Showing papers on "Domain knowledge published in 2010"

Systems and methods for responding to natural language speech utterance

Abstract: Systems and methods are provided for receiving speech and non-speech communications of natural language questions and/or commands, transcribing the speech and non-speech communications to textual messages, and executing the questions and/or commands. The invention applies context, prior information, domain knowledge, and user specific profile data to achieve a natural environment for one or more users presenting questions or commands across multiple domains. The systems and methods creates, stores and uses extensive personal profile information for each user, thereby improving the reliability of determining the context of the speech and non-speech communications and presenting the expected results for a particular question or command.

Knowledge management : concepts and best practices

Abstract: 1 Introduction.- I: Design Fields.- 2 Business Process Oriented Knowledge Management.- 3 The Fraunhofer Knowledge Management Audit (FKM-Audit).- 4 Motivation for Knowledge Management.- 5 Role Models, Human Resources and Strategy.- 6 Knowledge Management Tools.- 7 Intellectual Capital: Measuring Knowledge Management.- II: Survey.- 8 Delphi Study on the Future of Knowledge Management - Overview of the Results.- 9 A Survey on Good Practices in Knowledge Management in European Companies.- 10 How German Companies Employ Knowledge Management. An OECD Survey on Usage, Motivations and Effects.- III: Case Studies.- 11 Knowledge Management - Results of a Benchmarking Study.- 12 Knowledge Management: The Holistic Approach of Arthur D. Little, Inc..- 13 The Aventis Approach to Knowledge Management: Locating Inhouse Expertise for Immediate Anytime, Anywhere Availability.- 14 Cultural Change Triggers Best Practice Sharing - British Aerospace plc..- 15 Knowledge Management and Customer Orientation Hewlett Packard Austria.- 16 Knowledge Management in a Global Company - IBM Global Services.- 17 Open Minded Corporate Culture and Management Supports the Sharing of External and Internal Knowledge - Phonak.- 18 Sharing Process Knowledge in Production Environments - Roche Diagnostics - Laboratory Systems.- 19 KnowledgeSharing@MED - Enabling Knowledge Sharing by Turning Knowledge into Business.- IV: KM - Made in Europe.- 20 Building Communities. Organizational Knowledge Management within the European Commission's Information Society Technologies Programme.- List of Figures.- References.- Recommended Further Readings.- Editors.- Contributors.

The Nature and Value of Knowledge: Three Investigations

Abstract: PART I: KNOWLEDGE AND UNDERSTANDING 1. The Value Problem for Knowledge 2. Knowledge and Final Value 3. Anti-Luck Virtue Epistemology 4. Understanding PART II: KNOWLEDGE AND RECOGNITION 5. Knowledge in Recent Epistemology: Some Problems 6. Perceptual Knowledge and Recognitional Abilities 7. Knowledge from Indicators 8. The Social Transmission of Knowledge PART III: KNOWLEDGE AND ACTION 9. Knowledge and Justification 10. Second-Order Knowledge 11. Knowledge of Action Bibliography

Hydra: automatically configuring algorithms for portfolio-based selection

Abstract: The AI community has achieved great success in designing high-performance algorithms for hard combinatorial problems, given both considerable domain knowledge and considerable effort by human experts Two influential methods aim to automate this process: automated algorithm configuration and portfolio-based algorithm selection The former has the advantage of requiring virtually no domain knowledge, but produces only a single solver; the latter exploits per-instance variation, but requires a set of relatively uncorrelated candidate solvers Here, we introduce Hydra, a novel technique for combining these two methods, thereby realizing the benefits of both Hydra automatically builds a set of solvers with complementary strengths by iteratively configuring new algorithms It is primarily intended for use in problem domains for which an adequate set of candidate solvers does not already exist Nevertheless, we tested Hydra on a widely studied domain, stochastic local search algorithms for SAT, in order to characterize its performance against a well-established and highly competitive baseline We found that Hydra consistently achieved major improvements over the best existing individual algorithms, and always at least roughly matched—and indeed often exceeded— the performance of the best portfolios of these algorithms

The data avalanche is here. shouldn’t we be digging?

Abstract: We have access to an unprecedented amount of fine-grained data on cities, transportation, economies, and societies, much of these data referenced in geo-space and time. There is a tremendous opportunity to discover new knowledge about spatial economies that can inform theory and modeling in regional science. However, there is little evidence of computational methods for discovering knowledge from databases in the regional science literature. This paper addresses this gap by clarifying the geospatial knowledge discovery process, its relation to scientific knowledge construction, and identifying challenges to a greater role in regional science.

Thinking about the weather: How display salience and knowledge affect performance in a graphic inference task.

