Integrating textual analysis and evidential reasoning for decision making in Engineering design

Decision making is an important element throughout the life-cycle of large-scale projects. Decisions are critical as they have a direct impact upon the success/outcome of a project and are affected by many factors including the certainty and precision of information. In this paper we present an evidential reasoning framework which applies Dempster-Shafer Theory and its variant Dezert-Smarandache Theory to aid decision makers in making decisions where the knowledge available may be imprecise, conflicting and uncertain. This conceptual framework is novel as natural language based information extraction techniques are utilized in the extraction and estimation of beliefs from diverse textual information sources, rather than assuming these estimations as already given. Furthermore we describe an algorithm to define a set of maximal consistent subsets before fusion occurs in the reasoning framework. This is important as inconsistencies between subsets may produce results which are incorrect/adverse in the decision making process. The proposed framework can be applied to problems involving material selection and a Use Case based in the Engineering domain is presented to illustrate the approach. © 2013 Elsevier B.V. All rights reserved.

England, Neo-Latin, and the Continental Journey: Linguistic and Textual Itineraries

This chapter examines the ramifications of continental travel and associated epistolary communication for English poets of the period. It argues that recourse to neo-Latin, the universal language of diplomacy, served not only to establish a sense of shared space—linguistic, cultural, generic—between England and the continent, but also to signal self-conscious differences (climatic, geographical, historical, political) between England and her continental peers. Through an investigation of a range of ‘performances’ on stages that were ‘academic’, poetic, autobiographical, and epistolographic, it assesses the central role of neo-Latin as a language that underwent a series of textual itineraries. These ‘itineraries’ manifest themselves in a number of ways. Neo-Latin as a shared linguistic medium can facilitate, and quite uniquely so, intertextual engagement with the classics, but now ancient Rome, its language, its mythology, its hierarchy of genres, are viewed through a seventeenth-century lens and appropriated by poets in both England and Italy to describe contemporary events, whether personal, or political. Close examination of the neo-Latin poetry of Milton and Marvell reveals, it is argued, a self-fashioning coloured by such textual itineraries and interchanges. The absorption and replication of continental literary and linguistic methodologies (the academic debate; the etymological play of Marinism; the hybridity of neo-Latin and Italian voices) reveal in short a linguistic and textual reciprocity that gave birth to something very new.

Studying administrative reforms through textual analysis: the case of Italian central government accounting

This paper contributes to the literature on public-sector reforms by proposing textual analysis as a useful research strategy to explore how reform archetypes and related ideas are deployed in the parliamentary debate and regulations advancing reforms. Public Administration (PA) (Wilson 1887; Weber 1922), New Public Management (NPM) (Hood 1991, 1995; Dunleavy and Hood 1994; Ferlie et al. 1996) and Public Governance (GOV) (Osborne 2010; Rhodes 1997) can be depicted as three different archetypes providing characteristic administrative ideas and concepts (i.e. interpretive schemes) and related tools and practices (i.e. structures and systems) which lead reforms. We use textual analysis to look into more than twenty years of Italian central government accounting reforms and investigate how the three administrative archetypes have evolved, intertwined and replaced each other. Textual analysis proves a useful tool to investigate reform processes and allows highlighting that in neo-Weberian countries, such as Italy, NPM and GOV, far from being revolutionary paradigms, may represent fashionable trends that did not leave significant traces in the practice and rhetoric of reforms. These results also suggest interesting implications for practitioners and policy makers.

Query Suggestions for Textual Problem Solution Repositories

Textual problem-solution repositories are available today in
various forms, most commonly as problem-solution pairs from community
question answering systems. Modern search engines that operate on
the web can suggest possible completions in real-time for users as they
type in queries. We study the problem of generating intelligent query
suggestions for users of customized search systems that enable querying
over problem-solution repositories. Due to the small scale and specialized
nature of such systems, we often do not have the luxury of depending on
query logs for finding query suggestions. We propose a retrieval model
for generating query suggestions for search on a set of problem solution
pairs. We harness the problem solution partition inherent in such
repositories to improve upon traditional query suggestion mechanisms
designed for systems that search over general textual corpora. We evaluate
our technique over real problem-solution datasets and illustrate that
our technique provides large and statistically significant

Knowledge Management Techniques to Augment Textual CBR (PhD Thesis)

The past decade had witnessed an unprecedented growth in the amount of available digital content, and its volume is expected to continue to grow the next few years. Unstructured text data generated from web and enterprise sources form a large fraction of such content. Many of these contain large volumes of reusable data such as solutions to frequently occurring problems, and general know-how that may be reused in appropriate contexts. In this work, we address issues around leveraging unstructured text data from sources as diverse as the web and the enterprise within the Case-based Reasoning framework. Case-based Reasoning (CBR) provides a framework and methodology for systematic reuse of historical knowledge that is available in the form of problemsolution
pairs, in solving new problems. Here, we consider possibilities of enhancing Textual CBR systems under three main themes: procurement, maintenance and retrieval. We adapt and build upon the stateof-the-art techniques from data mining and natural language processing in addressing various challenges therein. Under procurement, we investigate the problem of extracting cases (i.e., problem-solution pairs) from data sources such as incident/experience
reports. We develop case-base maintenance methods specifically tuned to text targeted towards retaining solutions such that the utility of the filtered case base in solving new problems is maximized. Further, we address the problem of query suggestions for textual case-bases and show that exploiting the problem-solution partition can enhance retrieval effectiveness by prioritizing more useful query suggestions. Additionally, we illustrate interpretable clustering as a tool to drill-down to domain specific text collections (since CBR systems are usually very domain specific) and develop techniques for improved similarity assessment in social media sources such as microblogs. Through extensive empirical evaluations, we illustrate the improvements that we are able to
achieve over the state-of-the-art methods for the respective tasks.