Showing papers on "Knowledge acquisition published in 2008"

Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications

Abstract: The web-based e-learning system (WELS) has emerged as a new means of skill training and knowledge acquisition, encouraging both academia and industry to invest resources in the adoption of this system. Traditionally, most pre- and post-adoption tasks related to evaluation are carried out from the viewpoints of technology. Since users have been widely recognized as being a key group of stakeholders in influencing the adoption of information systems, their attitudes toward this system are pivotal. Therefore, based on the theory of multi-criteria decision making and the research products of user satisfaction from the fields of human-computer interaction and information systems, this study proposed a multi-criteria methodology from the perspective of learner satisfaction to support those evaluation-based activities taking place at the pre- and post-adoption phases of the WELS life cycle. In addition, by following this methodology, this study empirically investigated learners' perceptions of the relative importance of decision criteria. This investigation carried out a survey of college students, and the data thus obtained was then analyzed by analytic hierarchy process in order to derive an integrated preference structure of learners as a ground for evaluation. We found that learners regarded the learner interface as being the most important dimension of decision criteria. Future applications of these results are recommended and the implications are discussed.

A Delphi-based approach to developing expert systems with the cooperation of multiple experts

Abstract: Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that ''time'' is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the ''time'' factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.

Ecological knowledge is lost in wealthier communities and countries.

Abstract: Accumulated knowledge about nature is an important part of people’s capacity to manage and conserve the environment. But this ecological knowledge is now being increasingly lost. There have been few cross-cultural and quantitative studies to describe the phenomenon of its loss. Here we show a strong inverse correlation between ecological knowledge and income levels in and among India, Indonesia, and the UK (n = 1095 interviews). Knowledge acquisition and subsequent saturation occurs at an early age in the most resource-dependent communities, but not in the UK, where knowledge levels are low and acquisition is slow. Knowledge variance within communities increases in association with ecological knowledge decline and a scale of progressive knowledge loss was revealed with the most rapid rates of loss in industrialized regions. Various studies have described the mutually exclusive relationship between economic growth and environmental conservation; however this is the first to consider the association between economic growth and social capacity to manage the environment. Understanding ecological knowledge loss is important to understanding the declining capacities of communities undergoing economic development to manage their natural resources and the future of ecosystem diversity in the light of current patterns of economic growth.

Learning non-taxonomic relationships from web documents for domain ontology construction

Abstract: In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that addresses the non-taxonomic learning process for constructing domain ontologies. It is able to discover domain-related verbs, extract non-taxonomically related concepts and label relationships, using the Web as corpus. The paper also discusses how the obtained relationships can be automatically evaluated against WordNet and presents encouraging results for several domains.

The contribution of Project-based-learning to high-achievers’ acquisition of technological knowledge and skills

Abstract: The main goals of this study were to look after the technological knowledge construction process by high-school high-achievers, and their ability to design and implement solutions for technological problems. More specifically, we examine the contribution of Project-based-learning (PBL), as pedagogical means for supporting the students’ knowledge acquisition and problem-solving process. The findings show a significant increase in formal knowledge as measured by standardized matriculation exams; an expansion in the scope of technological knowledge acquired and implemented, and in the scope of knowledge resources utilized for the projects; a high level of overall performance as regards to the set of design skills studied; a positive change in attitude towards technology and technological studies; the emergence of consistent design styles by individuals and groups along their work in the projects.

A MCDM-based expert system for climate-change impact assessment and adaptation planning - A case study for the Georgia Basin, Canada

Abstract: An MCDM-based expert system was developed to tackle the interrelationships between the climate change and the adaptation policies in terms of water resources management in the Georgia Basin, Canada User interfaces of the developed expert system, named MAEAC (MCDM-based expert system for adaptation analysis under changing climate), was developed based on system configuration, knowledge acquisition, survey analysis, and MCDM-based policy analysis A number of processes that were vulnerable to climate change were examined and pre-screened through extensive literature review, expert consultation and statistical analysis Adaptation policies to impacts of temperature increase, precipitation-pattern variation and sea-level rise were comprehensively explicated and incorporated within the developed system The MAEAC could be used for both acquiring knowledge of climate-change impacts on water resources in the Georgia Basin and supporting formulation of the relevant adaptation policies It can also be applied to other watersheds to facilitate assessment of climate-change impacts on socio-economic and environmental sectors, as well as formulation of relevant adaptation policies