Abstract: Three experiments examined how bottom-up and top-down processes interact when people view and make inferences from complex visual displays (weather maps). Bottom-up effects of display design were investigated by manipulating the relative visual salience of task-relevant and task-irrelevant information across different maps. Top-down effects of domain knowledge were investigated by examining performance and eye fixations before and after participants learned relevant meteorological principles. Map design and knowledge interacted such that salience had no effect on performance before participants learned the meteorological principles; however, after learning, participants were more accurate if they viewed maps that made task-relevant information more visually salient. Effects of display design on task performance were somewhat dissociated from effects of display design on eye fixations. The results support a model in which eye fixations are directed primarily by top-down factors (task and domain knowledge). They suggest that good display design facilitates performance not just by guiding where viewers look in a complex display but also by facilitating processing of the visual features that represent task-relevant information at a given display location.

A Brief History of Knowledge Building

Abstract: Knowledge Building as a theoretical, pedagogical, and technological innovation focuses on the 21st century need to work creatively with knowledge. The team now advancing Knowledge Building spans multiple disciplines, sectors, and cultural contexts. Several teacher-researcher-government partnerships have formed to bring about the systemic changes required to accommodate pedagogical innovations that range from elementary to tertiary education and require new forms of teacher education. This paper tracks the evolution of Knowledge Building, starting with research on “knowledge transforming,” “intentional learning,” and other processes leading to the development of expertise. It provides an account of how the first networked collaborative learning environment was developed to support such processes and next-generation research and development to advance education for innovation and knowledge creation.

Managing Software Engineering Knowledge

Abstract: 1 Why Is It Important to Manage Knowledge?.- 1 Managing Software Engineers and Their Knowledge.- 2 An Investigation into Software Development Process Knowledge.- 3 Usage of Intranet Tools for Knowledge Management in a Medium-Sized Software Consulting Company.- 2 Supporting Structures for Managing Software Engineering Knowledge.- 4 Knowledge Management for Software Organizations.- 5 A Dynamic Model of Software Engineering Knowledge Creation.- 6 Evaluating an Approach to Sharing Software Engineering Knowledge to Facilitate Learning.- 7 Eliciting and Maintaining Knowledge for Requirements Evolution.- 8 Emergent Knowledge in Web Development.- 3 Application of Knowledge Management in Software Engineering.- 9 Case-Based Reasoning and Software Engineering.- 10 A Process for Identifying Relevant Information for a Repository: A Case Study for Testing Techniques.- 11 A Knowledge Management Framework to Support Software Inspection Planning.- 12 Lessons Learned in Software Quality Assurance.- 13 Making Software Engineering Competence Development Sustained through Systematic Experience Management.- 4 Practical Guidelines for Managing Software Engineering Knowledge.- 14 Practical Guidelines for Learning-Based Software Product Development.- 15 In-Project Learning by Goal-oriented Measurement.- 16 e-R&D: Effectively Managing and Using R&D Knowledge.- 17 Knowledge Infrastructure for Project Management.

Discovery-driven graph summarization

Abstract: Large graph datasets are ubiquitous in many domains, including social networking and biology. Graph summarization techniques are crucial in such domains as they can assist in uncovering useful insights about the patterns hidden in the underlying data. One important type of graph summarization is to produce small and informative summaries based on user-selected node attributes and relationships, and allowing users to interactively drill-down or roll-up to navigate through summaries with different resolutions. However, two key components are missing from the previous work in this area that limit the use of this method in practice. First, the previous work only deals with categorical node attributes. Consequently, users have to manually bucketize numerical attributes based on domain knowledge, which is not always possible. Moreover, users often have to manually iterate through many resolutions of summaries to identify the most interesting ones. This paper addresses both these key issues to make the interactive graph summarization approach more useful in practice. We first present a method to automatically categorize numerical attributes values by exploiting the domain knowledge hidden inside the node attributes values and graph link structures. Furthermore, we propose an interestingness measure for graph summaries to point users to the potentially most insightful summaries. Using two real datasets, we demonstrate the effectiveness and efficiency of our techniques.

The interplay between individual and collective knowledge: technologies for organisational learning and knowledge building

Abstract: This article presents a framework model that defines knowledge building as a co-evolution of cognitive and social systems. Our model brings together Nonaka's knowledge-creating theory and Luhmann's systems theory. It is demonstrated how collaborative knowledge building may occur – in an ideal situation – within an organisation, when people interact with each other using shared digital artefacts. For this purpose, three different technologies are introduced as examples: social-tagging systems, pattern-based task-management systems, and wikis. These examples have been chosen to demonstrate that knowledge building can occur with respect to both declarative and procedural knowledge. The differences and similarities between these technologies, as far as their potential for organisational knowledge building is concerned, are discussed in the light of the framework model.

