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Web Intelligence, IEEE/WIC/ACM International Conference on

Date 2-5 Nov. 2007

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Displaying Results 1 - 25 of 156
  • 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology - Cover

    Page(s): c1
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  • 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology - Title

    Page(s): i - iii
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  • 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology - Copyright

    Page(s): iv
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  • 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology - TOC

    Page(s): v - xv
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  • Welcome Message from Conference Chairs and Program Chair

    Page(s): xvi - xviii
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  • WI'07 Conference Organization

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  • WI'07 Program Committee Members

    Page(s): xxi - xxiii
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  • WI'07 Non-PC reviewers:

    Page(s): xxiv
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  • Computer Science as a Lens on the Sciences:

    Page(s): xxvi
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    This talk will trace the growing influence of fundamental ideas from computer science on the nature of research in a number of scientific fields. There is a growing awareness that information processing lies at the heart of the processes studied in fields as diverse as quantum mechanics, statistical physics, nanotechnology, neuroscience, linguistics, economics and sociology. Increasingly, mathematical models in these fields are expressed in algorithmic languages and describe algorithmic processes. The speaker will briefly describe connections between quantum computing and the foundations of quantum mechanics, and between statistical mechanics and phase transitions in computation. He will indicate how the growth of the Web has created new phenomena to be investigated by sociologists and economists. He will then focus in greater detail on computational molecular biology, where the view of living cells as complex information processing systems has become the dominant paradigm, and will discuss specific algorithmic problems arising in the sequencing of genomes, the comparative analysis of the resulting genomic sequences,the modeling of networks of interacting proteins, and the associations between genetic variation and disease. View full abstract»

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  • ServiceWeb 3.0

    Page(s): xxvii
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    Computer science is entering a new generation. The previous generation was based on abstracting from hardware. The emerging generation comes from abstracting from software and sees all resources as services in a service-oriented architecture (SOA). In a world of services, it is the service that counts for a customer and not the software or hardware components that implement the service. Service-oriented architectures are rapidly becoming the dominant computing paradigm. However, current SOA solutions are still restricted in their application context to in-house solution of companies. A service web will have billions of services. While service orientation is widely acknowledged for its potential to revolutionize the world of computing by abstracting form underlying hardware and software layers, that success depends on resolving fundamental challenges that SOA does not address currently. The mission of Service Web 3.0 is to provide solutions to integration and search that will enable the Service Oriented Architecture (SOA) revolution on a worldwide scale. Hereby we must focus on three major areas where we need to extend current approaches towards service orientation: View full abstract»

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  • Conversational Informatics and Human-Centered Web Intelligence

    Page(s): xxviii
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    Conversation is the most natural communication means for people to communicate with each other. I believe that conversation plays a critical role in realizing a paradigm of human-centered web intelligence in which web intelligence engines are grounded on the human society. We are currently building a computational framework for circulating information in a conversational fashion, using information packages called conversation quanta that encapsulate conversational scenes. Technologies are being developed for acquiring conversation quanta on the spot, accumulating them in a visually recognizable form, and reusing them in a situated fashion. Conversational Informatics constitutes the theoretical foundation for measurement, analysis, and modeling of conversation. I will overview recent results in Conversational Informatics that will help achieve our vision. I will also discuss our approach in the context of Social Intelligence Design aimed at the understanding and augmentation of social intelligence for collective problem solving and learning. View full abstract»

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  • How Relevant is Game Theory to Intelligent Agent Technology?

    Page(s): xxix
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    At this point, restricted to rather specialized areas, and even there must be taken with a grain of salt. But at the same time you can't afford not to know it; there is currently no better underpinning for understanding multiagent systems. I will elaborate using experience in both academia and industry. View full abstract»

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  • The Challenge of Cultural Modeling for Inferring Intentions and Behavior

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

    Accounting for social, cultural, and political factors must form the basis for understanding decision-making, actions, and reactions of individuals, thus driving their behaviors and intentions. Clearly, the individual is not wholly defined by just personal social, cultural, and political beliefs but also functions within a group of individuals. Within these groups (or organizations), they assimilate a potentially wide variety of different social factors, which may or may not differ from their own. Also, the group itself can vary in degrees of complexity, styles of interaction, and so forth, resulting in highly dynamic and emergent modes of behaviors. Even more difficult, this also includes taking into account the values, attitudes, and beliefs of the local population/environment that the individual/group is situated within. Without all these factors, we cannot expect to effectively understand, analyze, or predict the behaviors and intentions of others which grows ever more critical as our society continues to globalize and especially in today's conflicts and catastrophes. Thus, the need for a comprehensive modeling framework is evident as our only real hope of addressing such complexity. However, to date, only small isolated groups of pertinent behavioral factors have been studied, while there is little or no work towards developing a general unified and comprehensive approach that is also computational. The major challenges we face can be summed up in the following questions: 1. For prediction and explanation of intent and behavior, how does one computationally model individual or organizations and their emergent interactions with others, in various situations? 2. How does one organize and build the necessary social, cultural, political, behavioral, etc. knowledge- base? 3. How do you avoid brittleness and overspecialization? How do you construct these models efficiently and effectively, and dynamically evolve such models over time based on changing cultural and s- ocial factors? 4. How do you validate your models? In this talk, we will explore these challenges and focus on addressing pragmatic and computational issues in such modeling and examine some existing real-world efforts, current solutions, and openquestions. View full abstract»

