An ontological approach for recovering legacy business content

Legacy Information Systems (LIS) pose a challenge for many organizations. On one hand, LIS are viewed as aging systems needing replacement; on the other hand, years of accumulated business knowledge have made these systems mission-critical. Current approaches however are often criticized for being overtly dependent on technology and ignoring the business knowledge which resides within LIS. In this light, this paper proposes a means of capturing the business knowledge in a technology agnostic manner and transforming it in a way that reaps the benefits of clear semantic expression - this transformation is achieved via the careful use of ontology. The approach called Content Sophistication (CS) aims to provide a model of the business that more closely adheres to the semantics and relationships of objects existing in the real world. The approach is illustrated via an example taken from a case study concerning the renovation of a large financial system and the outcome of the approach results in technology agnostic models that show improvements along several dimensions

The P3 platform: an approach and software system for developing diagrammatic model-based methods in design research

Many issues in design and design management have been explored by building models which capture the relationships between different aspects of the problem at hand. These models require computer support to construct and analyse. However, appropriate modelling tools can be time-consuming to develop in a research environment. Reflecting upon five design research projects, this paper proposes that such projects can be facilitated by recognising the iterative and tightly-coupled nature of research and tool development, and by attempting to minimise the effort of solution prototyping within this process. Our approach is enabled by a software platform which can be rapidly configured to implement many conceivable modelling approaches. This configurability is complemented by an emerging library of modelling and analysis approaches tailored to explore design process systems. The platform-based approach enables any mix of modelling concepts to be easily created. We propose it could thus help researchers to explore a wide range of questions without being constrained to existing conventions for modelling – or for model integration

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

Towards a Context Knowledge Taxonomy. Combined Methodologies to Improve a Fast-Search Concept Extraction for an Ontology Population

Context in Architectural Design can be defined-related-comparable to hypothesis and boundary conditions in mathematics. An eco-system that influences it by means of natural and artificial events, space and time dimension. The research has the aim to analyze the critical issues related to Context by providing a contribution to the study of interactions between Context Knowledge and Architectural Design and how it can be used to improve the performance of the buildings and reducing design mistakes. The research focusing on formal ontologies, has developed a model that enables a semantic approach to design application programs, to manage information, to answer design questions and to have a clear relation between the formal representation of the context domain and its meanings. This context model provides an advancement on the state of the art in simplified design assumptions, in term of ontology ambiguity and complexity reduction, by using algorithms to extract and optimize branches of the graph. The extraction does not limit the number of relations, that can be extended and improve context taxonomy coherency and accuracy

Exploiting Transitivity in Probabilistic Models for Ontology Learning

Capturing word meaning is one of the challenges of natural language processing (NLP). Formal models of meaning such as ontologies are knowledge repositories used in a variety of applications. To be effectively used, these ontologies have to be large or, at least, adapted to specific domains. Our main goal is to contribute practically to the research on ontology learning models by covering different aspects of the task. We propose probabilistic models for learning ontologies that expands existing ontologies taking into accounts both corpus-extracted evidences and structure of the generated ontologies. The model exploits structural properties of target relations such as transitivity during learning. We then propose two extensions of our probabilistic models: a model for learning from a generic domain that can be exploited to extract new information in a specific domain and an incremental ontology learning system that put human validations in the learning loop. This latter provides a graphical user interface and a human-computer interaction workflow supporting the incremental leaning loop