Defining knowledge management (KM) activities: towards consensus

Abstract: Purpose – The purpose of the paper is to present a vocabulary of terms that clearly define knowledge management (KM) activities in order to move towards consensus in the adoption of a common language within the field.Design/methodology/approach – Existing literature across several disciplines has been integrated to provide a clear description of the sorts of activities an individual undertakes in order to move from knowledge acquisition to innovation, and a clarification of the terms used to describe such activities is put forth.Findings – Adoption of a common vocabulary to describe KM activities provides a platform to better understand how best to manage these activities, and enables clearer identification of the knowledge management capabilities held by various sectors within the broader business community.Research limitations/implications – There is a need to undertake empirical research and in‐depth case studies of knowledge management practices using a common vocabulary as a framework with which to i...

Decoding wikipedia categories for knowledge acquisition

Abstract: This paper presents an approach to acquire knowledge from Wikipedia categories and the category network. Many Wikipedia categories have complex names which reflect human classification and organizing instances, and thus encode knowledge about class attributes, taxonomic and other semantic relations. We decode the names and refer back to the network to induce relations between concepts in Wikipedia represented through pages or categories. The category structure allows us to propagate a relation detected between constituents of a category name to numerous concept links. The results of the process are evaluated against ResearchCyc and a subset also by human judges. The results support the idea that Wikipedia category names are a rich source of useful and accurate knowledge.

Knowledge acquisition and environmental commitment in SMEs

Abstract: This article presents the results of an empirical study that investigated the relationship between a firm's knowledge acquisition activities and its environmental commitment. The study focuses on both regular knowledge acquisition activities and those specific to environmental issues. Statistical analyses of the data obtained from a sample of 136 Canadian manufacturing small and medium enterprises (SMEs) were conducted. Study results first revealed that regular knowledge acquisition activities and those specific to environmental issues are positively related to environmental commitment. Results also suggest that SMEs use different knowledge sources when acquiring environmental knowledge and that trade associations and suppliers play a significant role in this process. Copyright © 2007 John Wiley & Sons, Ltd and ERP Environment.

Organizational characteristics as prescriptive factors of knowledge management initiatives

Abstract: Purpose – The use of knowledge in organizations is largely a discretionary behavior that can be encouraged but not demanded. As such, the firm can only attempt to provide the right conditions for employees to endorse the role of knowledge workers. The purpose of this paper is to examine how the organization of the firm affects knowledge management.Design/methodology/approach – This research proposes a new framework showing the prescriptive role of organizational characteristics onto knowledge management (KM) initiatives. Based on this framework, data were generated from nine semi‐structured interviews conducted in the American, British and Japanese offices of a major Japanese pharmaceutical company, using a Boolean approach and qualitative content analysis.Findings – Organizational characteristics, specifically – structure, membership, relationship, and strategy affect KM, namely – knowledge acquisition, storage, diffusion, and application respectively.Research limitations/implications – Even though the d...

ToolSHeDTM: The development and evaluation of a decision support tool for health and safety in construction design

Abstract: Purpose – The purpose of this paper is to describe an innovative information and decision support tool (ToolSHeD™) developed to help construction designers to integrate the management of OHS risk into the design process. The underlying structure of the prototype web‐based system and the process of knowledge acquisition and modelling are described. Design/methodology/approach – The ToolSHeD™ research and development project involved the capture of expert reasoning regarding design impacts upon occupational health and safety (OHS) risk. This knowledge was structured using an innovative method well‐suited to modelling knowledge in the context of uncertainty and discretionary decision‐making. Example “argument trees” are presented, representing the reasoning used by a panel of experts to assess the risk of falling from height during roof maintenance work. The advantage of using this method for modelling OHS knowledge, compared to the use of simplistic rules, is discussed Findings – The ToolSHeD™ prototype development and testing reveals that argument trees can represent design safety risk knowledge effectively. Practical implications – The translation of argument trees into a web‐based decision support tool is described and the potential impact of this tool in providing construction designers (architects and engineers) with easy and inexpensive access to expert OHS knowledge is discussed. Originality/value – The paper describes a new computer application, currently undergoing testing in the Australian building and construction industry. Its originality lies in the fact that ToolSHeD™ deploys argument trees to represent expert OHS reasoning, overcoming inherent limitations in rule‐based expert systems.