Active learning for biomedical citation screening

Abstract: Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe a real-world, deployed application of AL to the problem of biomedical citation screening for systematic reviews at the Tufts Medical Center's Evidence-based Practice Center. We propose a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features)and extend this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features. Our methods outperform existing AL strategies on three real-world systematic review datasets. We argue that evaluation must be specific to the scenario under consideration. To this end, we propose a new evaluation framework for finite-pool scenarios, wherein the primary aim is to label a fixed set of examples rather than to simply induce a good predictive model. We use a method from medical decision theory for eliciting the relative costs of false positives and false negatives from the domain expert, constructing a utility measure of classification performance that integrates the expert preferences. Our findings suggest that the expert can, and should, provide more information than instance labels alone. In addition to achieving strong empirical results on the citation screening problem, this work outlines many important steps for moving away from simulated active learning and toward deploying AL for real-world applications.

The Effects of Consumer Prior Knowledge and Processing Strategies on Judgments

Abstract: Four studies examine how consumers' prior knowledge of a product category and the way they process product information affect evaluation. Consumers with extensive prior knowledge of a category evaluate the brand more favorably when the presentation of the product information prompts a sense of progress rather than facilitating a detailed assessment (Studies 1 and 2), as well as when the information presentation involves a high level of construal rather than a low level (Studies 3 and 4). Consumers with limited domain knowledge exhibit opposite outcomes. The subjective experience of processing fluency mediates these effects. The findings suggest that evaluations are more favorable when there is a fit between prior knowledge and message processing than when fit is absent.

Systems and methods for determining information and knowledge relevancy, relevant knowledge discovery and interactions, and knowledge creation

Abstract: Systems and methods for determining user specific information and knowledge relevancy, relevant knowledge and information discovery, user intent and relevant interactions via intelligent messaging, collaboration, sharing and information categorisation, further delivering created knowledge accessible through a personalised user experience.

Computer-Based Diagnostics and Systematic Analysis of Knowledge

Abstract: What is knowledge? How can it be successfully assessed? How can we best use the results? As questions such as these continue to be discussed and the learning sciences continue to deal with expanding amounts of data, the challenge of applying theory to diagnostic methods takes on more complexity. Computer-Based Diagnostics and Systematic Analysis of Knowledge meets this challenge head-on as an international panel of experts reviews current and emerging assessment methodologies in the psychological and educational arenas. Emphasizing utility, effectiveness, and ease of interpretation, contributors critically discuss practical innovations and intriguing possibilities (including mental representations, automated knowledge visualization, modeling, and computer-based feedback) across fields ranging from mathematics education to medicine. These contents themselves model the steps of systematic inquiry, from theoretical construct to real-world application: Historical and theoretical foundations for the investigation of knowledge Current opportunities for understanding knowledge empirically Strategies for the aggregation and classification of knowledge Tools and methods for comparison and empirical testing Data interfaces between knowledge assessment tools Guidance in applying research results to particular fields Researchers and professionals in education psychology, instructional technology, computer science, and linguistics will find Computer-Based Diagnostics and Systematic Analysis of Knowledge a stimulating guide to a complex present and a rapidly evolving future.

Intrinsically Motivated Hierarchical Skill Learning in Structured Environments

Abstract: We present a framework for intrinsically motivated developmental learning of abstract skill hierarchies by reinforcement learning agents in structured environments. Long-term learning of skill hierarchies can drastically improve an agent's efficiency in solving ensembles of related tasks in a complex domain. In structured domains composed of many features, understanding the causal relationships between actions and their effects on different features of the environment can greatly facilitate skill learning. Using Bayesian network structure (learning techniques and structured dynamic programming algorithms), we show that reinforcement learning agents can learn incrementally and autonomously both the causal structure of their environment and a hierarchy of skills that exploit this structure. Furthermore, we present a novel active learning scheme that employs intrinsic motivation to maximize the efficiency with which this structure is learned. As new structure is acquired using an agent's current set of skills, more complex skills are learned, which in turn allow the agent to discover more structure, and so on. This bootstrapping property makes our approach a developmental learning process that results in steadily increasing domain knowledge and behavioral complexity as an agent continues to explore its environment.

Activity recognition using temporal evidence theory

Abstract: The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. Peoples' activities in the home often have an identifiable routine; activities take place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naive Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.

Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data.

Abstract: Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine-tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross-validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.

