classification

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 classification;  facets;  web;  

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 classification;  knowledge-discovery;  information-retrieval;  cluster;  google;  

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Abstract: In a world flooded by text of various sources, it is of strategic importance to find ways to map information present in written documents in a form that helps users locate and associate important information within a particular text data set. Content-based maps can support extremely useful explorations of text data sets. This paper proposes and evaluates the use of Kolmogorov complexity approximations as a means to detect similarity between general textual documents, in order to support mapping and visualization techniques for corpora exploration. The calculation of this similarity measure requires no intermediate representation of a corpus (such as vector representation) and therefore no pre-processing or parametrization steps. That makes it very attractive for a wider range of exploratory applications compared to conventional measures that need vector-based text representations. The visual layout used here is based on fast distance multi-dimensional projections. It is shown that the similarity measure and the resulting maps present very good precision and that the approach can be used successfully for visual analysis of automatically generated text maps.

 Visualization;  classification;  analysis;  document;  

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Abstract: Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used.

 classification;  clustering;  knowledge-discovery;  information-retrieval;  data-mining;  library;  automated;  wiki;  

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 classification;  concepts;  information-retrieval;  web-based;  

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Abstract: One barrier to the uptake of geocomputation is that, unlike GIS, it has no system or toolbox that provides easy access to useful functionality. This paper describes an experimental environment, GeoVISTA Studio, that attempts to address this shortcoming. Studio is a Java-based, visual programming environment that allows for the rapid development of complex data exploration and knowledge construction applications to support geographic analysis. It achieves this by leveraging advances in geocomputation, software engineering, visualization and machine learning. At the time of writing, Studio contains full 3D rendering capability and has the following functionality: interactive parallel coordinate plots, scatterplot, visual classifier, 2D map and image viewer, sophisticated colour selection (including Munsell colour-space), spreadsheet, statistics package, and supervised and unsupervised neural networks. Through examples of Studio at work, this paper demonstrates the roles that geocomputation and visualization can play throughout the scientific cycle of knowledge creation, emphasising their supportive and mutually beneficial relationship. A brief overview of different types of inference used in such knowledge creation activities is given, and related to the exploratory analysis tools described. By way of results, a detailed account of the use of these tools is presented, and various findings and insights generated are pointed out. The domain of application is the process of uncovering useful categories by which a taxonomy of landuse/landcover can be created. The proposed categories are then evaluated using a combination of neural and visual methods, to ensure their viability.

 knowledge discovery;  Visualization;  classification;  exploratory data analysis;  Geocomputation;  Inference;  

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 ontology;  classification;  data models;  geologic map information;  

Abstract: This paper explores the development of categories shared in the field logging of a region by a team of geologists. Visualization, neural networks and spatial statistical tools are employed to gain insight into the complex space of attributes observed, and into the categories developed. Background material and a discussion of results examines the findings in the light of research into category development, and specifically how categories are thought to be formed and modified as part of the (geo)scientific process and the situations encountered. Results show that (1) category discrepancy exists between individuals; (2) category development or revision is evident among individuals; and (3) that some categories do not seem to be totally defined by observed data alone. The results imply that contextual factors should also be considered when adopting ontological approaches to information representation.

 classification;  categorization;  category development;  situated learning;  information ontology;  self-organizing maps;  geological fieldwork;  

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Abstract: This paper describes knowledge components that are thought to be appropriate and sufficient to precisely describe certain types of cognitive subject matter content (knowledge). It also describes knowledge structures that show the relationships among these knowledge components and among other knowledge objects. It suggests that a knowledge structure is a form of schema such as those that learners use to represent knowledge in memory. A mental model is a schema plus cognitive processes for manipulating and modifying the knowledge stored in a schema. We suggested processes that enable learners to manipulate the knowledge components of conceptual network knowledge structures for purposes of classification, generalization, and concept elaboration. We further suggested processes that enable learners to manipulate the knowledge components of process knowledge structures (PEAnets) for purposes of explanation, prediction, and troubleshooting. The hypothesis of this paper is that knowledge components and knowledge structures, such as those described in this paper, could serve as meta mental models that would enable learners to more easily acquire conceptual and causal networks and their associated processes. The resulting specific mental models would facilitate their ability to solve problems of conceptualization and interpretation

 classification;  computer aided instruction;  psychology;  user modelling;  causal networks;  cognitive processes;  cognitive subject matter;  knowledge components;  knowledge objects;  knowledge structures;  mental models;  process knowledge structures;  

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Abstract: Software systems of today are frequently composed from prefabricated, heterogeneous components that provide complex functionality and engage in complex interactions. Existing research on component-based development has mostly focused on component structure, interfaces, and functionality. Recently, software architecture has emerged as an area that also places significant importance on component interactions, embodied in the notion of software connectors. However, the current level of understanding and support for connectors has been insufficient. This has resulted in their inconsistent treatment and a notable lack of understanding of what the fundamental building blocks of software interaction are and how they can be composed into more complex interactions. This paper attempts to address this problem. It presents a comprehensive classification framework and taxonomy of software connectors. The taxonomy is obtained through an extensive analysis of existing component interactions. The taxonomy is used both to understand existing software connectors and to suggest new, unprecedented connectors. We demonstrate the use of the taxonomy on the architecture of a large, existing system.

 taxonomy;  software architecture;  software connector;  classification;  

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 classification;  agents;  document;  query;  information-retrieval;  web;  

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 classification;  agents;  exploration;  document;  knowledge-discovery;  web;  

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 classification;  document;  tagging;  bayesian;  

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 classification;  spatial analysis;  

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 classification;  neural nets;  remote sensing;  

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 classification;  Knowledge Representation;  chap 3;