Unsupervised multi-label text classification using a world knowledge ontology

The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.

A System for Representing and Using Real-World Knowledge

This report describes a knowledge-base system in which the information is stored in a network of small parallel processing elements ??de and link units ??ich are controlled by an external serial computer. This network is similar to the semantic network system of Quillian, but is much more tightly controlled. Such a network can perform certain critical deductions and searches very quickly; it avoids many of the problems of current systems, which must use complex heuristics to limit and guided their searches. It is argued (with examples) that the key operation in a knowledge-base system is the intersection of large explicit and semi-explicit sets. The parallel network system does this in a small, essentially constant number of cycles; a serial machine takes time proportional to the size of the sets, except in special cases.

World knowledge in the process of translation

The paper illustrates the role of world knowledge in comprehending and translating texts. A short news item, which displays world knowledge fairly implicitly in condensed lexical forms, was translated by students from English into German. It is shown that their translation strategies changed from a first draft which was rather close to the surface structure of the source text to a final version which took situational aspects, texttypological conventions and the different background knowledge of the respective addressees into account. Decisions on how much world knowledge has to be made explicit in the target text, however, must be based on the relevance principle. Consequences for teaching and for the notions of semantic knowledge and world knowledge are discussed.

Benchmarking the knowledge competitiveness of the globe's high performing regions:a review of the World Knowledge Competitiveness Index

Purpose – The purpose of this paper is to analyze the way in which the knowledge competitiveness of regions is measured and further introduces the World Knowledge Competitiveness Index (WKCI) benchmarking tool. Design/methodology/approach – The methodology consists of an econometric analysis of key indicators relating to the concept of knowledge competitiveness for 125 regions from across the globe consisting of 55 representatives from North America, 45 from Europe and 25 from Asia and Oceania. Findings – The key to winning the super competitive race in the knowledge-based economy is investment in the future: research and development, and education and training. It is found that the majority of the high-performing regional economies in the USA have a knowledge competitive edge over their counterparts in Europe and Asia. Research limitations/implications – To an extent, the research is limited by the availability of comparable indicators and metrics at the regional level that extend across the globe. Whilst comparative data are often accessible at the national level, regional data sources remain underdeveloped. Practical implications – The WKCI has become internationally recognized as an important instrument for economic development policymakers and regional investment promotion agents as they create and refine their strategies and targets. In particular, it has provided a benchmark that allows regions to compare their knowledge competitiveness with other regions for around the world and not only their own nation or continent. Originality/value – The WKCI is the first composite and relative measure of the knowledge competitiveness of the globe's best performing regions.

World knowledge competitiveness index 2002:benchmarking the globes high-performing regions

The World Knowledge Competitiveness Index 2002 is the first composite and relative measure of the knowledge economies of the globe's best performing regions. It represents an integrated and overall benchmark of the knowledge capacity, capability and sustainability of each region and the extent to which this knowledge is translated into economic value and transferred into the wealth of the citizens of each region. This publication has over 50 pages and covers the following sections: The Economics of Knowledge Competitiveness The Rankings - World Knowledge Competitiveness Index Human Capital Components Knowledge Capital Components Regional Economy Outputs Knowledge Sustainability Components Driving Knowledge-Based Growth

World knowledge competitiveness index 2008

The 2008 edition of the WKCI compares 145 regions across 19 knowledge economy benchmarks (full data for all indicators across each of the 19 benchmarks is contained in the accompanying Excel spreadsheets). This represents an increase of twenty regions compared to the last edition in 2005: nine from Europe, eight from North America, and three from Asia Pacific. These new regions were selected on the basis of a survey of a wide range of regions appearing to be become more internationally competitive. This year’s report also contains a special chapter on economic development in the three leading Chinese regions.

Knowledge-Based Urban Development : Planning and Applications in the Information Era

In the globalizing world, knowledge and information (and the social and technological settings for their production and communication) are now seen as keys to economic prosperity. The economy of a knowledge city creates value-added products using research, technology, and brainpower. The social benefit of knowledge-based urban development (KBUD); however, extends beyond aggregate economic growth.

Empirical approaches in knowledge city research

In the 21st century, it has become apparent that ‘knowledge’ is a major factor of postmodern production (Yigitcanlar et al., 2007). Beyond this, in today’s rapidly globalizing world, knowledge, along with the social and technological settings, is seen as a key to secure economic prosperity and quality of life (Yigitcanlar et al., 2008a). However, limiting the benefits of a ‘knowledge-based development’ to only economic gains—and to a degree to social ones—is quite a narrow sighted view (Yigitcanlar et al., 2008b). Thus, the concept of ‘knowledge-based urban development’ is coined to bring economic prosperity, environmental sustainability, a just socio-spatial order and good governance to cities, and as a result producing a purposefully designed city—i.e., ‘knowledge city’—generating positive environmental and governance outcomes as well as economic and societal ones (Yigitcanlar, 2011; Carrillo et al., 2014).

