An Intelligent Help-Desk Framework for Effective Troubleshooting

Nowadays, technological infrastructure requires an intelligent virtual environment based on decision processes. These processes allow the coordination of individual elements and the tasks that connect them. Thus, incident resolution must be efficient and effective to achieve maximum productivity. In this paper, we present the design and implementation of an intelligent decision-support system applied in technology infrastructure at the University of Seville (Spain). We have used a Case Based Reasoning (CBR) methodology and an ontology to develop an intelligent system for supporting expert diagnosis and intelligent management of incidents. This is an innovative and interdisciplinary approach to knowledge management in problem-solving processes that are related to environmental issues. Our system provides an automatic semantic indexing for the generating of question/answer pairs, a case based reasoning technique for finding similar questions, and an integration of external information sources via ontologies. A real ontology-based question/answer platform named ExpertSOS is presented as a proof of concept. The intelligent diagnosis platform is able to identify and isolate the most likely cause of infrastructure failure in case of a faulty operation

Ontology modeling for generation of clinical pathways

Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP) as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM) is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM) is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the behavior will occur. To overcome this weakness, a semiotic approach to generation of clinical pathway is introduced. The CP generated from SAM together with norms will enrich the knowledge representation of the domain through ontology modeling, which allows the recognition of human responsibilities and obligations and more importantly, the ultimate power of decision making in exceptional circumstances

Business and logic charateristic in an collaborative situation

MISE 2.0 (for Mediation Information System Engineering, second iteration) project has been launched in 2009. The MISE 2.0 engineering approach is based on BPM (Business Process Management) and MDE (Model-Driven Engineering). Running a regular BPM approach on a specific organization consists in gathering structural, informational, and functional knowledge in order to design cartography of processes covering the behavior of the modeled organization. Regarding the MISE 2.0 project the BPM approach concerns a set of organizations and MDE helps in automatizing the different steps: i) Knowledge gathering (situation layer): collect information concerning the collaborative situation, ii) Processes cartography design (solution layer): design the processes according to the knowledge gathered and iii) MIS deployment (implementation layer): implement an IT structure able to run the processes cartography. Both the transitions between these layers are the hard-points of this approach: The first gap is managed at the abstract level of MISE 2.0 while the second one is managed at the concrete level of MISE 2.0. The current PhD is focused on the first issue: designing a relevant processes cartography from the modeled collaborative situation. However, this is usually a manual activity, which requires a large amount of work to draw the processes and their links. The current research works aim at building such collaborative process cartography in an automated manner. Our principles are (i) to gather the essential and minimum initial collaborative knowledge (e.g. partners, shared functions and collaborative objectives) in models, ii) to deduce the missing knowledge with the help of a collaborative metamodel, an associated ontology and transformation rules and iii) to structure the deduced knowledge in a collaborative process cartography thanks to dedicated algorithms

A framework for developing engineering design ontologies within the aerospace industry

This paper presents a framework for developing engineering design ontologies within the aerospace industry. The aim of this approach is to strengthen the modularity and reuse of engineering design ontologies to support knowledge management initiatives within the aerospace industry. Successful development and effective utilisation of engineering ontologies strongly depends on the method/framework used to develop them. Ensuring modularity in ontology design is essential for engineering design activities due to the complexity of knowledge that is required to be brought together to support the product design decision-making process. The proposed approach adopts best practices from previous ontology development methods, but focuses on encouraging modular architectural ontology design. The framework is comprised of three phases namely: (1) Ontology design and development; (2) Ontology validation and (3) Implementation of ontology structure. A qualitative research methodology is employed which is composed of four phases. The first phase defines the capture of knowledge required for the framework development, followed by the ontology framework development, iterative refinement of engineering ontologies and ontology validation through case studies and experts’ opinion. The ontology-based framework is applied in the combustor and casing aerospace engineering domain. The modular ontologies developed as a result of applying the framework and are used in a case study to restructure and improve the accessibility of information on a product design information-sharing platform. Additionally, domain experts within the aerospace industry validated the strengths, benefits and limitations of the framework. Due to the modular nature of the developed ontologies, they were also employed to support other project initiatives within the case study company such as role-based computing (RBC), IT modernisation activity and knowledge management implementation across the sponsoring organisation. The major benefit of this approach is in the reduction of man-hours required for maintaining engineering design ontologies. Furthermore, this approach strengthens reuse of ontology knowledge and encourages modularity in the design and development of engineering ontologies

Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation

Extracting Implicit Knowledge and Inter-relationships from Initial Public Offering (IPO) Prospectus for Pricing Prediction

