Simulating Language Dynamics by Means of Concept Reasoning

A problem in the phenomenological reconstruction of Complex Systems (CS) is the extraction of the knowledge that elements playing in CS use during its evolution. This problem is important because such a knowledge would allow the researcher to understand the global behavior of the system [1,2]. In this paper an approach to partially solve this problem by means of Formal Concept Analysis (FCA) is described in a particular case, namely Language Dynamics. The main idea lies in the fact that global knowledge in CS is naturally built by local interactions among agents, and FCA could be useful to represent their own knowledge. In this way it is possible to represent the effect of interactions on individual knowledge as well as the dynamics of global knowledge. Experiments in order to show this approach are given using WordNet.Ministerio de Ciencia e Innovación TIN2009-09492Junta de Andalucía TIC-606

LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu

LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Jitender Deogu

Makine çevirisinde yeni bir bilgisayımsal yaklaşım

Doktora TeziBilişsel süreçlerde bağlantısallık ve biçimsellik şeklinde ortaya çıkan ayrım, özünde bilişsel işlevlerin aşamalı gelişiminin ürünüdür ve bir ayrımdan çok katmanlı bir yapıya işaret eder. Doğal dil işlemede biçimsel yaklaşımların tek başına yetersiz kalması da geliştirilen biçimsel sistemlerin, bağlantısal temeller üzerine oturtulmamasından kaynaklanmaktadır. Bu tez çalışmasının ardındaki temel motivasyon da bağlantısal dayanaklı ontolojilerin makine çevirisinde kullanılabilecek biçimsel yapıdaki aradillerin oluşturulmasında temel olabileceği düşüncesi olmuştur. Bu noktadan yola çıkarak, dillerarası kavramsal eşlemeler bağlantısal bir şekilde gerçeklenmeye çalışılmış, ardından bu eşlemeler üzerinde temellenen aradil yapıları biçimsel bir çerçeve içinde modellenmiştir. Son olarak, kavram kafesleri şeklinde biçimselleştirilen ontolojiler, aradil yapıları olarak kullanılmış ve böylece edimsel bilginin süreç odaklı makine çevirisine eklemlenmesi sağlanarak bu ontolojilerin işlevsellikleri sınanmıştır. Ortaya konan çeviri sisteminin, Türkçe bir çocuk hikayesine uygulanması ile elde edilen test sonuçları, çeviri sonuçlarında filtre olarak kullanılan kavram kafeslerinin, makine çevirisini geliştirme açısından umut verici bir potansiyele sahip olduğunu ortaya koymuştur.AbstractThe distinction emerged as associativeness and formality in cognitive processes is fundamentally the product of the gradual development of cognitive functions and this indicates a multi-stratified structure. Since formal systems which have been developed for natural language processing are not based on associative grounds, formal approaches that are merely used in natural language processing lead to limited systems. The main motivation behind this thesis is the thought that ontologies based on associativeness could serve as the core of forming formal-structured interlingua that could be used in machine translation. Therefore, it is tried to realize cross-language conceptual mapping on the basis of associativeness; then, interlingual structures based on these mappings are modeled within a formal framework. At the end, ontologies formed as concept lattices are used as interlingual structures and functionalities of the ontologies are tested via incorporating pragmatic information into process-oriented machine translation. Test results retrieved from the application of the developed translation system to a child story reveal that concept lattices used as filters in translation results have a promising potential with regard to the development of machine translation

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

Concept Neighbourhoods in Lexical Databases

Abstract. This paper discusses results from an experimental study of concept neighbourhoods in WordNet and Roget’s Thesaurus. The general aim of this research is to determine ways in which neighbourhood lattices can be derived in real time from a lexical database and displayed on the web. In order to be readable the lattices must not be too large, not contain overlapping concepts or labels and must be calculated within seconds. Lattices should, furthermore, not be too small and they should contain sufficient complexity to be interesting for the viewer. For these purposes the sizes of the lattices of different types of concept neighbourhoods have been calculated. Using the size information should help with the task of on-line generation of the lattices.