Matches in ScholarlyData for { ?s ?p ?o. }
- 247 type InProceedings.
- 247 label "Privacy-Enhancing Personalized Web Search".
- 247 sameAs 247.
- 247 abstract "Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user's interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.".
- 247 hasAuthorList authorList.
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- 247 title "Privacy-Enhancing Personalized Web Search".
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- item-1 hasContent yabo-xu.
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- item-2 hasContent benyu-zhang.
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- item-3 hasContent zheng-chen.
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- item-4 hasContent ke-wang.
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- 272 creator armin-haller.
- 272 creator eyal-oren.
- 272 creator renaud-delbru.
- 272 creator sebastian-gerke.
- 272 type InProceedings.
- 272 label "ActiveRDF: Object-Oriented Semantic Web Programming".
- 272 sameAs 272.
- 272 abstract "Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly, given the different nature of Semantic Web data, as can for example be seen in the semantics of class membership, inheritance relations, and object conformance to schemas.<br /><br /> We present ActiveRDF, an object-oriented API for managing RDF data that offers full manipulation and querying of RDF data, does not rely on a schema and fully conforms to RDF(S) semantics. ActiveRDF can be used with different RDF data stores, adapters have been implemented to generic SPARQL endpoints, Sesame, Jena, Redland and YARS and new adapters can be added easily. In addition, integration with the popular Ruby on Rails framework enables fast development of Semantic Web applications.".
- 272 hasAuthorList authorList.
- 272 isPartOf proceedings.
- 272 title "ActiveRDF: Object-Oriented Semantic Web Programming".
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- item-1 hasContent eyal-oren.
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- item-2 hasContent renaud-delbru.
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- item-3 hasContent armin-haller.
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- item-4 hasContent sebastian-gerke.
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- 279 creator l-libkin.
- 279 creator s-kolahi.
- 279 type InProceedings.
- 279 label "XML Design for Relational Storage".
- 279 sameAs 279.
- 279 abstract "Design principles for XML schemas that eliminate redundancies and avoid update anomalies have been studied recently. Several normal forms, generalizing those for relational databases, have been proposed. All of them, however, are based on the assumption of a native XML storage, while in practice most of XML data is stored in relational databases.<br /><br /> In this paper we study XML design and normalization for relational storage of XML documents. To be able to relate and compare XML and relational designs, we use an information-theoretic framework that measures information content in relations and documents, with higher values corresponding to lower levels of redundancy. We show that most common relational storage schemes preserve the notion of being well-designed (i.e., anomalies- and redundancy-free). Thus, existing XML normal forms guarantee well-designed relational storages as well. We further show that if this perfect option is not achievable, then a slight restriction on XML constraints guarantees a ``second-best'' relational design, according to possible values of the information-theoretic measure. We finally consider an edge-based relational representation of XML documents, and show that while it has similar information-theoretic properties with other relational representations, it can behave significantly worse in terms of enforcing integrity constraints.".
- 279 hasAuthorList authorList.
- 279 isPartOf proceedings.
- 279 title "XML Design for Relational Storage".
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- item-1 hasContent l-libkin.
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- item-2 hasContent s-kolahi.
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- 286 creator hang-li.
- 286 creator tao-qin.
- 286 creator tie-yan-liu.
- 286 creator yuting-liu.
- 286 creator zhiming-ma.
- 286 type InProceedings.
- 286 label "Supervised Rank Aggregation".
- 286 sameAs 286.
- 286 abstract "This paper is concerned with rank aggregation, the task of combining results of individual ranking functions in meta-search. Previously, rank aggregation was performed mainly by using unsupervised methods. It is hard for the unsupervised approach to improve ranking performances by leveraging the use of labeled data, when such data is available. We propose employing a supervised learning approach to perform the task, which we refer to as 'Supervised Rank Aggregation'. We set up a general framework for conducting rank aggregation with supervised learning, in which learning for rank aggregation is formalized as an optimization issue that minimizes disagreements with the labeled ground truth data. As case study, we focus on Markov Chain based rank aggregation in this paper. The optimization problem is not a convex optimization problem for Markov Chain based methods, however, and thus is hard to solve. We transform the optimization problem into semi-definite programming and give proofs on the correctness. Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods.".
- 286 hasAuthorList authorList.
- 286 isPartOf proceedings.
- 286 title "Supervised Rank Aggregation".
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