An investigation of innovation and knowledge creation in virtual worlds

The Internet and World Wide Web have had, and continue to have, an incredible impact on our civilization. These technologies have radically influenced the way that society is organised and the manner in which people around the world communicate and interact. The structure and function of individual, social, organisational, economic and political life begin to resemble the digital network architectures upon which they are increasingly reliant. It is increasingly difficult to imagine how our ‘offline’ world would look or function without the ‘online’ world; it is becoming less meaningful to distinguish between the ‘actual’ and the ‘virtual’. Thus, the major architectural project of the twenty-first century is to “imagine, build, and enhance an interactive and ever changing cyberspace” (Lévy, 1997, p. 10). Virtual worlds are at the forefront of this evolving digital landscape. Virtual worlds have “critical implications for business, education, social sciences, and our society at large” (Messinger et al., 2009, p. 204). This study focuses on the possibilities of virtual worlds in terms of communication, collaboration, innovation and creativity. The concept of knowledge creation is at the core of this research. The study shows that scholars increasingly recognise that knowledge creation, as a socially enacted process, goes to the very heart of innovation. However, efforts to build upon these insights have struggled to escape the influence of the information processing paradigm of old and have failed to move beyond the persistent but problematic conceptualisation of knowledge creation in terms of tacit and explicit knowledge. Based on these insights, the study leverages extant research to develop the conceptual apparatus necessary to carry out an investigation of innovation and knowledge creation in virtual worlds. The study derives and articulates a set of definitions (of virtual worlds, innovation, knowledge and knowledge creation) to guide research. The study also leverages a number of extant theories in order to develop a preliminary framework to model knowledge creation in virtual worlds. Using a combination of participant observation and six case studies of innovative educational projects in Second Life, the study yields a range of insights into the process of knowledge creation in virtual worlds and into the factors that affect it. The study’s contributions to theory are expressed as a series of propositions and findings and are represented as a revised and empirically grounded theoretical framework of knowledge creation in virtual worlds. These findings highlight the importance of prior related knowledge and intrinsic motivation in terms of shaping and stimulating knowledge creation in virtual worlds. At the same time, they highlight the importance of meta-knowledge (knowledge about knowledge) in terms of guiding the knowledge creation process whilst revealing the diversity of behavioural approaches actually used to create knowledge in virtual worlds and. This theoretical framework is itself one of the chief contributions of the study and the analysis explores how it can be used to guide further research in virtual worlds and on knowledge creation. The study’s contributions to practice are presented as actionable guide to simulate knowledge creation in virtual worlds. This guide utilises a theoretically based classification of four knowledge-creator archetypes (the sage, the lore master, the artisan, and the apprentice) and derives an actionable set of behavioural prescriptions for each archetype. The study concludes with a discussion of the study’s implications in terms of future research.

Tools for Finding Inconsistencies in Real-world Logic-based Systems

Currently there is extensive theoretical work on inconsistencies in logic-based systems. Recently, algorithms for identifying inconsistent clauses in a single conjunctive formula have demonstrated that practical application of this work is possible. However, these algorithms have not been extended for full knowledge base systems and have not been applied to real-world knowledge. To address these issues, we propose a new algorithm for finding the inconsistencies in a knowledge base using existing algorithms for finding inconsistent clauses in a formula. An implementation of this algorithm is then presented as an automated tool for finding inconsistencies in a knowledge base and measuring the inconsistency of formulae. Finally, we look at a case study of a network security rule set for exploit detection (QRadar) and suggest how these automated tools can be applied.

Using Analogy to Acquire Commonsense Knowledge from Human Contributors

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical.

Strategy clusters and sources of knowledge in French L2 listening comprehension

This article reports on an exploratory investigation into the listening strategies of lower-intermediate learners of French as an L2, including the sources of knowledge they employed in order to comprehend spoken French. Data from 14 learners were analysed to investigate whether employment of strategies in general and sources of knowledge in particular varied according to the underlying linguistic knowledge of the student. While low linguistic knowledge learners were less likely to deploy effectively certain strategies or strategy clusters, high linguistic knowledge levels were not always associated with effective strategy use. Similarly, while there was an association between linguistic knowledge and learners’ ability to draw on more than one source of knowledge in a facilitative manner, there was also evidence that learners tended to over-rely on linguistic knowledge where other sources, such as world knowledge, would have proved facilitative. We conclude by arguing for a fresh approach to listening pedagogy and research, including strategy instruction, bottom-up skill development and a consideration of the role of linguistic knowledge in strategy use.

The knowledge competitiveness of regional economies: conceptualisation and measurement

Whereas the competitive advantage of firms can arise from size and position within their industry as well as physical assets, the pattern of competition in advanced economies has increasingly come to favour those firms that can mobilise knowledge and technological skills to create novelty in their products. At the same time, regions are attracting growing attention as an economic unit of analysis, with firms increasingly locating their functions in select regions within the global space. This article introduces the concept of knowledge competitiveness, defined as an economy’s knowledge capacity, capability and sustainability, and the extent to which this knowledge is translated into economic value and transferred into the wealth of the citizens. The article discusses the way in which the knowledge competitiveness of regions is measured and further introduces the World Knowledge Competitiveness Index, which is the first composite and relative measure of the knowledge competitiveness of the globe’s best performing regions.