Initial Public Offering (IPO) process and the associated pricing strategies are of much interest to researchers and practitioners (e.g., underwriters, investors) in the finance and accounting domains. IPO prospectuses, regulated by Security Exchange Committee (SEC), serve as the most reliable publicly available information source in the IPO process. IPO prospectuses disclose a variety of information; however, traditional studies do not leverage the rich knowledge hidden in the vast textual information within them. The research gap can be partially attributed to the lack of an underlying formal knowledge structure to support the extraction of the implicit knowledge from the prospectuses, as well as to the absence of quantitative metrics that reflect management\u27s outlook and awareness embedded in the prospectuses. The primary research question addressed in this work is: How do the management\u27s awareness of risks (expressed via the emphasized mentions in the Risk Factors sections of the Form 424 filings), and confidence about the firm\u27s outlook (expressed through the sentiments in the MD&A sections) affect IPO valuations? The major research problem could be further broken down into two research goals: a) to develop an actionable knowledge structure for guiding the extraction, storing the results, and facilitate reasoning of the knowledge hidden in the textual content of the IPO prospectuses; and b) to utilize the knowledge structure developed above, as well as the predictive models, to estimate pricing volatility prior to and right after the IPO date. In order to identify and quantify such inter­relationships, an underlying knowledge structure needs to be constructed and updated with minimal manual interventions for efficient knowledge acquisition and accurate knowledge representation purposes. In this dissertation, to bridge aforementioned research gaps, I proposed a text analytics framework for assisting the investment and underwriting decision making processes. Two major components existing in the proposed framework, namely the ontology enrichment methodology that updates the ontology in real time and online mode, and the predictive modeling techniques using the extracted information based on the ontology for predicting IPO pricing. The proposed framework is then developed in the form of a research prototype, which is used to predict pricing trends during and after the IPO process. I use real world data to evaluate the framework itself as well as the prediction results through a set of experiments, which yield promising results. Design science research methodology is applied as the methodological framework in this study. Two motivational scenarios are provided to illustrate the significance and relevance of this study. The searching and developing process of a solution is documented in detail. I have compared our approach to the existing body of research and illustrated its novelty. Further, I have evaluated the proposed IT artifacts (the analytical framework), first through feasibility and functionality testing and second through an experimental approach for analyzing efficiency and accuracy. The proposed analytical framework is evaluated by various means. First, a case study is designed to evaluate the functionalities and efficiencies of the framework. Second, the practical relevance of the framework is evaluated through the results of the predictive models. Third, the design artifacts are also evaluated against the design requirements drawn from existing literature. The evaluation results in this study are satisfying, which indicate the promising prospects of this project in practice. There are two key research contributions of this work: a) an (semi-) automatic approach for enriching the specifications of domain knowledge bases (i.e. ontologies) is developed and evaluated, as an underlying knowledge structure for the analytical process. The approach is unique in the sense of incorporating feature-based word sense disambiguation and relation extraction methods in the process; b) several predictive models are designed based on extracted knowledge from the prospectus, for the purpose of predicting pre- and post-IPO pricing volatility. The results of this phase of the study ensure its practical relevance. In addition to these two primary contributions, two metrics are also designed, as a proxy of the management\u27s awareness of risks and management\u27s confidence regarding the organization\u27s future operations. This metrics are based on the textual contents in the more informative sections in the prospectuses (i.e. Risk Factors, Management Discussions and Analysis) and to the best of our knowledge, these metrics are the first of its kind to quantify such information. Further, the analytical framework and development approaches of the design artifacts can be adopted in other application domains such as healthcare informatics, social media analysis, and so forth

Overview of methodologies for building ontologies

A few research groups are now proposing a series of steps and methodologies for developing ontologies. However, mainly due to the fact that Ontological Engineering is still a relatively immature discipline, each work group employs its own methodology. Our goal is to present the most representative methodologies used in ontology development and to perform an analysis of such methodologies against the same framework of reference. So, the goal of this paper is not to provide new insights about methodologies, but to put it all in one place and help people to select which methodology to use

Ontology-based patterns for the integration of business processes and enterprise application architectures

Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data. Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their applicability in business process-driven application integration is demonstrated

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

An Ontology for Product-Service Systems

Industries are transforming their business strategy from a product-centric to a more service-centric nature by bundling products and services into integrated solutions to enhance the relationship between their customers. Since Product- Service Systems design research is currently at a rudimentary stage, the development of a robust ontology for this area would be helpful. The advantages of a standardized ontology are that it could help researchers and practitioners to communicate their views without ambiguity and thus encourage the conception and implementation of useful methods and tools. In this paper, an initial structure of a PSS ontology from the design perspective is proposed and evaluated