formally analyze the domain in a software development context. It turns out that the approach presented in this thesis cannot be fully automated; nevertheless several useful contributions have been made. These include (1) capturing formal concepts and defining them in FCA; (2) defining composition rules to categorize formal concepts and their trustworthy properties; (3) integrating partial formal context tables to build the concept lattice; (4) specifying and developing a model transformation approach to construct trustworthy OWL ontology; (5) implementing a model transformation technique to generate the TADL specification of the reusable component-based system. The proposed approach is applied to CoCoME, as a benchmark case study in the domain of component-based development

Visual exploration of large geolocation-rich data sets using formal concept analysis

Information Scienc

Workshop NotesInternational Workshop ``What can FCA do for Artificial Intelligence?'' (FCA4AI 2015)

International audienceThis volume includes the proceedings of the fourth edition of the FCA4AI --What can FCA do for Artificial Intelligence?-- Workshop co-located with the IJCAI 2015 Conference in Buenos Aires (Argentina). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge discovery, learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval and text processing. There are many ``natural links'' between FCA and AI, and the present workshop is organized for discussing about these links and more generally for improving the links between knowledge discovery based on FCA and knowledge management in artificial intelligence

Eighth International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2020)

International audienceProceedings of the 8th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2020)co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, August 29, 202

Development of a model-based algorithm for the assessment of the Obsessive-Compulsive Disorder

Questa tesi presentata AAS-PD (Sistema di Assessment Adattivo per i disturbi psicologici), un sistema computerizzato di assessment psicologico adattivo per il Disturbo Ossessivo-Compulsivo (DOC). Tale sistema software è basato su una rappresentazione forma del DOC, chiamata Formal Psychological Assessment (FPA), e rappresenta una novità nel campo della psicologia clinica. AAS-PD prende una struttura di conoscenza (struttura clinica), ed esegue l'assessment facendo inferenze probabilistiche su tale struttura, usando come criterio di stop la misura dell'entropia della struttura. I risultati mostrano che AAS-PD assegna correttamente pattern di risposta a stati clinici, evidenziando inoltre alcuni miglioramenti del modello formale da fare. Sviluppi futuri comportano lo sviluppo di un vero e proprio software capace di supportare il clinico nell'assessment dei principali disturbi psicologici / This thesis presents AAS-PD (Adaptive Assessment System for psychological disorders), a computerized adaptive psychological assessment system for the Obsessive-Compulsive Disorder (OCD). This software system is based on a formal representation of the OCD called Formal Psychological Assessment (FPA), and represents an innovation in the field of clinical psychology. AAS-PD requires a knowledge structure (clinical structure), and performs the assessment by making probabilistic inferences of such a structure, using as stop criterion the measure of entropy of the structure. The results show that PD-AAS properly assigns response patterns to clinical states, and note some improvements of the formal model to do. Future developments will involve the development of a real software that supports the clinician in the assessment of the major psychological disordersope