Conceptual modelling: knowledge acquisition and model abstraction

Abstract: Conceptual modelling has gained a lot of interest in recent years and simulation modellers are particularly interested in understanding the processes involved in arriving at a conceptual model. This paper contributes to this understanding by discussing the artifacts of conceptual modelling and two specific conceptual modelling processes: knowledge acquisition and model abstraction. Knowledge acquisition is the process of finding out about the problem situation and arriving at a system description. Model abstraction refers to the simplifications made in moving from a system description to a conceptual model. Soft Systems Methodology has tools that can help a modeller with knowledge acquisition and model abstraction. These tools are drawing rich pictures, undertaking analyses `one?, `two?, `three?, and constructing a root definition and the corresponding purposeful activity model. The use of these tools is discussed with respect to a case study in health care.

Building Domain Ontologies from Text for Educational Purposes

Abstract: This paper presents a semi-automatic framework that aims to produce domain concept maps from text and then to derive domain ontologies from these concept maps. This methodology particularly targets the e-learning and AIED (artificial intelligence in education) communities as they need such structures to sustain the production of e-learning resources tailored to learners' needs. This paper details the steps to transform textual resources, particularly textual learning objects (LOs), into domain concept maps and it explains how this abstract structure is transformed into a formal domain ontology. A methodology is also presented to evaluate the results of ontology learning. The paper shows how such structures (domain concept maps and formal ontologies) make it possible to bridge the gap between eLearning and intelligent tutoring systems by providing a common domain model.

An Environment to Support Large Scale Experimentation in Software Engineering

Abstract: Experimental studies have been used as a mechanism to acquire knowledge through a scientific approach based on measurement of phenomena in different areas. However it is hard to run such studies when they require models (simulation), produce amount of information, and explore science in scale. In this case, a computerized infrastructure is necessary and constitutes a complex system to be built. In this paper we discuss an experimentation environment that has being built to support large scale experimentation and scientific knowledge management in software engineering.

Use of Knowledge Management Systems and the Impact on the Acquisition of Explicit Knowledge

Abstract: In an era where knowledge is increasingly seen as an organization's most valuable asset, many firms have implemented knowledge management systems (KMS) in an effort to capture, store and disseminate knowledge across the firm. Concerns have been raised, however, about the potential dependency of users on KMS and the related potential for decreases in knowledge acquisition and expertise development (Cole 1998; Alavi and Leidner 2001b; O'Leary 2002a). The purpose of this study, which is exploratory in nature, is to investigate whether using KMS embedded with explicit knowledge impacts novice decision makers' judgment performance and knowledge acquisition differently than using traditional reference materials (e.g. manuals, textbooks) to research and solve a problem. An experimental methodology is used to study the relative performance and explicit knowledge acquisition of 188 participants partitioned into two groups using either a KMS or traditional reference materials in problem solving. The study finds that KMS users outperform users of traditional reference materials when they have access to their respective systems/materials, but the users of traditional reference materials outperform KMS users when respective systems/materials are removed. While all users improve interpretive problem solving and encoding of definitions and rules, there are significant differences in knowledge acquisition between the two groups.

Learning through International Acquisitions: The Process of Knowledge Acquisition in China

Abstract: and Key Results Research on the process of knowledge learning and absorption in acquisition context has emerged recently. Yet relatively less attention has been paid to the process of knowledge transfer and learning and its impact on successful acquisitions. This paper adopts a process perspective’ to investigate this issue. Based on four international acquisitions in China, it generates new theoretical propositions as well as practical managerial implications. Results reveal the types of knowledge acquired and how it is transferred and learnt to contribute to the success of international acquisitions. The knowledge acquisition and learning process in international context involve three stages: knowledge assessment, knowledge sharing and knowledge assimilation. Foreign acquirers tend to acquire complementary knowledge from local targets, adopt dual management structure and facilitate communications with local personnel in order to achieve the success of acquisitions and future operations.

