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

Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems

Strategic Assessment of Organizational Commitment

The concept of organizational commitment has been widely studied over recent decades, yet it remains one of the most challenging concepts in organizational research. While commitment is understood to be highly valuable in today’s dynamic business environment, its multifaceted nature is not necessarily understood adequately. The purpose of this study was to examine the concept of organizational commitment and its measurement issues within organizations, and to develop a practical evaluation tool for management, which is based on previous scientific research. First, a theoretical framework discussing organizational commitment and engagement was established. Based on the literature research, three ontologies were developed addressing organizational commitment and engagement, as well as academic engagement. The ontologies were constructed as a synthesis of existing theories. With the help of the ontologies and the created evaluation system, it is possible to better understand these concepts, gain a collective view of the organization’s current state and vision for the future, and to open a dialogue between members of the organization regarding their development. The results of the empirical case studies are presented at the end of this thesis, as well as in the attached research papers. The empirical results indicate that, by using these applications, it is possible to gain insights about the respondents’ feelings and aspirations, which can be used to support effective decision-making and as the basis for creating development actions within the organization.Organisaatiositoutumisen käsitettä on tutkittu laajasti kuluneiden vuosikymmenten aikana, kuitenkin se on edelleen yksi organisaatiotutkimuksen haastavimmista käsitteistä. Sitoutuminen on laajalti ymmärretty erittäin tärkeäksi tämän päivän liiketoimintaympäristössä mutta sen moniulotteista luonnetta ei yrityksissä ole välttämättä ymmärretty riittävästi. Tämän tutkimuksen tavoitteena oli tarkastella organisatorisen sitoutumisen käsitettä ja sen mittaamisen ongelmallisuutta sekä kehittää aikaisempaan tieteelliseen tutkimukseen perustuva käytännön sovellus sitoutumisen tason määrittämiseksi. Tutkimuksen ensimmäisessä osassa laadittiin organisaatioon sitoutumista käsittelevä teoreettinen viitekehys, jonka perusteella kehitettiin kolme ontologiaa. Ontologiat käsittelevät organisaation sitoutumista eri näkökulmista sekä opiskelijoiden akateemista sitoutumista. Ontologioiden sekä laaditun arviointijärjestelmän avulla on mahdollista ymmärtää sitoutumiseen liittyviä käsitteitä, saada yhteinen näkemys organisaation nykytilasta ja tulevaisuuden näkemyksestä sekä löytää mahdollisia kehityskohteita. Empiiristen case-tutkimusten tuloksia on esitetty tämän työn loppuosassa sekä liitteenä olevissa tutkimusartikkeleissa. Tulokset osoittavat, että laadittujen sovellusten avulla on mahdollista saada tietoa vastaajien tuntemuksista ja pyrkimyksistä. Tätä tietoa voidaan hyödyntää päätöksenteon tukena sekä perustana kehitystoimien luomiselle.fi=vertaisarvioitu|en=peerReviewed

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Interpreting, Representing and Integrating Scientific Knowledge from Interdisciplinary Projects

We describe a coherent, eclectic approach to interpreting, representing, and integrating knowledge from different scientific disciplines or communities of practice. The approach, called ECLECTIC, draws from a complementary blend of ethnological methods, the hermeneutic analysis of domains, and ecology. Our description focuses on the conceptual bases of this approach, its value, and uses, particularly in handling the methodological considerations in the overlapping phases of interpretation, representation, and integration. We give examples from our use of the approach and describe how it handles difficult methodological issues: (1) knowing what questions to initially ask of members of science communities, (2) identifying their states of knowledge, (3) determining the analyst’s role, (4) determining how the knowledge may be self elicited by the members themselves, (5) verifying that the interpretation and representation of the knowledge is meaningful to the members, and (6) integrating differing representations from the communities