Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

Knowledge-based acquisition of tradeoff preferences of negotiating agents

A wide range of algorithms have been developed for various types of automated egotiation. In developing such algorithms the main focus has been on their efficiency and their effectiveness. However, this is only part of the picture. Agents typically negotiate on behalf of their owners and for this to be effective the agent must be able to adequately represent the owners' preferences. However, the process by which such knowledge is acquired is typically left unspecified. To remove this shortcoming, we present a case study indicating how the knowledge for a particular negotiation algorithm can be acquired. More precisely, according to the analysis on the automated negotiation model, we identified that user trade-off preferences play a fundamental role in negotiation in general. This topic has been addressed little in the research area of user preference elicitation for general decision making problems as well. In a previous paper, we proposed an exhaustive method to acquire user trade-off preferences. In this paper, we developed another method to remove the limitation of the high user workload of the exhaustive method. Although we cannot say that it can exactly capture user trade-off preferences, it models the main commonalities of trade-off relations and re users' individualities as well

A Framework for Reference Management in the Semantic Web

Much of the semantic web relies upon open and unhindered interoperability between diverse systems. The successful convergence of multiple ontologies and referencing schemes is key. This is hampered by a lack of any means for managing and communicating co-references. We have therefore developed an ontology and framework for the exploration and resolution of potential co-references, in the semantic web at large, that allow the user to a) discover and record uniquely identifying attributes b) interface candidates with and create pipelines of other systems for reference management c) record identified duplicates in a usable and retrievable manner, and d) provide a consistent reference service for accessing them. This paper describes this ontology and a framework of web services designed to support and utilise it

The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

Investigating the use of semantic technologies in spatial mapping applications

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend

A Critique of the Use of the Balanced Scorecard in Multi-Enterprise Family Farm Businesses

Business strategy is very important to small and medium family businesses as many are both fragile and vulnerable; strategy provides a solid foundation for survival. Various studies have identified that businesses that engage in strategic management outperform those that do not. Despite this knowledge the uptake of many aspects of strategic management by farm businesses has been slow. Although the development of business plans is now common there is often a disconnect between monitoring and strategy. The Balanced Scorecard (BSC) was applied to case study farms during both the planning process and as they implemented and controlled their strategic choices to determine areas of difference that restrict or enhance it as a management tool for both family and farming businesses. The BSC was immediately applicable in the strategic management process for those businesses with current business plans. It could be used to test the degree of balance between the goals already identified in their plans. It was able to be used to critique the control measures they had in place and to determine how well they could be used to derive the causal chain from the operational level to family goals. In some instances either outcome or driver measures were recognized as being missing, in others the wiring within the balanced scorecard revealed some strategic measures without linkages.Farm Management,

A STUDY OF SUBCLINICAL MASTITIS IN TWO HERDS, ONE MANAGED ORGANICALLY, THE OTHER CONVENTIONALLY, AND THE EFFECT OF DIFFERENT MANAGEMENT STRATEGIES

Mastitis in two herds managed as a comparison between organic and conventional dairy farming systems was monitored for 9 years utilising regular bacterial culture of milk samples, individual and bulk somatic cell counts and observation by farm staff. The most important isolates in pure cultures were coagulase-negative staphylococci, Staphylococcus aureus, Streptococcus uberis, and Bacillus spp. Positive cultures were generally not associated with subclinical mastitis. The objective was to develop strategies for the control of mastitis in organic cows without the use of antibiotics. Within a set of key control measures, two management strategies were trialled, one of which reduced the prevalence of subclinical mastitis to very low levels

The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility

This paper proposes that two significant and emerging problems facing our connected, data-driven society may be more effectively solved by being framed as sensemaking challenges. The first is in empowering individuals to take control of their privacy, in device-rich information environments where personal information is fed transparently to complex networks of information brokers. Although sensemaking is often framed as an analytical activity undertaken by experts, due to the fact that non-specialist end-users are now being forced to make expert-like decisions in complex information environments, we argue that it is both appropriate and important to consider sensemaking challenges in this context. The second is in supporting human-in-the-loop algorithmic decision-making, in which important decisions bringing direct consequences for individuals, or indirect consequences for groups, are made with the support of data-driven algorithmic systems. In both privacy and algorithmic decision-making, framing the problems as sensemaking challenges acknowledges complex and illdefined problem structures, and affords the opportunity to view these activities as both building up relevant expertise schemas over time, and being driven potentially by recognition-primed decision making