Topic
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 published on a yearly basis
Papers
More filters
••
01 Sep 2001
TL;DR: XIRQL as discussed by the authors is a query language based on the document-centric view of XML, which integrates logic-based probabilistic IR models, in combination with concepts from the database area.
Abstract: Based on the document-centric view of XML, we present the query language XIRQL. Current proposals for XML query languages lack most IR-related features, which are weighting and ranking, relevance-oriented search, datatypes with vague predicates, and semantic relativism. XIRQL integrates these features by using ideas from logic-based probabilistic IR models, in combination with concepts from the database area. For processing XIRQL queries, a path algebra is presented, that also serves as a starting point for query optimization.
332 citations
••
TL;DR: This work takes advantage of the entire Physical Review publication archive to construct authors' networks where weighted edges, as measured from opportunely normalized citation counts, define a proxy for the mechanism of scientific credit transfer.
Abstract: Recently, the abundance of digital data is enabling the implementation of graph-based ranking algorithms that provide system level analysis for ranking publications and authors. Here, we take advantage of the entire Physical Review publication archive (1893-2006) to construct authors' networks where weighted edges, as measured from opportunely normalized citation counts, define a proxy for the mechanism of scientific credit transfer. On this network, we define a ranking method based on a diffusion algorithm that mimics the spreading of scientific credits on the network. We compare the results obtained with our algorithm with those obtained by local measures such as the citation count and provide a statistical analysis of the assignment of major career awards in the area of physics. A website where the algorithm is made available to perform customized rank analysis can be found at the address http://www.physauthorsrank.org.
331 citations
•
07 Jan 2012TL;DR: In this paper, a system and methods for implementing searches using contextual information associated with a Web page (or other document) that a user is viewing when a query is entered is described.
Abstract: Systems and methods, including user interfaces, are provided for implementing searches using contextual information associated with a Web page (or other document) that a user is viewing when a query is entered. The page includes a contextual search interface that has an associated context vector representing content of the page. When the user submits a search query via the contextual search interface, the query and the context vector are both provided to the query processor and used in responding to the query.
331 citations
••
06 Aug 2009TL;DR: This work proposes an unsupervised method for keyphrase extraction that outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure and guarantees the document to be semantically covered by these exemplar terms.
Abstract: Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.
331 citations
•
30 Jan 2001TL;DR: A re-ranking component in the search engine then refined the initially returned document rankings so that documents that are frequently cited in the initial set of relevant documents were preferred over documents that were less frequently cited within the original set.
Abstract: A search engine for searching a corpus improves the relevancy of the results by refining a standard relevancy score based on the interconnectivity of the initially returned set of documents. The search engine obtains an initial set of relevant documents by matching a user's search terms to an index of a corpus. A re-ranking component in the search engine then refines the initially returned document rankings so that documents that are frequently cited in the initial set of relevant documents are preferred over documents that are less frequently cited within the initial set.
330 citations