Development of a method for ontology-based empirical knowledge representation and reasoning

Abstract: In the knowledge economy era of the 21st century [14,17], the competitive advantage of enterprises has shifted from visible equipment, capital and labor in the past to invisible knowledge nowadays. Knowledge can be distinguished into tacit knowledge and explicit knowledge. Meanwhile, tacit knowledge largely encompasses empirical knowledge difficult to be documented and generally hidden inside of personal mental models. The inability to transfer tacit knowledge to organizational knowledge would cause it to disappear after knowledge workers leaving their post, ultimately losing important intellectual assets for enterprises. Therefore, enterprises attempting to create higher knowledge value are highly concerned with how to transfer personal empirical knowledge inside of an enterprise into an organizational explicit knowledge by using a systematic method to manage and share such valuable empirical knowledge effectively. This study develops a method of ontology-based empirical knowledge representation and reasoning, which adopts OWL (Web Ontology Language) to represent empirical knowledge in a structural way in order to help knowledge requesters clearly understand empirical knowledge. An ontology reasoning method is subsequently adopted to deduce empirical knowledge in order to share and reuse relevant empirical knowledge effectively. Specifically, this study involves the following tasks: (i) analyze characteristics for empirical knowledge, (ii) design an ontology-based multi-layer empirical knowledge representation model, (iii) design an ontology-based empirical knowledge concept schema, (iv) establish an OWL-based empirical knowledge ontology, (v) design reasoning rules for ontology-based empirical knowledge, (vi) develop a reasoning algorithm for ontology-based empirical knowledge, and (vii) implement an ontology-based empirical knowledge reasoning mechanism. Results of this study facilitate the tacit knowledge storage, management and sharing to provide knowledge requesters with accurate and comprehensive empirical knowledge for problem solving and decision support.

Knowledge-Discounted Event Detection in Sports Video

Abstract: Automatic events annotation is an essential requirement for constructing an effective sports video summary Researchers worldwide have actively been seeking the most robust and powerful solutions to detect and classify key events (or highlights) in different sports Most of the current and widely used approaches have employed rules that model the typical pattern of audiovisual features within particular sport events These rules are mainly based on manual observation and heuristic knowledge; therefore, machine learning can be used as an alternative To bridge the gap between the two alternatives, we propose a hybrid approach, which integrates statistics into logical rule-based models during highlight detection We have also successfully pioneered the use of play-break segment as a universal scope of detection and a standard set of features that can be applied for different sports, including soccer, basketball, and Australian football The proposed method uses a limited amount of domain knowledge, making this method less subjective and more robust for different sports An experiment using a large data set of sports video has demonstrated the effectiveness and robustness of the algorithms

Can Children Really Create Knowledge

Abstract: Can children genuinely create new knowledge, as opposed to merely carrying out activities that resemble those of mature scientists and innovators? The answer is yes, provided the comparison is not to works of genius but to standards that prevail in ordinary research communities. One important product of knowledge creation is concepts and tools that enable further knowledge creation. This is the kind of knowledge creation of greatest value in childhood education. Examples of it, drawn from elementary school knowledge-building classrooms, are examined to show both the attainability and the authenticity of knowledge creation to enable knowledge creation. It is mainly achieved through students’ theory building, and it is a powerful way of converting declarative knowledge to productive knowledge.

Knowledge creation: synthesis, tools and products

Abstract: The foundation for any activity related to knowledge translation is knowledge; it’s hard to do KT without the “K.” In the first article of this series, we introduced the knowledge-to-action cycle, which provides a framework for strategies in knowledge translation. In the centre of this cycle

Managing talent in knowledge‐intensive settings

Abstract: Purpose – This paper seeks to explore the processes and channels through which valuable knowledge from outside the firm reaches those employees who can exploit that knowledge for innovative purposes. It seeks to identify the specific talents exhibited by the key individuals involved in facilitating these important knowledge flows. It also aims to detail the interventions which management can adopt to harness knowledge flow talents. Design/methodology/approach – The methodology used was a single case study of a medical devices R&D group, incorporating social network analysis and semi-structured interviews. Findings – It was found that it is now rare for a single individual to possess all the talents necessary to effectively acquire and disseminate external knowledge. Owing to the prevalence of information and communication technologies, a small number of uniquely skilled individuals specialize in acquiring valuable external knowledge, while an altogether different set of individuals specialize in disseminating that knowledge internally. Originality/value – There is a dearth of literature in the knowledge management field directed towards understanding how the unique talents of those employees who are integral components of knowledge networks can be harnessed. Building on concepts of talent management and the technological gatekeeper, the specific talents exhibited by these individuals are explored. Then some organizational level interventions are pointed up, which can facilitate knowledge-intensive organizations in fully exploiting their resources to maximize innovative capabilities.