Anomaly detection in the maritime domain

Abstract: Defence R&D Canada is developing a Collaborative Knowledge Exploitation Framework (CKEF) to support the analysts in efficiently managing and exploiting relevant knowledge assets to achieve maritime domain awareness in joint operations centres of the Canadian Forces. While developing the CKEF, anomaly detection has been clearly recognized as an important aspect requiring R&D. An activity has thus been undertaken to implement, within the CKEF, a proof-of-concept prototype of a rule-based expert system to support the analysts regarding this aspect. This expert system has to perform automated reasoning and output recommendations (or alerts) about maritime anomalies, thereby supporting the identification of vessels of interest and threat analysis. The system must contribute to a lower false alarm rate and a better probability of detection in drawing operator's attention to vessels worthy of their attention. It must provide explanations as to why the vessels may be of interest, with links to resources that help the operators dig deeper. Mechanisms are necessary for the analysts to fine tune the system, and for the knowledge engineer to maintain the knowledge base as the expertise of the operators evolves. This paper portrays the anomaly detection prototype, and describes the knowledge acquisition and elicitation session conducted to capture the know-how of the experts, the formal knowledge representation enablers and the ontology required for aspects of the maritime domain that are relevant to anomaly detection, vessels of interest, and threat analysis, the prototype high-level design and implementation on the service-oriented architecture of the CKEF, and other findings and results of this ongoing activity.

A Theory of Local Entrepreneurship in the Knowledge Economy

Abstract: Contents: Foreword by Anders Lundstrom Introduction Part I: Context - The Knowledge Economy and Different Dynamics 1. The Knowledge Economy: Uncertainty, Ambiguity and Potential 2. Differentiated Entrepreneurship: Regional and Local Disparities Part II: The Main Actors: Entrepreneurs, Organizations and Milieux, their Capacity to Develop Knowledge 3. Entrepreneurs 4. The Learning Organization: Information-gathering Strategies Used by Small Businesses 5. The Entrepreneurial Milieu: The Key to Creating a Distinct Local Identity Part III: The Factors: Information, Networks and Innovation - Necessary and Sufficient Conditions for Entrepreneurship 6. Information: The First Necessary Condition for Reducing Uncertainty and Ambiguity 7. Networks: A Second Necessary Condition - The Sharing of Information Leading to Innovation 8. Innovation: A Sufficient Condition Part IV: The Functioning of Local Entrepreneurship: Dynamism through Contagion 9. Intelligence Networking: Developing a Dynamic Regional Fabric 10. Entrepreneurial Contagion And Knowledge Acquisition 11. Conclusion: Towards a New Theory of Entrepreneurship Bibliography Index

Advancing Knowledge Discovery and Data Mining

Abstract: Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and Data Mining, and Knowledge Discovery in Database (KDD) process are presented in the paper. Data mining theory, Data mining tasks, Data Mining technology and Data Mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.

Use of Knowledge Management Systems and the Impact on the Acquisition of Explicit Knowledge.

Abstract: In an era where knowledge is increasingly seen as an organization's most valuable asset, many firms have implemented knowledge‐management systems (KMS) in an effort to capture, store, and disseminate knowledge across the firm Concerns have been raised, however, about the potential dependency of users on KMS and the related potential for decreases in knowledge acquisition and expertise development (Cole 1998; Alavi and Leidner 2001b; O'Leary 2002a) The purpose of this study, which is exploratory in nature, is to investigate whether using KMS embedded with explicit knowledge impacts novice decision makers' judgment performance and knowledge acquisition differently than using traditional reference materials (eg, manuals, textbooks) to research and solve a problem An experimental methodology is used to study the relative performance and explicit knowledge acquisition of 188 participants partitioned into two groups using either a KMS or traditional reference materials in problem solving The stu

Automated knowledge acquisition from clinical narrative reports.

Abstract: Knowledge of associations between biomedical entities, such as disease-symptoms, is critical for many automated biomedical applications. In this work, we develop automated methods for acquisition and discovery of medical knowledge embedded in clinical narrative reports. MedLEE, a Natural Language Processing (NLP) system, is applied to extract and encode clinical entities from narrative clinical reports obtained from New York-Presbyterian Hospital (NYPH), and associations between the clinical entities are determined based on statistical methods adjusted by volume tests. We focus on two types of entities, disease and symptom, in this study. Evaluation based on a random sample of disease-symptom associations indicates an overall recall of 90% and a precision of 92%. In conclusion, the preliminary study demonstrated that this method for knowledge acquisition of disease-symptom pairs from clinical reports is effective. The automated method is generalizable, and can be applied to detect other clinical associations, such as between diseases and medications.

