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

Enterprise Engineering theories -- Introduction and overview

In order to illustrate the basic idea of the Ciao! Network concerning the development of the discipline of enterprise engineering (EE), the tree metaphor is presented. The roots of the tree are the theories, the trunk contains the methodologies built on these roots, and the leafs and flowers stand for the flourishing enterprises that are achieved by applying the methodologies. The common theoretical basis for establishing EE, is the Ciao! paradigm that has its origins in the communication-centric view on information systems (engineering) that emerged around 2000. It replaces the information-centric view, which increasingly fails to support the theory and practice of information systems engineering effectively. The Ciao! paradigm provides a~coherent and integrated understanding of these four core notions: communication, information, action, and organisation. After the discussion of the paradigm, the current seven EE theories are discussed briefly, after having been ordered in an appropriate classification scheme

Embedding Non-Ground Logic Programs into Autoepistemic Logic for Knowledge Base Combination

In the context of the Semantic Web, several approaches to the combination of ontologies, given in terms of theories of classical first-order logic and rule bases, have been proposed. They either cast rules into classical logic or limit the interaction between rules and ontologies. Autoepistemic logic (AEL) is an attractive formalism which allows to overcome these limitations, by serving as a uniform host language to embed ontologies and nonmonotonic logic programs into it. For the latter, so far only the propositional setting has been considered. In this paper, we present three embeddings of normal and three embeddings of disjunctive non-ground logic programs under the stable model semantics into first-order AEL. While the embeddings all correspond with respect to objective ground atoms, differences arise when considering non-atomic formulas and combinations with first-order theories. We compare the embeddings with respect to stable expansions and autoepistemic consequences, considering the embeddings by themselves, as well as combinations with classical theories. Our results reveal differences and correspondences of the embeddings and provide useful guidance in the choice of a particular embedding for knowledge combination.Comment: 52 pages, submitte

A New Bidirectional Method for Ontologies Matching

AbstractRecently, much research has focused on developing techniques for schema/ontology matching and mapping as it is required in many areas e.g. heterogeneous database integration, merging of ontologies, semantic query processing. In this paper, we present a novel method to detect and repair the list of homologs concepts; this last is based on the bidirectional comparison (Checking) between the two matched ontologies to filter this list of concepts. The used measures are classical just to show the reliability and realisability of our method, but we can generalize it for supporting other advanced measures

Value Operation: Linking Value in New Business Model Creation Process

Enterprise engineering is a discipline concerning designing and modeling an enterprise system. On creating a new business model, we can start from scratch ideation, or conduct innovation, manipulation, etc. of the current, existing business model. New Business Model Creation Process is a framework to conduct business model manipulation to create a new business. However, one of the seemingly important aspects of business is lost: Value. This research attempts to create a framework of Value Operation and link it into New Business Model Creation Process, using the concept of e3value. This paper will explain the literature review related to this work, the methodology, the demonstration of this framework, discussion of the result and the conclusion of this research

Automatic, context-specific generation of Gene Ontology slims

Background: The use of ontologies to control vocabulary and structure annotation has added value to genome-scale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, high-level terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are time-consuming and largely manual