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  • Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations

    Page(s): 3 - 7
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    Project SEWEBAR concerning presentation of analytical reports from data mining through semantic Web is introduced. Related local and global analytical reports are mentioned. An example of local analytical report is given and problems of indexing local reports are shortly discussed. View full abstract»

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  • COBRA - Mining Web for Corporate Brand and Reputation Analysis

    Page(s): 11 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (589 KB) |  | HTML iconHTML  

    Corporations are extremely sensitive to issues such as brand stewardship and product reputation. Traditional brand image and reputation tracking is limited to news wires and contact centres analysis. However, with the emergence of Web, consumer generated media (COM), such as blogs, news forums, message boards, and Web pages/sites, is rapidly becoming the "voice of the people". This paper describes a COBRA (corporate brand and reputation analysis) solution that mines a wide range of COM contents for brand and reputation analysis. The solution contains a flexible ETL (Extract, Transform, and Load) engine that processes diverse sets of structured and unstructured information, a suite of analytical capabilities that mines COM content to extract semantic entities and insights out of the data, and an alerting mechanism that utilizes the analytics results to accurately generate brand and reputation alerts. We use a real-world case study to demonstrate the effectiveness of our approach. View full abstract»

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  • Question Answering over Implicitly Structured Web Content

    Page(s): 18 - 25
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    Implicitly structured content on the Web such as HTML tables and lists can be extremely valuable for web search, question answering, and information retrieval, as the implicit structure in a page often reflects the underlying semantics of the data. Unfortunately, exploiting this information presents significant challenges due to the immense amount of implicitly structured content on the web, lack of schema information, and unknown source quality. We present TQA, a web-scale system for automatic question answering that is often able to find answers to real natural language questions from the implicitly structured content on the web. Our experiments over more than 200 million structures extracted from a partial web crawl demonstrate the promise of our approach. View full abstract»

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  • Enhancing Search Engine Quality Using Concept-based Text Retrieval

    Page(s): 26 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (582 KB) |  | HTML iconHTML  

    Most of the common techniques in text retrieval are based on the statistical analysis of a term either as a word or a phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying representation should indicate terms that capture the semantics of text. In this case, the representation can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based representation, called Conceptual Ontological Graph (COG), where a concept can be either a word or a phrase and totally dependent on the sentence semantics, is introduced. The aim of the proposed representation is to extract the most important terms in a sentence and a document with respect to the meaning of the text. The COG representation analyzes each term at both the sentence and the document levels. This is different from the classical approach of analyzing terms at the document level. First, the proposed representation denotes the terms which contribute to the sentence semantics. Then, each term is chosen based on its position within the COG representation. Lastly, the selected terms are associated to their documents as features for the purpose of indexing before text retrieval. The COG representation can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the key concepts that represent the sentence meaning. Large sets of experiments using the proposed COG representation on different datasets in text retrieval are conducted. Experimental results demonstrate the substantial enhancement of the text retrieval quality using the COG representation over the traditional techniques. The evaluation of results relies on two quality measures, the bpref and P(10). Both the quality measures improved when the newly developed COG representation is used to enhance the quality of the text retrieval results. View full abstract»

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  • An Augmented Tagging Scheme with Triple Tagging and Collective Filtering

    Page(s): 35 - 38
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    Collaborative tagging is increasingly drawing attentions. However the keyword based tagging scheme has its limitations and it can be observed that tagging society are seeking and using new tagging patterns. This paper proposes a subject-predicate-object scheme for users to triple tag web resources. We first introduce the triple tag model and discuss its relations with existing tag schema and RDF model. Then a filter-based framework that supports the query of triple tags is proposed. The implementation and a case study in the comparison shopping domain are exhibited. View full abstract»

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  • A Semantically Enriched Competency Management System to Support the Analysis of a Web-based Research Network

