9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at classification and knowledge discovery that can be used for many purposes in Artificial Intelligence (AI). The objective of the ninth edition of the FCA4AI workshop (see http://www.fca4ai.hse.ru/) is to investigate several issues such as: how can FCA support various AI activities (knowledge discovery, knowledge engineering, machine learning, data mining, information retrieval, recommendation...), how can FCA be extended in order to help AI researchers to solve new and complex problems in their domains, and how FCA can play a role in current trends in AI such as explainable AI and fairness of algorithms in decision making.The workshop was held in co-location with IJCAI 2021, Montréal, Canada, August, 28 2021

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

International audienc

Constructing and Extending Description Logic Ontologies using Methods of Formal Concept Analysis

Description Logic (abbrv. DL) belongs to the field of knowledge representation and reasoning. DL researchers have developed a large family of logic-based languages, so-called description logics (abbrv. DLs). These logics allow their users to explicitly represent knowledge as ontologies, which are finite sets of (human- and machine-readable) axioms, and provide them with automated inference services to derive implicit knowledge. The landscape of decidability and computational complexity of common reasoning tasks for various description logics has been explored in large parts: there is always a trade-off between expressibility and reasoning costs. It is therefore not surprising that DLs are nowadays applied in a large variety of domains: agriculture, astronomy, biology, defense, education, energy management, geography, geoscience, medicine, oceanography, and oil and gas. Furthermore, the most notable success of DLs is that these constitute the logical underpinning of the Web Ontology Language (abbrv. OWL) in the Semantic Web. Formal Concept Analysis (abbrv. FCA) is a subfield of lattice theory that allows to analyze data-sets that can be represented as formal contexts. Put simply, such a formal context binds a set of objects to a set of attributes by specifying which objects have which attributes. There are two major techniques that can be applied in various ways for purposes of conceptual clustering, data mining, machine learning, knowledge management, knowledge visualization, etc. On the one hand, it is possible to describe the hierarchical structure of such a data-set in form of a formal concept lattice. On the other hand, the theory of implications (dependencies between attributes) valid in a given formal context can be axiomatized in a sound and complete manner by the so-called canonical base, which furthermore contains a minimal number of implications w.r.t. the properties of soundness and completeness. In spite of the different notions used in FCA and in DLs, there has been a very fruitful interaction between these two research areas. My thesis continues this line of research and, more specifically, I will describe how methods from FCA can be used to support the automatic construction and extension of DL ontologies from data

A Novel Method for Pre-combustion CO2 Capture in Fluidized Bed

La comunidad internacional está realizando enormes esfuerzos para mitigar los efectos de las emisiones de gases de efecto invernadero (GEI) en el cambio climático. Aproximadamente le 25% de las emisiones globales de GEI (fundamentalmente CO2) son generados por la combustión de combustibles fósiles en el sector eléctrico. La captura y almacenamiento de CO2 se ha propuesto como una alternativa para reducir las emisiones de GEI en centrales térmicas. Numerosas tecnologías para la captura de CO2 se han desarrollado en los últimos años, fundamentalmente en tres líneas tecnológicas: postcombustión, oxicombustión y precombustión. Esta tesis presenta un nuevo método para la captura de CO2 en precombustión, produciendo hidrógeno a partir de carbón, sin emisiones de GEI. El objetivo principal de este trabajo ha sido desarrollar un modelo completo, mediante herramientas de fluido dinámica computacional (CFD), del proceso de reformado de un gas de síntesis con alto contenido en metano combinado con la captura de CO2 mediante adsorción con sorbentes sólidos regenerables. Este proceso es conocido como reformado de metano mejorado por adsorción (o SE-SMR, su acrónimo en inglés). SE-SMR representa una novedosa y eficiente energéticamente ruta para la producción de hidrógeno con captura in situ de CO2. Este proceso ha sido estudiado en un lecho fluido burbujeante, usando sorbentes sólidos de óxido de calcio como captores de CO2. Dos sorbentes sólidos han sido estudiados en laboratorio: uno natural (Dolomita) y uno sintético (CaO- Ca12Al14O33). Además, varios tratamientos han sido desarrollados para mejorar la capacidad de captura de estos sorbentes. Un completo modelo CFD del proceso de SE-SMR ha sido desarrollado. Una aproximación Euleriana-Euleriana ha sido combinada con la Teoría Cinética de Flujos Granulares para simular la fluidodinámica del lecho fluido burbujeante. Los reacciones químicas de reformado y carbonatación han sido implementadas en el modelo CFD. Se ha incluido un modelo detallado de captura de CO2 para simular el comportamiento de los diferentes sorbentes sometidos a diferentes pretratamientos para mejorar su rendimiento. Asimismo, un modelo de arrastre de partículas ha sido desarrollado para reducir el coste computacional de las simulaciones a escala semi-industrial. Se ha llevado a cabo una extensa campaña de simulaciones para validar el modelo a escala de laboratorio y semi-industrial. Las simulaciones CFD han sido combinadas con un Diseño de Experimentos Robusto, con el objetivo predecir y evaluar la sensibilidad del proceso SE-SMR a diversos factores operativos

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

Efficient Maximum A-Posteriori Inference in Markov Logic and Application in Description Logics

Maximum a-posteriori (MAP) query in statistical relational models computes the most probable world given evidence and further knowledge about the domain. It is arguably one of the most important types of computational problems, since it is also used as a subroutine in weight learning algorithms. In this thesis, we discuss an improved inference algorithm and an application for MAP queries. We focus on Markov logic (ML) as statistical relational formalism. Markov logic combines Markov networks with first-order logic by attaching weights to first-order formulas. For inference, we improve existing work which translates MAP queries to integer linear programs (ILP). The motivation is that existing ILP solvers are very stable and fast and are able to precisely estimate the quality of an intermediate solution. In our work, we focus on improving the translation process such that we result in ILPs having fewer variables and fewer constraints. Our main contribution is the Cutting Plane Aggregation (CPA) approach which leverages symmetries in ML networks and parallelizes MAP inference. Additionally, we integrate the cutting plane inference (Riedel 2008) algorithm which significantly reduces the number of groundings by solving multiple smaller ILPs instead of one large ILP. We present the new Markov logic engine RockIt which outperforms state-of-the-art engines in standard Markov logic benchmarks. Afterwards, we apply the MAP query to description logics. Description logics (DL) are knowledge representation formalisms whose expressivity is higher than propositional logic but lower than first-order logic. The most popular DLs have been standardized in the ontology language OWL and are an elementary component in the Semantic Web. We combine Markov logic, which essentially follows the semantic of a log-linear model, with description logics to log-linear description logics. In log-linear description logic weights can be attached to any description logic axiom. Furthermore, we introduce a new query type which computes the most-probable 'coherent' world. Possible applications of log-linear description logics are mainly located in the area of ontology learning and data integration. With our novel log-linear description logic reasoner ELog, we experimentally show that more expressivity increases quality and that the solutions of optimal solving strategies have higher quality than the solutions of approximate solving strategies