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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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Book ChapterDOI
20 Oct 2003
TL;DR: A novel approach for determining relevance in ontology-based searching for information, which exploits the "full potential" of the semantics of such a semantically-based link structure is presented.
Abstract: One of the vital problems in the searching for information is the ranking of the retrieved results, because users make typically very short queries (2-3 terms) and tend to consider only the first ten results. In traditional IR approaches the relevance of the results is determined only by analysing the underlying information repository (content and hyperlink structure), which leads to the weak relevance model. On the other hand, in the Semantic Web the querying process is supported by an ontology such that other important sources for determining the relevance of results can be considered: the structure of the underlying domain and the characteristics of the searching process. In this paper we present a novel approach for determining relevance in ontology-based searching for information, which exploits the "full potential" of the semantics of such a semantically-based link structure. We present several analyses about how a Semantic Web querying mechanism can benefit of using our ranking approach.

100 citations

Journal ArticleDOI
TL;DR: The results from the experiments show that the use of a medical ontology to expand the queries greatly improves the results.

100 citations

Journal ArticleDOI
TL;DR: The architecture and associated algorithms for generating the supported subsuming queries and filters for Boolean queries in one rich front end language are introduced and it is shown that generated subsumed queries return a minimal number of documents.
Abstract: Searching over heterogeneous information sources is difficult because of the nonuniform query languages. Our approach is to allow a user to compose Boolean queries in one rich front end language. For each user query and target source, we transform the user query into a subsuming query that can be supported by the source but that may return extra documents. The results are then processed by a filter query to yield the correct final result. We introduce the architecture and associated algorithms for generating the supported subsuming queries and filters. We show that generated subsuming queries return a minimal number of documents; we also discuss how minimal cost filters can be obtained. We have implemented prototype versions of these algorithms and demonstrated them on heterogeneous Boolean systems.

100 citations

Patent
10 Apr 1996
TL;DR: In this article, a system and method provides for indexing and retrieval of stored documents using a decomposition of words in the documents in n-grams, or linear word subunits.
Abstract: A system and method provides for indexing and retrieval of stored documents using a decomposition of words in the documents in n-grams, or linear word subunits The documents are indexed as pages in a number of banks For each bank there is a bank index The individual n-grams are identified for each page are stored in the bank index Each bank index further contains an entry map that indicates whether a given n-gram is present in any of the pages of the bank, and then provides an index to a page map that further indicates which page in the bank contains the n-gram When a search query is input, the query words are decomposed into their n-grams The query word n-grams are compared first with entry maps to determine if the query word n-grams appear on any page in the bank If so, the associated page map is traversed to determine which page in the bank contains the query word n-grams The n-grams on the page are compared with the query word n-grams to determine the presence of an match therebetween Matching pages are flagged When all pages in all banks have been processed, the pages are consolidated with respect to the documents to which they belong, resulting in a list of documents that match the search query The results are displayed to a user

100 citations

Journal Article
TL;DR: A local form of the bipartite ranking problem where the goal is to focus on the best instances and a methodology based on the construction of real-valued scoring functions which involve empirical quantiles of the scores is proposed.
Abstract: We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scores. We first state the problem of finding the best instances which can be cast as a classification problem with mass constraint. Next, we develop special performance measures for the local ranking problem which extend the Area Under an ROC Curve (AUC) criterion and describe the optimal elements of these new criteria. We also highlight the fact that the goal of ranking the best instances cannot be achieved in a stage-wise manner where first, the best instances would be tentatively identified and then a standard AUC criterion could be applied. Eventually, we state preliminary statistical results for the local ranking problem.

100 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