    Page(s): 41 - 47
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    While it is generally acknowledged that domain ontologies can significantly improve knowledge management systems (KMS) within organizations and among distributed web communities, we have little evidence of operational ontology-based KMS and their practical utility in real settings in the literature. We describe here the INTEROP KMap, a fully implemented, semantically indexed, competency management system, used to facilitate research collaboration and coordination of a Network of Excellence (NoE) on Enterprise Interoperability. Since the main highlighted advantages of ontologies are improved information access and interoperability, our aim in this paper is to give experimental support to these claims. We provide a summary description and usage data on the KMap, as well as experiments to quantify the added value of semantic search wrt traditional document ranking measures. View full abstract»

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  • Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

    Page(s): 48 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1104 KB) |  | HTML iconHTML  

    There are information needs involving costly decisions that cannot be efficiently satisfied through conventional Web search engines. Alternately, community centric search can provide multiple viewpoints to facilitate decision making. We propose to discover and model the temporal dynamics of thematic communities based on mutual awareness, where the awareness arises due to observable blogger actions and the expansion of mutual awareness leads to community formation. Given a query, we construct a directed action graph that is time-dependent, and weighted with respect to the query. We model the process of mutual awareness expansion using a random walk process and extract communities based on the model. We propose an interaction space based representation to quantify community dynamics. Each community is represented as a vector in the interaction space and its evolution is determined by a novel interaction correlation method. We have conducted experiments with a real-world blog dataset and have promising results for detection as well as insightful results for community evolution. View full abstract»

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  • Contextual Prediction of Communication Flow in Social Networks

    Page(s): 57 - 65
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    The paper develops a novel computational framework for predicting communication flow in social networks based on several contextual features. The problem is important because prediction of communication flow can impact timely sharing of specific information across a wide array of communities. We determine the intent to communicate and communication delay between users based on several contextual features in a social network corresponding to (a) neighborhood context, (b) topic context and (c) recipient context. The intent to communicate and communication delay are modeled as regression problems which are efficiently estimated using Support Vector Regression. We predict the intent and the delay, on an interval of time using past communication data. We have excellent prediction results on a real-world dataset from MySpace.com with an accuracy of 13-16%. We show that the intent to communicate is more significantly influenced by contextual factors compared to the delay. View full abstract»

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  • Detection of Web Subsites: Concepts, Algorithms, and Evaluation Issues

    Page(s): 66 - 73
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (627 KB) |  | HTML iconHTML  

    Web sites are often organized into several regions, each dedicated to a specific topic or serving a particular function. From a user's perspective, these regions typically form coherent sets of pages characterized by a distinct navigation structure and page layout-we refer to them as subsites. In this paper we propose to characterize Web site structure as a collection of subsites and devise a method for detecting subsites and entry points for subsite navigation. In our approach we use a new model for representing Web site structure called Link Structure Graph (LSG). The LSG captures a complete hyperlink structure of a Web site and models link associations reflected in the page layout. We analyze a sample of Web sites and compare the LSG based approach to commonly used statistics for Web graph analysis. We demonstrate that LSG approach reveals site properties that are beyond the reach of standard site models. Furthermore, we devise a method for evaluating the performance of subsite detection algorithms and provide evaluation guidelines. View full abstract»

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  • Finding Experts Using Social Network Analysis

    Page(s): 77 - 80
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    Searching an organization's document repositories for experts is a frequently occurred problem in intranet information management. A common method for finding experts in an organization is to use social networks - people are not isolated but connected by various kinds of associations. In organizations, people explicitly send email to one another thus social networks are likely to be contained in the patterns of communication. Moreover, in some web pages, the relationship among people is also recorded. In our approach we propose several strategies in discovering the associations among people from emails and web pages. Based on the social networks, we proposed an expertise propagation algorithm: from a ranked list of candidates according to their probability of being expert for a certain topic, we select a small set of the top ones as seed, and then use the social networks among the candidates to discover other potential experts. The experiments on TREC enterprise track show significant performance improvement with the algorithm. View full abstract»

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  • How Contents Influence Clustering Features in the Web

    Page(s): 81 - 84
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    In World Wide Web, contents of web documents play important roles in the evolution process because of their effects on linking preference. A majority of topological properties are content-related, and among them the clustering features are sensitive to contents of Web documents. In this paper, we first observe the impacts of content similarity on web links by introducing a metric called Linkage Probability. Then we investigate how contents influence the formation mechanism of the most basic cluster, triangle, with a metric named Triangularization Probability. Experimental results indicate that content similarity has a positive function in the process of cluster formation in theWeb. Theoretical analysis predicts the contents influence on the clustering features in the Web very well. View full abstract»

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