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bookmarks  36

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    With the advent of Web 2.0, Social Computing has emerged as one of the hot research topics recently. Social Computing involves the investigation of collective intelligence by using computational techniques such as machine learning, data mining, natural language processing, etc. on social behavior data collected from blogs, wikis, clickthrough data, query logs, tags, etc. from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this tutorial, we will introduce Social Computing and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. Moreover, we will discuss the challenging issues involved in Social Computing in the context of theory and models of social networks, mining of spatial and temporal social information, ways to deal with partial and incomplete information in the social context, scalability and algorithmic issues with social computational techniques, and security & privacy issues. Some potential social computing applications for discussion would include collaborative filtering, query log processing, learning to rank, large graph and link algorithms, etc.
    16 years ago by @dbenz
     
      attendedwww2009irwin_kingtutorialsocial_computing
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      16 years ago by @dbenz
       
        www2009rankingbenchmark
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        Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective commu- nity recommendations in order to meet more users. In this paper, we investigate two algorithms from very different do- mains and evaluate their effectiveness for personalized com- munity recommendation. First is association rule mining (ARM), which discovers associations between sets of com- munities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommen- dations than directly mining rules from the observed data. We experiment on an Orkut data set consisting of 492, 104 users and 118, 002 communities. Our empirical comparisons using the top-k recommendations metric show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities. However, for recommendation lists of up to 3 communities, ARM is still a bit better. We analyze exam- ples of the latent information learned by LDA to explain this finding. To efficiently handle the large-scale data set, we parallelize LDA on distributed computers [1] and demon- strate our parallel implementation’s scalability with varying numbers of machines.
        16 years ago by @dbenz
         
          attendedwww2009
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          Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data – which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.
          16 years ago by @dbenz
           
            attendedwww2009
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            This paper presents SOFIE, a system for automated on- tology extension. SOFIE can parse natural language docu- ments, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the exist- ing knowledge and on the new knowledge in order to disam- biguate words to their most probable meaning, to reason on the meaning of text patterns and to take into account world knowledge axioms. This allows SOFIE to check the plau- sibility of hypotheses and to avoid inconsistencies with the ontology. The framework of SOFIE unites the paradigms of pattern matching, word sense disambiguation and ontolog- ical reasoning in one unified model. Our experiments show that SOFIE delivers high-quality output, even from unstruc- tured Internet documents.
            16 years ago by @dbenz
             
              attendedwww2009
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              In this paper we present Triplify – a simplistic but effective approach to publish Linked Data from relational databases. Triplify is based on mapping HTTP-URI requests onto relational database queries. Triplify transforms the resulting relations into RDF statements and publishes the data on the Web in various RDF serializations, in particular as Linked Data. The rationale for developing Triplify is that the largest part of information on the Web is already stored in structured form, often as data contained in relational databases, but usually published by Web applications only as HTML mixing structure, layout and content. In order to reveal the pure structured information behind the current Web, we have implemented Triplify as a light-weight software component, which can be easily integrated into and deployed by the numerous, widely installed Web applications. Our approach includes a method for publishing update logs to enable incremental crawling of linked data sources. Triplify is complemented by a library of configurations for common relational schemata and a REST-enabled data source registry. Triplify configurations containing mappings are provided for many popular Web applications, including osCommerce, WordPress, Drupal, Gallery, and phpBB. We will show that despite its light-weight architecture Triplify is usable to publish very large datasets, such as 160GB of geo data from the OpenStreetMap project.
              16 years ago by @dbenz
               
                attendedwww2009
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                16 years ago by @dbenz
                 
                  attendedwww2009
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                  We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or commu- nities, while non-important terms fall into weakly intercon- nected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two ad- vantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise infor- mation in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms exist- ing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
                  16 years ago by @dbenz
                   
                    attendedwww2009
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                    16 years ago by @dbenz
                     
                      annotationgamewww2009phetch
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                      16 years ago by @dbenz
                       
                        annotationgamewww2009peekaboomimage_labelling
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                      publications  1