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Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on

Date 18-22 Dec. 2006

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Displaying Results 1 - 25 of 202
  • 2006 IEEE/WIC/ACM International Conference on Web Intelligence - Cover

    Page(s): c1
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  • 2006 IEEE/WIC/ACM International Conference on Web Intelligence-Title

    Page(s): i - iii
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  • 2006 IEEE/WIC/ACM International Conference on Web Intelligence-Copyright

    Page(s): iv
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  • 2006 IEEE/WIC/ACM International Conference on Web Intelligence - TOC

    Page(s): v - xviii
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  • Welcome Message from Conference Chairs

    Page(s): xix - xx
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  • Welcome Message from Program Chair

    Page(s): xxi - xxii
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  • WI'06 and IAT'06 Conference Organization

    Page(s): xxiii - xxiv
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  • Program Committee

    Page(s): xxv - xxvii
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  • WI'06 Non-PC reviewers

    Page(s): xxviii
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  • Neuroscience: New Insights for AI?

    Page(s): 3 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (208 KB) |  | HTML iconHTML  

    Understanding the processing of information in our cortex is a significant part of understanding how the brain works and of understanding intelligence itself, arguably one of the greatest problems in science today. In particular, our visual abilities are computationally amazing and we are still far from imitating them with computers. Thus, visual cortex may well be a good proxy for the rest of the cortex and indeed for intelligence itself. But despite enormous progress in the physiology and anatomy of the visual cortex, our understanding of the underlying computations remains fragmentary. This position paper is based on the very recent, surprising realization that we may be on the verge of developing an initial quantitative theory of visual cortex, faithful to known physiology and able to mimic human performance in difficult recognition tasks, outperforming current computer vision systems. The proof of principle was provided by a preliminary model that, spanning several levels from biophysics to circuitry to the highest system level, describes information processing in the feedforward pathway of the ventral stream of primate visual cortex. The thesis of this paper is that - finally - neurally plausible computational models are beginning to provide powerful new insights into the key problem of how the brain works, and how to implement learning and intelligence in machines View full abstract»

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  • Service-Oriented Science: Scaling eScience Impact

    Page(s): 9 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (126 KB) |  | HTML iconHTML  

    Computational approaches to problem solving have proven their worth in many fields of science, allowing the collection and analysis of unprecedented quantities of data, and the exploration via simulation of previously obscure phenomena. New "systems" approaches in fields as diverse as biology [12], earthquake science [11], and environmental science [3] are enabled by, and are spurring the further development of, such computational approaches. But as computational and system-level science methods become increasingly sophisticated, we must ask: how do we scale their impact, from the specialist to entire communities [7]? View full abstract»

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  • Two Obvious Intuitions: Ontology-Mapping Needs Background Knowledge and Approximation

    Page(s): 11
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (94 KB) |  | HTML iconHTML  

    Summary form only given. Ontology mapping (or: ontology alignment, or integration) is one of the most active areas the semantic Web area. An increasing amount of ontologies are becoming available in recent years, and if the semantic Web is to be taken seriously, the problem of ontology mapping must be solved. In this article the author discuss recent work where we have quantitatively shown that indeed, ontology mapping can benefit from background knowledge, and that, somewhat surprisingly, more background knowledge leads to continuously improving results. We also discuss how the use of such background knowledge can be exploited to find approximate mappings when perfect mappings cannot be found View full abstract»

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  • Approximate Reasoning in MAS: Rough Set Approach

    Page(s): 12 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (106 KB) |  | HTML iconHTML  

    In modeling multiagent systems for real-life problems, techniques for approximate reasoning about vague concepts and dependencies (ARVCD) are necessary. We discuss an approach to approximate reasoning based on rough sets. In particular, we present a number of basic concepts such as approximation spaces, concept approximation, rough inclusion, construction of information granules in calculi of information granules, and perception logic. The approach to ARVCD is illustrated by examples relative to interactions of agents, ontology approximation, adaptive hierarchical learning of compound concepts and skills, behavioral pattern identification, planning, conflict analysis and negotiations, and perception-based reasoning View full abstract»

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  • Engaging in a Conversation with Synthetic Agents along the Virtuality Continuum

    Page(s): 19 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (318 KB) |  | HTML iconHTML  

