Workshop Notes of the Sixth International Workshop "What can FCA do for Artificial Intelligence?"

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Proceedings of the 5th International Workshop "What can FCA do for Artificial Intelligence?", FCA4AI 2016(co-located with ECAI 2016, The Hague, Netherlands, August 30th 2016)

International audienceThese are the proceedings of the fifth edition of the FCA4AI workshop (http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the FCA4AI workshop is to investigate two main main issues: how can FCA support various AI activities (knowledge discovery, knowledge representation and reasoning, learning, data mining, NLP, information retrieval), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain. Accordingly, topics of interest are related to the following: (i) Extensions of FCA for AI: pattern structures, projections, abstractions. (ii) Knowledge discovery based on FCA: classification, data mining, pattern mining, functional dependencies, biclustering, stability, visualization. (iii) Knowledge processing based on concept lattices: modeling, representation, reasoning. (iv) Application domains: natural language processing, information retrieval, recommendation, mining of web of data and of social networks, etc

International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013, Beijing, China, August 4 2013)

International audienceThis second edition of the FCA4AI workshop (the first edition was associated to the ECAI 2012 Conference, see http://www.fca4ai.hse.ru/), shows again that there are many AI researchers interested in FCA. 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 processing involving learning, knowledge discovery, knowledge representation and reasoning, ontology engineering, as well as information retrieval and text processing. Thus, there exist many natural links between FCA and AI. Accordingly, the focus in this workshop was on how can FCA support AI activities (knowledge processing) and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domains

Towards Ordinal Data Science

Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason - particularly important for this line of research - is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and ‘calculating’ with ordinal structures - a specific class of directed graphs - and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics

Consistent View-Based Management of Variability in Space and Time

Systeme entwickeln sich schnell weiter und existieren in verschiedenen Variationen, um unterschiedliche und sich ändernde Anforderungen erfüllen zu können. Das führt zu aufeinanderfolgenden Revisionen (Variabilität in Zeit) und zeitgleich existierenden Produktvarianten (Variabilität in Raum). Redundanzen und Abhängigkeiten zwischen unterschiedlichen Produkten über mehrere Revisionen hinweg sowie heterogene Typen von Artefakten führen schnell zu Inkonsistenzen während der Evolution eines variablen Systems. Die Bewältigung der Komplexität sowie eine einheitliche und konsistente Verwaltung beider Variabilitätsdimensionen sind wesentliche Herausforderungen, um große und langlebige Systeme erfolgreich entwickeln zu können. Variabilität in Raum wird primär in der Softwareproduktlinienentwicklung betrachtet, während Variabilität in Zeit im Softwarekonfigurationsmanagement untersucht wird. Konsistenzerhaltung zwischen heterogenen Artefakttypen und sichtbasierte Softwareentwicklung sind zentrale Forschungsthemen in modellgetriebener Softwareentwicklung. Die Isolation der drei angrenzenden Disziplinen hat zu einer Vielzahl von Ansätzen und Werkzeugen aus den unterschiedlichen Bereichen geführt, was die Definition eines gemeinsamen Verständnisses erschwert und die Gefahr redundanter Forschung und Entwicklung birgt. Werkzeuge aus den verschiedenen Disziplinen sind oftmals nicht ausreichend integriert und führen zu einer heterogenen Werkzeuglandschaft sowie hohem manuellen Aufwand während der Evolution eines variablen Systems, was wiederum der Systemqualität schadet und zu höheren Wartungskosten führt. Basierend auf dem aktuellen Stand der Forschung in den genannten Disziplinen werden in dieser Dissertation drei Kernbeiträge vorgestellt, um den Umgang mit der Komplexität während der Evolution variabler Systeme zu unterstützten. Das unifizierte konzeptionelle Modell dokumentiert und unifiziert Konzepte und Relationen für den gleichzeitigen Umgang mit Variabilität in Raum und Zeit basierend auf einer Vielzahl ausgewählter Ansätze und Werkzeuge aus der Softwareproduktlinienentwicklung und dem Softwarekonfigurationsmanagement. Über die bloße Kombination vorhandener Konzepte hinaus beschreibt das unifizierte konzeptionelle Modell neue Möglichkeiten, beide Variabilitätsdimensionen zueinander in Beziehung zu setzen. Die unifizierten Operationen verwenden das unifizierte konzeptionelle Modell als Datenstruktur und stellen die Basis für operative Verwaltung von Variabilität in Raum und Zeit dar. Die unifizierten Operationen werden basierend auf einer Analyse diverser Ansätze konzipiert, welche verschiedene Modalitäten und Paradigmen verfolgen. Während die unifizierten Operationen die Funktionalität von analysierten Werkzeugen abdecken, ermöglichen sie den gleichzeitigen Umgang mit beiden Variabilitätsdimensionen. Der unifizierte Ansatz basiert auf den vorhergehenden Beiträgen und erweitert diese um Konsistenzerhaltung. Zu diesem Zweck wurden Typen von variabilitätsspezifischen Inkonsistenzen identifiziert, die während der Evolution variabler heterogener Systeme auftreten können. Der unifizierte Ansatz ermöglicht automatisierte Konsistenzerhaltung für eine ausgewählte Teilmenge der identifizierten Inkonsistenztypen. Jeder Kernbeitrag wurde empirisch evaluiert. Zur Evaluierung des unifizierten konzeptionellen Modells und der unifizierten Operationen wurden Expertenbefragungen durchgeführt, Metriken zur Bewertung der Angemessenheit einer Unifizierung definiert und angewendet, sowie beispielhafte Anwendungen demonstriert. Die funktionale Eignung des unifizierten Ansatzes wurde mittels zweier Realweltfallstudien evaluiert: Die häufig verwendete ArgoUML-SPL, die auf ArgoUML basiert, einem UML-Modellierungswerkzeug, sowie MobileMedia, eine mobile Applikation für Medienverwaltung. Der unifizierte Ansatz ist mit dem Eclipse Modeling Framework (EMF) und dem Vitruvius Ansatz implementiert. Die Kernbeiträge dieser Arbeit erweitern das vorhandene Wissen hinsichtlich der uniformen Verwaltung von Variabilität in Raum und Zeit und verbinden diese mit automatisierter Konsistenzerhaltung für variable Systeme bestehend aus heterogenen Artefakttypen

