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- 592 creator bin-tan.
- 592 creator fuchun-peng.
- 592 type InProceedings.
- 592 label "Unsupervised Query Segmentation using Generative Language Models and Wikipedia".
- 592 sameAs 592.
- 592 abstract "In this paper, we propose a novel unsupervised approach to query segmentation, which is an important task in Web search. We use a generative query model to recover a query’s underlying concepts that compose its original segmented form. The model’s parameters are estimated using an expectation-maximization (EM) algorithm, optimizing the minimum description length objective function on a partial corpus that is specific to the query. To augment this unsupervised learning, we incorporate evidence from Wikipedia. Experiments show that our approach dramatically improves performance over the traditional approach based on mutual information, and produces comparable results with a supervised method. In particular, the basic generative language model contributes a 7.4% improvement over the mutual information based method (measured by segment F1 on the Intersection test set). EM optimization further improves the performance by 14.3%. Additional knowledge from Wikipedia provides another improvement of 24.3%, adding up to a total of 46% improvement (from 0.530 to 0.774).".
- 592 hasAuthorList authorList.
- 592 hasTopic World_Wide_Web.
- 592 isPartOf proceedings.
- 592 keyword "content discovery".
- 592 keyword "query segmentation".
- 592 title "Unsupervised Query Segmentation using Generative Language Models and Wikipedia".
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- item-1 hasContent bin-tan.
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- item-2 hasContent fuchun-peng.
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- 597 creator christopher-olston.
- 597 creator sandeep-pandey.
- 597 type InProceedings.
- 597 label "Recrawl Scheduling Based on Information Longevity".
- 597 sameAs 597.
- 597 abstract "It is crucial for a web crawler to distinguish between ephemeral and persistent content. It is usually not worth crawling ephemeral content (e.g., quote of the day), because by the time it reaches the index it is no longer representative of the web page from which it was acquired. On the other hand, content that persists across multiple page updates (e.g., recent blog postings) may be worth acquiring, because it matches the page's true content for a sustained period of time. In this paper we characterize the longevity of information found on the web, via both empirical measurements and a generative model that coincides with these measurements. We then develop new recrawl scheduling policies that take longevity into account. As we show via experiments over real web data, our policies obtain better freshness at lower cost, compared with previous approaches.".
- 597 hasAuthorList authorList.
- 597 hasTopic World_Wide_Web.
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- 597 keyword "crawling".
- 597 keyword "information longevity".
- 597 title "Recrawl Scheduling Based on Information Longevity".
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- item-1 hasContent christopher-olston.
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- item-2 hasContent sandeep-pandey.
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- 606 creator andrea-pugliese.
- 606 creator octavian-udrea.
- 606 creator vs-subrahmanian.
- 606 type InProceedings.
- 606 label "Scaling RDF with Time".
- 606 sameAs 606.
- 606 abstract "The World Wide Web Consortium's RDF standard primarily consists of (subject,property,object) triples that specify the value that a given subject has for a given property. However, it is frequently the case that even for a fixed subject and property, the value varies with time (examples of such properties include salary, weight, etc.). As a consequence, efforts have been made to annotate RDF triples with "valid time" intervals. However, to date, no proposals exist for efficient indexing of such temporal RDF databases. It is clearly beneficial to store RDF data in a relational DB - however, standard relational indexes are inadequately equipped to handle RDF's graph structure. In this paper, we propose the tGRIN index structure that builds a specialized index for temporal RDF that is physically stored in an RDBMS. Past efforts to store RDF in relational stores include Jena2 from HP, Sesame from OpenRDF.org, and 3store from the University of Southampton. We show that even when these efforts are augmented with well known temporal indexes like R+ trees, SR-trees, ST-index, and MAP21, the tGRIN index exhibits superior performance. In terms of index build time, tGRIN takes two thirds or less of the time used by any other system, and it uses a comparable amount of memory and less disk space than Jena, Sesame and 3store. More importantly, tGRIN can answer queries three to six times faster for average query graph patterns and five to ten times faster for complex queries than these systems.".
- 606 hasAuthorList authorList.
- 606 hasTopic World_Wide_Web.
- 606 isPartOf proceedings.
- 606 keyword "RDF indexing".
- 606 keyword "Resource Description Framework".
- 606 keyword "Temporal RDF".
- 606 title "Scaling RDF with Time".
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- item-1 hasContent andrea-pugliese.
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- item-2 hasContent octavian-udrea.
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- 607 creator bernhard-schueler.
- 607 creator sergej-sizov.
- 607 creator steffen-staab.
- 607 creator thanh-tran.
- 607 type InProceedings.
- 607 label "Querying for Meta Knowledge".
- 607 sameAs 607.
- 607 abstract "The Semantic Web is based on accessing and reusing RDF data from many different sources, which one may assign different levels of authority and credibility. Existing Semantic Web query languages, like SPARQL, have targeted the retrieval, combination and reuse of facts, but have so far ignored all aspects of meta knowledge, such as origins, authorship, recency or certainty of data, to name but a few. In this paper, we present an original, generic, formalized and implemented approach for managing many dimensions of meta knowledge, like source, authorship, certainty and others. The approach re-uses existing RDF modeling possibilities in order to represent meta knowledge. Then, it extends SPARQL query processing in such a way that given a SPARQL query for data, one may request meta knowledge without modifying the query proper. Thus, our approach achieves highly flexible and automatically coordinated querying for data and meta knowledge, while completely separating the two areas of concern.".
- 607 hasAuthorList authorList.
- 607 hasTopic World_Wide_Web.
- 607 isPartOf proceedings.
- 607 keyword "SPARQL".
- 607 keyword "meta knowledge".
- 607 keyword "provenance".
- 607 title "Querying for Meta Knowledge".
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