An ontological view in telemedicine.

The verification and validation of information system models impact on the adequacy and appropriateness of using the value of telemedicine services for continuously optimizing healthcare outcomes. We have defined a methodology to help the modeling and rigorous analysis of the requirements of information systems in telemedicine. On one hand, this methodology will be based on a formal representation of requirements (systemic, generic domain, etc.) within a knowledge base that will be a requirements repository. On the other hand, this methodology will use conceptual graphs for the formalization of ontology of activities and the production of arguments related to the formal verification of models built from this ontology. We describe an example illustrating the engagement of conceptual graph procedures to model the contextual situations in the telemedicine development. We also discuss the way in which ethical issues will actually take place in telemedicine applications

The ERA of FOLE: Superstructure

This paper discusses the representation of ontologies in the first-order logical environment FOLE (Kent 2013). An ontology defines the primitives with which to model the knowledge resources for a community of discourse (Gruber 2009). These primitives, consisting of classes, relationships and properties, are represented by the ERA (entity-relationship-attribute) data model (Chen 1976). An ontology uses formal axioms to constrain the interpretation of these primitives. In short, an ontology specifies a logical theory. This paper is the second in a series of three papers that provide a rigorous mathematical representation for the ERA data model in particular, and ontologies in general, within the first-order logical environment FOLE. The first two papers show how FOLE represents the formalism and semantics of (many-sorted) first-order logic in a classification form corresponding to ideas discussed in the Information Flow Framework (IFF). In particular, the first paper (Kent 2015) provided a "foundation" that connected elements of the ERA data model with components of the first-order logical environment FOLE, and this second paper provides a "superstructure" that extends FOLE to the formalisms of first-order logic. The third paper will define an "interpretation" of FOLE in terms of the transformational passage, first described in (Kent 2013), from the classification form of first-order logic to an equivalent interpretation form, thereby defining the formalism and semantics of first-order logical/relational database systems (Kent 2011). The FOLE representation follows a conceptual structures approach, that is completely compatible with Formal Concept Analysis (Ganter and Wille 1999) and Information Flow (Barwise and Seligman 1997)

The ERA of FOLE: Foundation

This paper discusses the representation of ontologies in the first-order logical environment FOLE (Kent 2013). An ontology defines the primitives with which to model the knowledge resources for a community of discourse (Gruber 2009). These primitives, consisting of classes, relationships and properties, are represented by the entity-relationship-attribute ERA data model (Chen 1976). An ontology uses formal axioms to constrain the interpretation of these primitives. In short, an ontology specifies a logical theory. This paper is the first in a series of three papers that provide a rigorous mathematical representation for the ERA data model in particular, and ontologies in general, within the first-order logical environment FOLE. The first two papers show how FOLE represents the formalism and semantics of (many-sorted) first-order logic in a classification form corresponding to ideas discussed in the Information Flow Framework (IFF). In particular, this first paper provides a foundation that connects elements of the ERA data model with components of the first-order logical environment FOLE, and the second paper provides a superstructure that extends FOLE to the formalisms of first-order logic. The third paper defines an interpretation of FOLE in terms of the transformational passage, first described in (Kent 2013), from the classification form of first-order logic to an equivalent interpretation form, thereby defining the formalism and semantics of first-order logical/relational database systems (Kent 2011). The FOLE representation follows a conceptual structures approach, that is completely compatible with formal concept analysis (Ganter and Wille 1999) and information flow (Barwise and Seligman 1997)

A semantic framework for ontology usage analysis

The Semantic Web envisions a Web where information is accessible and processable by computers as well as humans. Ontologies are the cornerstones for realizing this vision of the Semantic Web by capturing domain knowledge by defining the terms and the relationship between these terms to provide a formal representation of the domain with machine-understandable semantics. Ontologies are used for semantic annotation, data interoperability and knowledge assimilation and dissemination.In the literature, different approaches have been proposed to build and evolve ontologies, but in addition to these, one more important concept needs to be considered in the ontology lifecycle, that is, its usage. Measuring the “usage” of ontologies will help us to effectively and efficiently make use of semantically annotated structured data published on the Web (formalized knowledge published on the Web), improve the state of ontology adoption and reusability, provide a usage-based feedback loop to the ontology maintenance process for a pragmatic conceptual model update, and source information accurately and automatically which can then be utilized in the other different areas of the ontology lifecycle. Ontology Usage Analysis is the area which evaluates, measures and analyses the use of ontologies on the Web. However, in spite of its importance, no formal approach is present in the literature which focuses on measuring the use of ontologies on the Web. This is in contrast to the approaches proposed in the literature on the other concepts of the ontology lifecycle, such as ontology development, ontology evaluation and ontology evolution. So, to address this gap, this thesis is an effort in such a direction to assess, analyse and represent the use of ontologies on the Web.In order to address the problem and realize the abovementioned benefits, an Ontology Usage Analysis Framework (OUSAF) is presented. The OUSAF Framework implements a methodological approach which is comprised of identification, investigation, representation and utilization phases. These phases provide a complete solution for usage analysis by allowing users to identify the key ontologies, and investigate, represent and utilize usage analysis results. Various computation components with several methods, techniques, and metrics for each phase are presented and evaluated using the Semantic Web data crawled from the Web. For the dissemination of ontology-usage-related information accessible to machines and humans, The U Ontology is presented to formalize the conceptual model of the ontology usage domain. The evaluation of the framework, solution components, methods, and a formalized conceptual model is presented, indicating the usefulness of the overall proposed solution