    Summary form only given. Most work so far has concentrated on the design and implementation of conversational agents at the two extremes of the virtuality continuum. In this article, the author reports on a new generation of synthetic characters that are no longer bound to a flat screen, but able to enter a physical world and to engage in a conversation with a human user. Users and characters do not inhabit separated spaces, but share an informational and physical reality that is augmented by digital objects. As a consequence, communication has to take into account both the physical and the digital context. New forms of deixis are enabled by the manipulation of objects and movements of characters in the physical space. Further challenges arise from the realization of so-called traversable interfaces that allow human and synthetic agents to cross the border from the digital to the real world and vice versa View full abstract»

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  • Generating Concept Ontologies through Text Mining

    Page(s): 23 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (322 KB) |  | HTML iconHTML  

    Designing mechanisms for creating concept ontologies automatically is an important research problem. In this work we have proposed a rough-set based mechanism to generate concept ontologies with concepts mined from documents. When the concept ontology is mined from preclassified documents, the output signifies the core set of domain concepts and their inter-relationships that define the categories, as well as the inter-category relationships. When the ontology is mined from a heterogeneous collection, the documents are first clustered into homogeneous groups and then mined for concepts. Rough set based lower and upper approximations have been used to identify core concepts and associated concepts for a domain or a group. The scheme has been tested over multiple domains View full abstract»

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  • Enhancing Software Engineering Project Information through Software Engineering Ontology Instantiations

    Page(s): 33 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (238 KB) |  | HTML iconHTML  

    Software engineering project information is frequently evolving and queried to reflect project development changes in the software requirements or in the design process, to incorporate additional functionality to systems or to allow incremental improvement and the like. Therefore, the project information needs enhancement to ease up-to-date ontological information and to ease communication. Ontologies are widely used for capturing and organising knowledge of a particular domain of interest. We propose the use of software engineering ontology instantiations and enrichment to capture the software engineering project information View full abstract»

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  • Origin-Destination Network Tomography with Bayesian Inversion Approach

    Page(s): 38 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (182 KB) |  | HTML iconHTML  

    Origin-destination (OD) network tomography problem is the estimation of OD traffic counts from measurable traffic counts at router interfaces. In this paper the problem is formulated as a linear inverse problem with additive noise and is resolved using Bayesian inversion approach. Both OD traffic counts and noise are modelled as Gaussian random functions, and are represented by Karhunen-Loeve expansion, respectively. The posterior random function of OD traffic counts given the link counts is also represented as the Karhunen-Loeve expansion. With the singular system of routing matrix, we thus can found the optimal estimator of OD traffic counts analytically View full abstract»

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  • Temporal Analysis of the Wikigraph

    Page(s): 45 - 51
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (409 KB) |  | HTML iconHTML  

    Wikipedia is an online encyclopedia, available in more than 100 languages and comprising over 1 million articles in its English version. If we consider each Wikipedia article as a node and each hyperlink between articles as an arc we have a "Wikigraph", a graph that represents the link structure of Wikipedia. The Wikigraph differs from other Web graphs studied in the literature by the fact that there are explicit timestamps associated with each node's events. This allows us to do a detailed analysis of the Wikipedia evolution over time. In the first part of this study we characterize this evolution in terms of users, editions and articles; in the second part, we depict the temporal evolution of several topological properties of the Wikigraph. The insights obtained from the Wikigraphs can be applied to large Web graphs from which the temporal data is usually not available View full abstract»

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  • Mining and Visualizing the Evolution of Subgroups in Social Networks

    Page(s): 52 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1386 KB) |  | HTML iconHTML  

    A social network consists of people who interact in some way such as members of online communities sharing information via the WWW. To learn more about how to facilitate community building e.g. in organizations, it is important to analyze the interaction behavior of their members over time. So far, many tools have been provided that allow for the analysis of static networks and some for the temporal analysis of networks - however only on the vertex and edge level. In this paper we propose two approaches to analyze the evolution of two different types of online communities on the level of subgroups. The first method consists of statistical analyses and visualizations that allow for an interactive analysis of subgroup evolutions in communities that exhibit a rather membership structure. The second method is designed for the detection of communities in an environment with highly fluctuating members. For both methods, we discuss results of experiments with real data from an online student community View full abstract»

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  • ESMAP: A Multi-agent Platform for Extending a Knowledge Management System

    Page(s): 59 - 65
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (202 KB) |  | HTML iconHTML  

