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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Proceedings ArticleDOI
01 Jun 1992
TL;DR: A new automated assignment method called “n of 2n” achieves better performance than human experts by sending reviewers more papers than they actually have to review and then allowing them to choose part of their review load themselves.
Abstract: The 117 manuscripts submitted for the Hypertext '91 conference were assigned to members of the review committee, using a variety of automated methods based on information retrieval principles and Latent Semantic Indexing. Fifteen reviewers provided exhaustive ratings for the submitted abstracts, indicating how well each abstract matched their interests. The automated methods do a fairly good job of assigning relevant papers for review, but they are still somewhat poorer than assignments made manually by human experts and substantially poorer than an assignment perfectly matching the reviewers' own ranking of the papers. A new automated assignment method called “n of 2n” achieves better performance than human experts by sending reviewers more papers than they actually have to review and then allowing them to choose part of their review load themselves.

206 citations

Journal ArticleDOI
01 May 1988
TL;DR: A series of experiments were run using the Cranfield test collection to discover techniques to select terms for lists of suggested terms gathered from feedback, nearest neighbors, and term variants of original query terms that would be effective for further retrieval.
Abstract: In an era of online retrieval, it is appropriate to offer guidance to users wishing to improve their initial queries. One form of such guidance could be short lists of suggested terms gathered from feedback, nearest neighbors, and term variants of original query terms. To verify this approach, a series of experiments were run using the Cranfield test collection to discover techniques to select terms for these lists that would be effective for further retrieval. The results show that significant improvement can be expected from this approach to query expansion.

206 citations

Book ChapterDOI
Qi Zhou1, Chong Wang1, Miao Xiong1, Haofen Wang1, Yong Yu1 
11 Nov 2007
TL;DR: This paper explores a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search.
Abstract: Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.

204 citations

Patent
Chandrasekhar Thota1
04 Aug 2005
TL;DR: In this paper, the authors proposed a method of ranking weblogs and blog items by creating a context rank around each blog feed, which represents a sum of a context weight, a track-back weight and a comment weight.
Abstract: A mechanism of ranking weblog or "blog" items is provided. More particularly, the subject ranking mechanisms can facilitate ranking blog feeds and blog items contained therein thus focusing and intelligently delivering content (e.g., blog items) to users. The subject innovation facilitates ranking the blog feeds and blog items by creating a context rank around each blog feed. The context rank represents a sum of a context weight, a track-back weight and a comment weight. Accordingly, this context rank can allow readers to sort blog items in the order of popularity or importance thus effectively reducing content noise.

204 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the application of a novel relevance ranking technique, cover density ranking, to the requirements of Web-based information retrieval, where a typical query consists of a few search terms and a typical result consists of pages indicating several potentially relevant documents.
Abstract: We investigate the application of a novel relevance ranking technique, cover density ranking, to the requirements of Web-based information retrieval, where a typical query consists of a few search terms and a typical result consists of a page indicating several potentially relevant documents. Traditional ranking methods for information retrieval, based on term and inverse document frequencies, have been found to work poorly in this context. Under the cover density measure, ranking is based on term proximity and cooccurrence. Experimental comparisons show performance that compares favorably with previous work.

203 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168