Matches in ScholarlyData for { ?s ?p ?o. }
- item-4 hasContent wolfgang-nejdl.
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- 570 creator andreas-harth.
- 570 creator axel-polleres.
- 570 creator juergen-umbrich.
- 570 creator kai-uwe-sattler.
- 570 creator katja-hose.
- 570 creator marcel-karnstedt.
- 570 type InProceedings.
- 570 label "Data Summaries for On-demand Queries over Linked Data".
- 570 sameAs 570.
- 570 abstract "Typical approaches for search and querying over structured Web Data collect (crawl) and pre-process (index) large amounts of data before allowing for query answering in a central data warehouse. This time-consuming pre-processing phase decreases the freshness of query results and only uses to a limited degree the benefits of Linked Data where structured data is accessible live and up-to-date at distributed Web resources that may change constantly. An ideal query answering system for Linked Data should return always current answers in a reasonable amount of time, even on corpora as large as the web. Query processors evaluating queries directly on the life sources require knowledge of the contents of data sources. In the current paper we develop and evaluate a probabilistic index structure for covering graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on the web exploiting this structure, and evaluate the system using synthetically generated queries. We find that our lightweight index structure enable more complete query results over Linked Data compared to direct lookup approaches, while keeping the overhead for additional lookups and index maintenance low.".
- 570 hasAuthorList authorList.
- 570 isPartOf proceedings.
- 570 keyword "Querying".
- 570 keyword "distributed approaches".
- 570 keyword "including combinations with statistics".
- 570 keyword "searching the Semantic/Data Web".
- 570 keyword "soft computing".
- 570 title "Data Summaries for On-demand Queries over Linked Data".
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- item-1 hasContent andreas-harth.
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- item-2 hasContent katja-hose.
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- item-3 hasContent marcel-karnstedt.
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- item-4 hasContent axel-polleres.
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- item-5 hasContent kai-uwe-sattler.
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- item-6 hasContent juergen-umbrich.
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- 572 creator boris-chidlovskii.
- 572 creator matthijs-hovelynck.
- 572 type InProceedings.
- 572 label "Multi-modality in One-class Classification".
- 572 sameAs 572.
- 572 abstract "We propose a method of improving classification performance in a one-class setting by combining classifiers of different modalities. We applied it to the problem of distinguishing responsive documents in a corpus of e-mails, like Enron Corpus. We propose a way to turn the social network that is implicit in a large body of electronic communication into valuable features for classifying the exchanged documents. Working in a one-class setting we take a semi-supervised approach based on the Mapping Convergence framework. We propose an alternative interpretation, that allows for broader applicability by dismissing the prerequisite that positive and negative items must be naturally separable. We propose an extension to the one-class evaluation framework which turns to be useful even when very little positive training examples are available. We extent the one-class setting to the {\it co-training} principle that enables us to take advantage of the availability of multiple redundant views on the data. We evaluate this extension on the Enron Corpus, classifying towards responsiveness.".
- 572 hasAuthorList authorList.
- 572 isPartOf proceedings.
- 572 keyword "Scalable machine learning".
- 572 keyword "data mining techniques for media data analysis".
- 572 title "Multi-modality in One-class Classification".
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- item-1 hasContent boris-chidlovskii.
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- item-2 hasContent matthijs-hovelynck.
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- 578 creator ed-chang.
- 578 creator hongji-bao.
- 578 type InProceedings.
- 578 label "AdHeat: An Influence-based Diffusion Model for Propagating Hints to Match Ads".
- 578 sameAs 578.
- 578 abstract "In this paper, we present AdHeat, a social ads model considering {\em user influence} in addition to {\em relevance} for matching ads. Traditionally, ads placement employs the {\em relevance} model. Such a model matches ads with Web page content, user interests, or both. We have observed, however, on social networks that the relevance model suffers from two shortcomings. First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise. Second, because influential users' contents and activities are attractive to other users, {\em hint words} summarizing their expertise and activities may be widely preferred. Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints. We performed experiments on a large online Q\&A community with half a million users. The experimental results show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins.".
- 578 hasAuthorList authorList.
- 578 isPartOf proceedings.
- 578 keyword "Monetizing digital media".
- 578 keyword "the social web".
- 578 keyword "user generated content".
- 578 title "AdHeat: An Influence-based Diffusion Model for Propagating Hints to Match Ads".
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- item-1 hasContent hongji-bao.
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- item-2 hasContent ed-chang.
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- 583 creator lei-zhang-2.
- 583 creator rui-cai.
- 583 type InProceedings.
- 583 label "A Pattern Tree-based Approach to Learning URL Normalization Rules".