VooDooM : support for understanding and re-engineering of VDM-SL specifications

Tese mestrado informáticaThe main purpose of this work is to define steady ground for supporting the understanding and re-engineering of VDM-SL specifications. Understanding and re-engineering are justified by Lehman’s laws of software evolution which state, for instance, that systems must be continually adapted and as a program evolves its complexity increases unless specific work is done to reduce it. This thesis reports the implementation of understanding and re-enginering techniques in a tool called VooDooM, which was built in three well defined steps. First, development of the language front-end to recognize the VDMSL language, using a grammar-centered approach, supported by the SDF formalism, in which a wide variety of components are automatically generated from a single grammar; Second, development of understanding support, in which graphs are extracted and derived and subsequently used as input to strongly-connected components, formal concept analysis and metrication. Last, development of re-engineering support, through the development of a relational calculator that transforms a formal specification into an equivalent model which can be translated to SQL. In all steps of the work we thoroughly document the path from theory to practice and we conclude by reporting successful results obtained in two test cases.O objectivo principal deste trabalho é a definiçãoo de uma infra-estrutura para suportar compreensão e re-engenharia de especificações escritas em VDM-SL. compreensão e re-engenharia justificam-se pelas leis de evolução do software. Estas leis, formuladas por Lehman, definem, por exemplo, que um qualquer sistema deve ser continuamente adaptado e `a medida que os programas evoluem a sua complexidade tende sempre a aumentar. Esta tese descreve o estudo de técnicas de compreensão e re-engenharia que foram implementadas numa ferramenta chamada VooDooM. Esta implementação foi efectuada em três etapas bem definidas. Primeiro, foi desenvolvido um parser (front-end) para reconhecer a linguagem VDM-SL. Para tal, foi utilizada uma abordagem centrada na gramática, suportada no formalismo SDF, que está equipado com ferramentas de geração automática de diversos componentes. Segundo, para o suporte de compreensão, foram desenvolvidas funcionalidades para extrair e derivar grafos que são utilizados em técnicas de análise como componentes fortemente relacionados, análise de conceitos (formal concept analysis) e métricas. Por último, para o suporte de re-engenharia, foi prototipada uma calculadora relacional que transforma um modelo, definido numa especificação formal, no seu equivalente relacional que pode ser traduzido para SQL. Em todas as etapas realizadas h a preocupação de documentar o percurso entre teoria para a prática. A análise de resultados obtida no estudo de caso revela o sucesso da abordagem e as suas potencialidades para desenvolvimentos futuros

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Consistent View-Based Management of Variability in Space and Time

Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems

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