Visualizing and Interacting with Concept Hierarchies

Concept Hierarchies and Formal Concept Analysis are theoretically well grounded and largely experimented methods. They rely on line diagrams called Galois lattices for visualizing and analysing object-attribute sets. Galois lattices are visually seducing and conceptually rich for experts. However they present important drawbacks due to their concept oriented overall structure: analysing what they show is difficult for non experts, navigation is cumbersome, interaction is poor, and scalability is a deep bottleneck for visual interpretation even for experts. In this paper we introduce semantic probes as a means to overcome many of these problems and extend usability and application possibilities of traditional FCA visualization methods. Semantic probes are visual user centred objects which extract and organize reduced Galois sub-hierarchies. They are simpler, clearer, and they provide a better navigation support through a rich set of interaction possibilities. Since probe driven sub-hierarchies are limited to users focus, scalability is under control and interpretation is facilitated. After some successful experiments, several applications are being developed with the remaining problem of finding a compromise between simplicity and conceptual expressivity

Supporting scientific knowledge discovery with extended, generalized Formal Concept Analysis

In this paper we fuse together the Landscapes of Knowledge of Wille's and Exploratory Data Analysis by leveraging Formal Concept Analysis (FCA) to support data-induced scientific enquiry and discovery. We use extended FCA first by allowing K-valued entries in the incidence to accommodate other, non-binary types of data, and second with different modes of creating formal concepts to accommodate diverse conceptualizing phenomena. With these extensions we demonstrate the versatility of the Landscapes of Knowledge metaphor to help in creating new scientific and engineering knowledge by providing several successful use cases of our techniques that support scientific hypothesis-making and discovery in a range of domains: semiring theory, perceptual studies, natural language semantics, and gene expression data analysis. While doing so, we also capture the affordances that justify the use of FCA and its extensions in scientific discovery.FJVA and AP were partially supported by EUFP7 project LiMo- SINe (contract288024) for this research. CPM was partially supported by the Spanish Ministry of Economics and Competitiveness projects TEC2014-61729-EXP and TEC2014-53390-P

Sequence and Emphasis in Automated Domain-Independent Discourse Generation

For humans to gain comprehensive views of large amounts of repository contents, they need to have insight into the relations among information objects. It is a challenge to automatically generate presentations of repository contents, through, for example, search results, which reveal such relations to readers. Such presentations must reflect properties of information objects such that large sets of information objects appear as a coherent whole. An approach to this is generation of discourse structures that convey such properties of information objects in presentations. Semantic Web technology provides a conceptual basis for generation of discourse in Web-based information environments. This paper describes automatic generation of sequence and emphasis in presentations of information objects. It shows generation of object sequences and emphasis in accordance with a user input of relevance of information attributes in our Topia architecture. The resulting presentations allow users to encounter informati

Sequence and emphasis in automated domain-independent discourse generation

For humans to gain comprehensive views of large amounts of repository contents, they need to have insight into the relations among information objects. It is a challenge to automatically generate presentations of repository contents, through, for example, search results, which reveal such relations to readers. Such presentations must reflect properties of information objects such that large sets of information objects appear as a coherent whole. An approach to this is generation of discourse structures that convey such properties of information objects in presentations. Semantic Web technology provides a conceptual basis for generation of discourse in Web-based information environments. This paper describes automatic generation of sequence and emphasis in presentations of information objects. It shows generation of object sequences and emphasi

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