The FOLE Table

This paper continues the discussion of the representation of ontologies in the first-order logical environment FOLE. According to Gruber, an ontology defines the primitives with which to model the knowledge resources for a community of discourse. These primitives, consisting of classes, relationships and properties, are represented by the entity-relationship-attribute ERA data model of Chen. An ontology uses formal axioms to constrain the interpretation of these primitives. In short, an ontology specifies a logical theory. A series of three papers by the author provide a rigorous mathematical representation for the ERA data model in particular, and ontologies in general, within FOLE. The first two papers, which provide a foundation and superstructure for FOLE, represent the formalism and semantics of (many-sorted) first-order logic in a classification form corresponding to ideas discussed in the Information Flow Framework (IFF). The third paper will define an interpretation of FOLE in terms of the transformational passage, first described in (Kent, 2013), from the classification form of first-order logic to an equivalent interpretation form, thereby defining the formalism and semantics of first-order logical/relational database systems. Two papers will provide a precise mathematical basis for FOLE interpretation: the current paper develops the notion of a FOLE relational table following the relational model of Codd, and a follow-up paper will develop the notion of a FOLE relational database. Both of these papers expand on material found in the paper (Kent, 2011). Although the classification form follows the entity-relationship-attribute data model of Chen, the interpretation form follows the relational data model of Codd. In general, the FOLE representation uses a conceptual structures approach, that is completely compatible with formal concept analysis and information flow.Comment: 48 pages, 21 figures, 9 tables, submitted to T.A.C. for review in August 201

The Role of Application Domain Knowledge in Using OWL DL Diagrams: A Study of Inference and Problem-Solving Tasks

Diagrammatic conceptual schemas are an important part of information systems analysis and design. For effectively communicating domain semantics, modeling grammars have been proposed to create highly expressive conceptual schemas. One such grammar is the Web Ontology Language (OWL), which relies upon description logics (DL) as a knowledge representation mechanism. While an OWL DL diagram can be useful for representing domain semantics in great detail, the formal semantics of OWL DL places a burden on diagram users. This research investigates how user’s prior knowledge of the application domain impacts solving inference tasks as well as schema-based problem-solving tasks using OWL DL diagrams. Our empirical validation shows that application domain knowledge has no effect on inference performance but enhances schema-based problem-solving performance. We contribute to the conceptual modeling literature by studying task performance for a highly expressive modeling grammar and introducing inference tasks as a new task type

Terminology and Knowledge Representation. Italian Linguistic Resources for the Archaeological Domain

Knowledge representation is heavily based on using terminology, due to the fact that many terms have precise meanings in a specific domain but not in others. As a consequence, terms becomes unambiguous and clear, and at last, being useful for conceptualizations, are used as a starting point for formalizations. Starting from an analysis of problems in existing dictionaries, in this paper we present formalized Italian Linguistic Resources (LRs) for the Archaeological domain, in which we integrate/couple formal ontology classes and properties into/to electronic dictionary entries, using a standardized conceptual reference model. We also add Linguistic Linked Open Data (LLOD) references in order to guarantee the interoperability between linguistic and language resources, and therefore to represent knowledge

A Knowledge-Oriented Approach to Enhance Integration and Communicability in the Polkadot Ecosystem

The Polkadot ecosystem is a disruptive and highly complex multi-chain architecture that poses challenges in terms of data analysis and communicability. Currently, there is a lack of standardized and holistic approaches to retrieve and analyze data across parachains and applications, making it difficult for general users and developers to access ecosystem data consistently. This paper proposes a conceptual framework that includes a domain ontology called POnto (a Polkadot Ontology) to address these challenges. POnto provides a structured representation of the ecosystem's concepts and relationships, enabling a formal understanding of the platform. The proposed knowledge-oriented approach enhances integration and communicability, enabling a wider range of users to participate in the ecosystem and facilitating the development of AI-based applications. The paper presents a case study methodology to validate the proposed framework, which includes expert feedback and insights from the Polkadot community. The POnto ontology and the roadmap for a query engine based on a Controlled Natural Language using the ontology, provide valuable contributions to the growth and adoption of the Polkadot ecosystem in heterogeneous socio-technical environments

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable