Matches in ScholarlyData for { ?s ?p ?o. }
- 57 creator birgitta-koenig-ries.
- 57 creator michael-klein.
- 57 creator mirco-stern.
- 57 creator ulrich-kuester.
- 57 type InProceedings.
- 57 label "DIANE - An Integrated Approach to Automated Service Discovery, Matchmaking and Composition".
- 57 sameAs 57.
- 57 abstract "Automated matching of semantic service descriptions is the key to automatic service discovery and binding. But when trying to find a match for a certain request it may often happen, that the request cannot be serviced by a single offer but could be handled by combining existing offers. In this case automatic service composition is needed. Although automatic composition is an active field of research it is mainly viewed as a planning problem and treated separatedly from service discovery. In this paper we argue that an integrated approach to the problem is better than seperating these issues as is usually done. We propose an approach that integrates service composition into service discovery and matchmaking to match service requests that ask for multiple connected effects, discuss general issues involved in describing and matching such services and present an efficient algorithm implementing our ideas.".
- 57 hasAuthorList authorList.
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- 57 title "DIANE - An Integrated Approach to Automated Service Discovery, Matchmaking and Composition".
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- item-1 hasContent ulrich-kuester.
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- item-2 hasContent birgitta-koenig-ries.
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- item-3 hasContent mirco-stern.
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- item-4 hasContent michael-klein.
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- 570 creator abhinandan-das.
- 570 creator ashutosh-garg.
- 570 creator mayur-datar.
- 570 creator shyam-rajaram.
- 570 type InProceedings.
- 570 label "Google News Personalization: Scalable Online Collaborative Filtering".
- 570 sameAs 570.
- 570 abstract "Several approaches to collaborative filtering have been studied but seldom have the studies been reported for large (several millions of users and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptible for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.".
- 570 hasAuthorList authorList.
- 570 isPartOf proceedings.
- 570 title "Google News Personalization: Scalable Online Collaborative Filtering".
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- item-1 hasContent abhinandan-das.
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- item-2 hasContent mayur-datar.
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- item-3 hasContent ashutosh-garg.
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- item-4 hasContent shyam-rajaram.
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- 58 creator chunqiang-tang.
- 58 creator gang-luo.
- 58 creator ying-li-tian.
- 58 type InProceedings.
- 58 label "Answering Relationship Queries on the Web".
- 58 sameAs 58.
- 58 abstract "Finding relationships between entities on the Web, e.g., the connections between different places or the commonalities of people, is a novel and challenging problem. Existing Web search engines excel in keyword matching and document ranking, but they cannot well handle many relationship queries. This paper proposes a new method for answering relationship queries on two entities. Our method first respectively retrieves the top Web pages for either entity from a Web search engine. It then matches these Web pages and generates an ordered list of Web page pairs. Each Web page pair consists of one Web page for either entity. The top ranked Web page pairs are likely to contain the relationships between the two entities. One main challenge in the ranking process is to effectively filter out the large amount of noise in the Web pages without losing much useful information. To achieve this, our method assigns appropriate weights to terms in Web pages and intelligently identifies the potential connecting terms that capture the relationships between the two entities. Only those top potential connecting terms with large weights are used to rank Web page pairs. Finally, the top ranked Web page pairs are presented to the searcher. For each such pair, the query terms and the top potential connecting terms are properly highlighted so that the relationships between the two entities can be easily identified. We implemented a prototype on top of the Google search engine and evaluated it under a wide variety of query scenarios. The experimental results show that our method is effective at finding important relationships with low overhead.".
- 58 hasAuthorList authorList.
- 58 isPartOf proceedings.
- 58 title "Answering Relationship Queries on the Web".
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- item-1 hasContent gang-luo.
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- item-2 hasContent chunqiang-tang.
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- item-3 hasContent ying-li-tian.
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- 582 creator andrew-tomkins.
- 582 creator bo-pang.
- 582 creator jasmine-novak.
- 582 creator ravi-kumar.
- 582 type InProceedings.
- 582 label "On Anonymizing Query Logs via Token-based Hashing".
- 582 sameAs 582.
- 582 abstract "In this paper we study the privacy preservation properties of a specific technique for query log anonymization: token-based hashing. In this approach, each query is tokenized, and then a secure hash function is applied to each token. We show that statistical linguistic techniques may be applied to partially compromise the anonymization. We then analyze the specific risks that arise from these partial compromises, focused on revelation of identity from unambiguous names, addresses, and so forth, and the revelation of facts associated with an identity that are deemed to be highly sensitive. Our goal in this work is twofold: to show that token-based hashing is unsuitable for anonymization, and to present a concrete risk analysis framework for evaluating other proposals.".
- 582 hasAuthorList authorList.
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