Semantic caching of Web queries

Chidlovskii, Boris; Borghoff, Uwe M.

doi:10.1007/s007780050080

Cited by 59 publications

(34 citation statements)

References 24 publications

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“…2000), semantic cache for web queries (Chidlovskii B and Borghoff U. M., 2000), (Qiong L and Jaffrey F. N., 2001), xml based semantic cache (Mandhani B. and Suciu D., 2005), (Sanaullah, M., 2008) and description logic based subsumption analysis in semantic cache (Baader et al, 1991a) (Hollunder et al, 1990) techniques.…”

Section: Query Processing Techniquesmentioning

confidence: 99%

“…Because the user query in this case is not a URL, and extracting qualified tuples against an individual keyword or whole query from page headers is not possible (Chidlovskii B and Borghoff U. M., 2000), (Qiong L and Jaffrey F. N., 2001). Semantic cache was introduced as an alternative to plain page caching where cache is managed as semantic regions.…”

Section: Semantic Reasoning For Web Queriesmentioning

confidence: 99%

“…Such as (Chidlovskii B and Borghoff U. M., 2000) addressed both semantic cache management and query processing of web queries for meta-searcher systems. Their technique is based on a signature file method.…”

Section: Semantic Reasoning For Web Queriesmentioning

confidence: 99%

See 2 more Smart Citations

Semantic Cache Reasoners

Abbas¹,

Qadir²,

Afzal³

2012

Semantics - Advances in Theories and Mathematical Models

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Section: Query Processing Techniquesmentioning

confidence: 99%

Section: Semantic Reasoning For Web Queriesmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Cache Reasoners

Abbas¹,

Qadir²,

Afzal³

2012

Semantics - Advances in Theories and Mathematical Models

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“…This reduces the problem to set containment of the number of objects in the cache that could satisfy some condition of semantic overlap being verifiable in linear time. In this context, Chidlovskii et al [4] have proposed evaluation methods for web queries using semantic caches. The proposed methods store clusters of cooccurring answers identified as regions, each of these associated to a signature.…”

Section: Related Workmentioning

confidence: 99%

A Last-Resort Semantic Cache for Web Queries

Ferrarotti

Mendoza²

2009

String Processing and Information Retrieval

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Abstract. We propose a method to evaluate queries using a last-resort semantic cache in a distributed Web search engine. The cache stores a group of frequent queries and for each of these queries it keeps minimal data, that is, the list of machines that produced their answers. The method for evaluating the queries uses the inverse frequency of the terms in the queries stored in the cache (Idf) to determine when the results recovered from the cache are a good approximation to the exact answer set. Experiments show that the method is effective and efficient.

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“…This method is used to dynamically rank search nodes in accordance with specific query terms. Whereas the method proposed in [9] views the query terms and search node IDs stored in the LCache as a semantic cache [4,3]. The approach presented in [9] determines which processors are most likely to contain good approximated results for queries that are not found both in the RCache and in the LCache.…”

Section: Introductionmentioning

confidence: 99%

Learning to Distribute Queries into Web Search Nodes

Mendoza¹,

Ferrarotti²,

Poblete³

2010

Lecture Notes in Computer Science

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Abstract. Web search engines are composed of a large set of search nodes and a broker machine that feeds them with queries. A location cache keeps minimal information in the broker to register the search nodes capable of producing the top-N results for frequent queries. In this paper we show that it is possible to use the location cache as a training dataset for a standard machine learning algorithm and build a predictive model of the search nodes expected to produce the best approximated results for queries. This can be used to prevent the broker from sending queries to all search nodes under situations of sudden peaks in query traffic and, as a result, avoid search node saturation. This paper proposes a logistic regression model to quickly predict the most pertinent search nodes for a given query.

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Semantic caching of Web queries

Cited by 59 publications

References 24 publications

Semantic Cache Reasoners

Semantic Cache Reasoners

A Last-Resort Semantic Cache for Web Queries

Learning to Distribute Queries into Web Search Nodes

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