    For more than one decade knowledge management has taken advantage of some abilities that software agents are endowed with. Agent features such as autonomy, cooperation, communication, and learning capacity have been used to improve the performance of knowledge management applications. In this paper we outline our proposal for implementing a Jade-based multi-agent platform to enhance the potential of KnowCat: a fully consolidated, thoroughly tested and validated knowledge management system which has been in active use at Universidad Autonoma de Madrid (Spain) since 1998. We also give a succinct description of the agent platform architecture and a brief presentation of its current status View full abstract»

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  • Different Aspects of Social Network Analysis

    Page(s): 66 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5553 KB) |  | HTML iconHTML  

    A social network is a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of pattern of relationships among people, organizations, states and such social entities. Social network analysis provides both a visual and a mathematical analysis of human relationships. Web can also be considered as a social network. Social networks are formed between Web pages by hyperlinking to other Web pages. In this paper a state of the art survey of the works done on social network analysis ranging from pure mathematical analyses in graphs to analysing the social networks in semantic Web is given. The main goal is to provide a road map for researchers working on different aspects of social network analysis View full abstract»

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  • Labeled Link Analysis for Extracting User Characteristics in E-commerce Activity Network

    Page(s): 73 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1147 KB) |  | HTML iconHTML  

    This paper proposes an approach to characterize user activity in e-commerce, especially in Internet auctions. It is considered that users are connected to each other through their transactions. The connectivity makes an extensive complex network where users' intentions and behaviors are reflected in the network structure. That is to say, the network is composed of nodes as users and links as transactions between two users. Moreover, the links have one label that indicates a category type of transacted goods such as "book", "fashion", "music" among others. In order to characterize user activities in the network, a modified HITS algorithm is proposed. The results of the analysis using real data collected from an Internet auction site show characteristics of each user. Since the method takes advantage of network structures, evaluation values for the user characteristics that have been obtained illustrate not only the tendency of category types of goods that the users have transacted but also the tendency of relationships with other users. The characteristics are influenced by both direct and indirect connections among users. Our final goal is to construct a system that can personalize and profile users for marketing in e-commerce View full abstract»

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  • Measuring Qualities of Articles Contributed by Online Communities

    Page(s): 81 - 87
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (190 KB) |  | HTML iconHTML  

    Using open source Web editing software (e.g., wiki), online community users can now easily edit, review and publish articles collaboratively. While much useful knowledge can be derived from these articles, content users and critics are often concerned about their qualities. In this paper, we develop two models, namely basic model and peer review model, for measuring the qualities of these articles and the authorities of their contributors. We represent collaboratively edited articles and their contributors in a bipartite graph. While the basic model measures an article's quality using both the authorities of contributors and the amount of contribution from each contributor, the peer review model extends the former by considering the review aspect of article content. We present results of experiments conducted on some Wikipedia pages and their contributors. Our result show that the two models can effectively determine the articles' qualities and contributors' authorities using the collaborative nature of online communities View full abstract»

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  • Learning Social Networks from Web Documents Using Support Vector Classifiers

    Page(s): 88 - 94
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (813 KB) |  | HTML iconHTML  

    Automatic generation of a social network requires extracting pair-wise relations of the individuals. In this research, learning social network from incomplete relationship data is proposed. It is assumed that only a small subset of relations between the individuals is known. With this assumption, the social network extraction is translated into a text classification problem. The relations between two individuals are modeled by merging their document vectors and the given relations are used as labels of training data. By this transformation, a text classifier such as SVM is used for learning the unknown relations. We show that there is a link between the intrinsic sparsity of social networks and class distribution imbalance of the training data. In order to re-balance the unbalanced training data, a minority class down-sampling strategy is employed. The proposed framework is applied to a true FOAF (friend of a friend) database and evaluated by the macro-averaged F-measure View full abstract»

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  • Understanding Leadership Behavior in Human Influence Network

    Page(s): 95 - 102
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (374 KB) |  | HTML iconHTML  

    We determined the results of the questionnaire for 97 staff members working in dot-jp, a non-profit organization in Japan, to understand the degrees of satisfaction of staff members with leaders. We then extracted human influence networks from the archives of e-mail used at dot-jp to understand relationships between leaders and other staff members. By integrating these two approaches, we revealed ideal leadership behavior. We discovered that leaders should receive messages from staff members as well as send messages to construct reliable relationships. Otherwise, leaders become smug and lose trusts from other staff members View full abstract»

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