The adoption and use of Through-life Engineering Services within UK Manufacturing Organisations

Manufacturing organisations seek ever more innovative approaches in order to maintain and improve their competitive position within the global market. One such initiative that is gaining significance is ‘through-life engineering services’. These seek to adopt ‘whole life’ service support through the greater understanding of component and system performance driven by knowledge gained from maintenance, repair and overhaul activities. This research presents the findings of exploratory research based on a survey of UK manufacturers who provide through-life engineering services. The survey findings illustrate significant issues to be addressed within the field before the concept becomes widely accepted. These include a more proactive approach to maintenance activities based on real-time responses; standardisation of data content, structure, collection, storage and retrieval protocols in support of maintenance; the development of clear definitions, ontologies and a taxonomy of through-life engineering services in support of the service delivery system; lack of understanding of component and system performance due to the presence of ‘No Fault Found’ events that skew maintenance metrics and the increased use of radio-frequency identification technology in support of maintenance data acquisition

Designing Service-Oriented Chatbot Systems Using a Construction Grammar-Driven Natural Language Generation System

Service oriented chatbot systems are used to inform users in a conversational manner about a particular service or product on a website. Our research shows that current systems are time consuming to build and not very accurate or satisfying to users. We find that natural language understanding and natural language generation methods are central to creating an e�fficient and useful system. In this thesis we investigate current and past methods in this research area and place particular emphasis on Construction Grammar and its computational implementation. Our research shows that users have strong emotive reactions to how these systems behave, so we also investigate the human computer interaction component. We present three systems (KIA, John and KIA2), and carry out extensive user tests on all of them, as well as comparative tests. KIA is built using existing methods, John is built with the user in mind and KIA2 is built using the construction grammar method. We found that the construction grammar approach performs well in service oriented chatbots systems, and that users preferred it over other systems

Towards a system redesign for better performance and customer satisfaction : a case study of the ICTS helpdesk at the University of Cape Town

Includes bibliographical references.This paper presents the findings from a study, which was carried out to investigate how the design of knowledge management systems could be improved for enhanced performance and greater customer satisfaction. The ICTS Department's helpdesk at the University of Cape Town, South Africa, was the venue for this case study. The study set out to meet the following objectives: - undertaking a knowledge acquisition strategy by carrying out a systems evaluation and analysis of the existing web-based user support system, - suggesting a knowledge representation model for an adaptive web-based user support system, and - developing and testing an online troubleshooter prototype for an improved knowledge use support system. To achieve the objectives of the study, knowledge engineering techniques were deployed on top of a qualitative research design. Questionnaires, which were supplemented by interview guides and observations, were the research tools used in gathering the data. In addition to this, a representative sample of the ICTS clientele and management was interviewed. It was discovered that poorly designed knowledge management systems cause frustration among the clientele who interact with the system. Specifically, it was found that the language used for knowledge representation plays a vital role in determining how best users can interpret knowledge items in a given knowledge domain. In other words, knowledge modelling and representation can improve knowledge representation if knowledge engineering techniques are appropriately followed in designing knowledge based systems. It was concluded that knowledge representation can be improved significantly if, firstly, the ontology technique is embraced as a mechanism of knowledge representation. Secondly, using hierarchies and taxonomies improves navigability in the knowledge structure. Thirdly, visual knowledge representation that supplements textual knowledge adds more meaning to the user, and is such a major and important technique that it can even cater for novice users

Software-based dialogue systems: Survey, taxonomy and challenges

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. The corresponding author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the inancial support of his predoctoral grant FPI-UPC. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

D1.1 DEMAND ASSESSMENT FRAMEWORK

This report proposes the initial draft of the LeADS ADS Framework composed by three major elements; identification and definition of technologies in scope; skills included under those technologies, and definition of job roles, where other skills frameworks are considered for comparison and alignment. The report summarises the first workshop held by the project with external constituencies even though the feedback will be incorporated in the final version of the framework, where the layer of job roles will be completed, and the others revised according to additional input. This framework serves as reference for the next step in LeADS: the assessment of the demand and the supply

Automatic Construction of Knowledge Graphs from Text and Structured Data: A Preliminary Literature Review

Knowledge graphs have been shown to be an important data structure for many applications, including chatbot development, data integration, and semantic search. In the enterprise domain, such graphs need to be constructed based on both structured (e.g. databases) and unstructured (e.g. textual) internal data sources; preferentially using automatic approaches due to the costs associated with manual construction of knowledge graphs. However, despite the growing body of research that leverages both structured and textual data sources in the context of automatic knowledge graph construction, the research community has centered on either one type of source or the other. In this paper, we conduct a preliminary literature review to investigate approaches that can be used for the integration of textual and structured data sources in the process of automatic knowledge graph construction. We highlight the solutions currently available for use within enterprises and point areas that would benefit from further research