Combining the evidence of multiple query representations for information retrieval

Belkin, Nicholas J.; Kantor, Paul B.; Fox, Edward A.; Shaw, Joseph A.

doi:10.1016/0306-4573(94)00057-a

Cited by 214 publications

(125 citation statements)

References 5 publications

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“…Belkin, et al. [2] examined both combining the results of multiple query formulations and combining retrieval results obtained from multiple retrieval systems. Voorhees, et al [20,21] proposed a merging approach in which the number of documents retrieved from a database was based on the estimated usefulness of that database.…”

Section: Distributed Retrieval Database Selection and Results Mergingmentioning

confidence: 99%

The impact of database selection on distributed searching

Powell

French

Callan

et al. 2000

Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

101

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The proliferation of online information resources increases the importance of effective and efficient distributed searching. Distributed searching is cast in three parts -database selection, query processing, and results merging. In this paper we examine the effect of database selection on retrieval performance. We look at retrieval performance in three different distributed retrieval testbeds and distill some general results. First we find that good database selection can result in better retrieval effectiveness than can be achieved in a centralized database. Second we find that good performance can be achieved when only a few sites are selected and that the performance generally increases as more sites are selected. Finally we find that when database selection is employed, it is not necessary to maintain collection wide information (CWI), e.g. global idf. Local information can be used to achieve superior performance. This means that distributed systems can be engineered with more autonomy and less cooperation. This work suggests that improvements in database selection can lead to broader improvements in retrieval performance, even in centralized (i.e. single database) systems. Given a centralized database and a good selection mechanism, retrieval performance can be improved by decomposing that database conceptually and employing a selection step.

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Section: Distributed Retrieval Database Selection and Results Mergingmentioning

confidence: 99%

The impact of database selection on distributed searching

Powell

French

Callan

et al. 2000

Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

101

View full text Add to dashboard Cite

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“…Each image i has multiple rankings {r i,cat , r i,glau , r i,prot , r i,deut , r i,trit }. In [4,15], various methods for combining multiple rankings of a set of search results are presented. The purpose of each method is to compute a combined ranking for an image, based on its multiple individual rankings.…”

Section: Evaluation With Simulated Usersmentioning

confidence: 99%

Accessibility-based reranking in multimedia search engines

Kalamaras

Dimitriou

Drosou

et al. 2016

Multimed Tools Appl

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Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context.

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“…[38][39] Thus far, we have described data fusion as involving the combination of multiple rankings of a database to produce a single, fused ranking that is the output from a similarity search. Our initial studies used simple arithmetic fusion rules that had first been described for the combination of rankings in textual information retrieval systems [40] as exemplified in Table 2. For example, the fused ranking for a database structure might be the sum of its rank positions in the individual rankings that were to be combined, or the fused ranking might be based on the sum of the similarity scores in the individual similarity searches.…”

Section: Data Fusionmentioning

confidence: 99%