A theory for integrating knowledge in architectural design education

Abstract: This paper argues for introducing a theory for knowledge integration in architectural design education. A contextual analysis of the reasons for developing a theory is introduced and reasons are categorized. The milieu of the theory is constituted in several contextual elements. The theory encompasses a number of underlying theories and concepts derived from other fields that differ dramatically from architecture. It consists of three major components: the disciplinary component; the cognitive-philosophical component; and the inquiryepistemic component. Each of these components encompasses other smaller components integral to the building of the theory itself. Notably, the three components address ways in which knowledge can be integrated, how the desired integration would meet the capacity of the human mind, how such integration relates to the nature of knowledge and how knowledge about it is acquired, conveyed, and assimilated. Possible mechanisms for knowledge acquisition are an indispensable component of the theory, whose aim is to foster the development of responsive knowledge critical to the successful creation of built environments.

Automatically Harvesting and Ontologizing Semantic Relations

Abstract: With the advent of the Web and the explosion of available textual data, it is key for modern natural language processing systems to access, represent and reason over large amounts of knowledge in semantic repositories. Separately, the knowledge representation and natural language processing communities have been developing representations/engines for reasoning over knowledge and algorithms for automatically harvesting knowledge from textual data, respectively. There is a pressing need for collaboration between the two communities to provide large-scale robust reasoning capabilities for knowledge rich applications like question answering. In this chapter, we propose one small step by presenting algorithms for harvesting semantic relations from text and then automatically linking the knowledge into existing semantic repositories. Experimental results show better than state of the art performance on both relation harvesting and ontologizing tasks.

Knowledge and Expertise

Abstract: What is knowledge? In fact, it may be simpler to ask, what is not knowledge? There is perhaps no single, more all-encompassing concept in cognitive psychology than knowledge. Knowledge contributes to simple perceptual tasks such as object recognition, when people identify an ambiguous stimulus on the basis of prior knowledge (e.g., Bar & Ullman 1996). Knowledge contributes to memory performance in myriad ways, for example when people reconstruct events according to a schema or script (e.g., Roediger et al. 2001; Tuckey & Brewer 2003). Finally, knowledge is the fundamental ingredient of human cognition at its best, namely expert performance. Accordingly, the literature on knowledge is vast, and its sheer size prevents a summary by a few simple assertions. To keep our task manageable we have therefore imposed some strong constraints on this chapter. Charness and Schultetus (1999) defi ned knowledge as “acquired information that can be activated in a timely fashion in order to generate an appropriate response” (p. 61). We accept this as our working defi nition, but restrict consideration to manifestations of knowledge that have been variously called declarative or explicit (Reber & Squire 1994; Shanks & Johnstone 1998). These forms of knowledge are characterized by being accessible to awareness and verbal report, for example in response to a query such as “What is the capital of France?” We do not give much consideration to issues of training and knowledge acquisition, which are the domain of Chapter 21. Finally, we use the applied focus of this handbook to guide which topics to foreground and which to downplay. Accordingly, we omit discussion of computational models of knowledge and its acquisition and transfer (e.g., ACT; Anderson 1990) as extensive treatments of those models can be found elsewhere (e.g., Singley & Anderson 1989). Instead, we foreground research on expertise and expert performance; we focus on the fractionation and encapsulation of knowledge; and we examine the success or failure of the transfer of that knowledge.

RKBExplorer.com: a knowledge driven infrastructure for linked data providers

Abstract: RKB Explorer is a Semantic Web application that is able to present unified views of a significant number of heterogeneous data sources. We have developed an underlying information infrastructure which is mediated by ontologies and consists of many independent triple-stores, each publicly available through both SPARQL endpoints and resolvable URIs. To realise this synergy of disparate information sources, we have deployed tools to identify co-referent URIs, and devised an architecture to allow the information to be represented and used. This paper provides a brief overview of the system including the underlying infrastructure, and a number of associated tools for both knowledge acquisition and publishing.

Designing and evaluating patterns for relation acquisition from texts with CAMÉLÉON

Abstract: Pattern-based approaches for knowledge identification in texts assume that linguistic regularities always characterise the same kind of knowledge, such as semantic relations. In this paper, we report the experimental evaluation of a large set of patterns using an ontology enrichment tool: Cameleon. Results emphasize the strong influence of the corpus on pattern efficiency and on their meaning. This influence confirms two of the hypotheses that motivated to define Cameleon as a support used in a human-driven process: (1) patterns and relations must be adapted to each project; (2) human interpretation is required to decide how to report the pieces of knowledge identified with patterns in the ontology.