Matches in ScholarlyData for { ?s ?p ?o. }
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- item-1 hasContent giuseppe-carenini.
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- item-2 hasContent raymond-ng.
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- item-3 hasContent xiaodong-zhou.
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- 632 creator danushka-bollegala.
- 632 creator mitsuru-ishizuka.
- 632 creator yutaka-matsuo.
- 632 type InProceedings.
- 632 label "Measuring Semantic Similarity between Words Using Web Search Engines".
- 632 sameAs 632.
- 632 abstract "Semantic similarity measures play important roles in information retrieval and Natural Language Processing. In information retrieval, semantic similarity measures are used in automatic query suggestion and expansion. Previous work in semantic web-related applications such as community mining, relation extraction, automatic meta data extraction have used various semantic similarity measures. Despite the usefulness of semantic similarity measures in these applications, robustly measuring semantic similarity between two words (or entities) remains a challenging task. Semantic similarity is a dynamic phenomenon that changes over time and across domains. In this paper, we propose a robust semantic similarity measure that uses the information available on the Web to measure similarity between words or entities. We propose a method that exploits page counts and text snippets returned by a Web search engine to measure semantic similarity between words. We define various similarity scores for two given words \textit{P} and \textit{Q}, using the page counts for the queries \textit{P}, \textit{Q} and \textit{P AND Q}. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexico-syntactic patterns from text snippets. These different similarity scores are integrated using support vector machines, to leverage a robust semantic similarity measure. Experimental results on Miller-Charles benchmark dataset show that the proposed measure outperforms all the existing web-based semantic similarity measures by a wide margin, achieving a correlation coefficient of $0.834$. Moreover, the proposed semantic similarity measure significantly improves the accuracy ($F$-measure of $0.78$) in a community mining task, and improves accuracy in a entity disambiguation task, thereby verifying the capability of the proposed measure to capture semantic similarity using web content.".
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- 632 title "Measuring Semantic Similarity between Words Using Web Search Engines".
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- item-1 hasContent danushka-bollegala.
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- item-2 hasContent yutaka-matsuo.
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- item-3 type ListItem.
- item-3 hasContent mitsuru-ishizuka.
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- 635 creator hana-shepherd.
- 635 creator harry-halpin.
- 635 creator valentin-robu.
- 635 type InProceedings.
- 635 label "The Complex Dynamics of Collaborative Tagging".
- 635 sameAs 635.
- 635 abstract "The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from tagged sites on the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used analyze the meaning of particular tags given their relationship to other tags.".
- 635 hasAuthorList authorList.
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- 635 title "The Complex Dynamics of Collaborative Tagging".
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- item-1 hasContent harry-halpin.
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- item-2 hasContent valentin-robu.
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- item-3 hasContent hana-shepherd.
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- 649 creator i-v-ramakrishnan.
- 649 creator jalal-mahmud.
- 649 creator yevgen-borodin.
- 649 type InProceedings.
- 649 label "CSurf: A Context-Driven Non-Visual Web-Browser".
- 649 sameAs 649.
- 649 abstract "Web sites are designed for graphical mode of interaction. Sighted users can "cut to the chase" and quickly identify relevant information in Web pages. On the contrary, indi- viduals with visual disabilities have to use screen-readers to browse the Web. As screen-readers process pages sequen- tially and read through everything, Web browsing can be- come strenuous and time-consuming. Although, the use of shortcuts and searching offers some improvements, the prob- lem still remains. In this paper, we address the problem of information overload in non-visual Web access using the notion of context. Our prototype system, CSurf, embodying our approach, provides the usual features of a screen-reader. However, when a user follows a link, CSurf captures the context of the link using a simple topic-boundary detection technique, and uses it to identify relevant information on the next page with the help of a Support Vector Machine, a statistical machine-learning model. Then, CSurf reads the Web page starting from the most relevant section, identified by the model. We conducted a series experiments to eval- uate the performance of CSurf against the state-of-the-art screen-reader, JAWS. Our results show that the use of con- text can potentially save browsing time and substantially improve browsing experience of visually disabled people.".
- 649 hasAuthorList authorList.
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- 649 title "CSurf: A Context-Driven Non-Visual Web-Browser".
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- item-1 hasContent jalal-mahmud.
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- item-2 hasContent yevgen-borodin.
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- item-3 hasContent i-v-ramakrishnan.
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- 656 creator bijan-parsia.
- 656 creator james-hendler.
- 656 creator vladimir-kolovski.
- 656 type InProceedings.
- 656 label "Analyzing Web Access Control Policies".
- 656 sameAs 656.
- 656 abstract "XACML has emerged as a popular access control language on the Web, but because of its rich expressiveness, it has proved difficult to analyze in an automated fashion. Previous attempts to analyze XACML policies either use propositional logic or full First-Order logic. In this paper, we present a formalization of XACML using Description Logics (DL) . This formalization allows us to extend the subset of XACML supported by propositional logic-based analysis tools; we also provide a new analysis service (policy redundancy). Mapping XACML to description logics allows us to use off-the-shelf DL reasoners for analysis tasks such as policy comparison, policy verification and querying. We provide empirical evaluation of a policy analysis tool that was implemented on top of open source reasoner Pellet.".
- 656 hasAuthorList authorList.