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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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Patent
03 Jun 2005
TL;DR: In this paper, a method and system that dynamically ranks electronic messages based on their situational and inherent dimensions, which are judged by a set of filters, is presented, where the filters evaluate the different elemental metadata constituting a message and produce a priority value based on filters relevance and importance.
Abstract: A method and system that dynamically ranks electronic messages based on their situational and inherent dimensions, which are judged by a set of filters. These filters evaluate the different elemental metadata constituting a message and produce a priority value based on filters relevance and importance. The system iterates through queued messages, examine the structured content for expected attributes, statistically analyze unstructured content, apply dynamically weighted rules and policies to deliver a priority ranking, and then display the message and its vital attributes in accordance with the priority ranking. The system also adaptive learns and adjusts its weighted rules and policies to permit priority ranking to change on real-time or interval-based (may be user-defined) schedule. The system includes a GUI for increasing reading and processing efficiency. The GUI performs supervised and unsupervised learning from the user's behaviors, and displays messages in accordance with their priority classification.

228 citations

Proceedings ArticleDOI
13 Jun 2004
TL;DR: This paper provides an elegant definition of relaxation on structure and defines primitive operators to span the space of relaxations for ranking schemes and proposes natural ranking schemes that adhere to these principles.
Abstract: Querying XML data is a well-explored topic with powerful database-style query languages such as XPath and XQuery set to become W3C standards. An equally compelling paradigm for querying XML documents is full-text search on textual content. In this paper, we study fundamental challenges that arise when we try to integrate these two querying paradigms.While keyword search is based on approximate matching, XPath has exact match semantics. We address this mismatch by considering queries on structure as a "template", and looking for answers that best match this template and the full-text search. To achieve this, we provide an elegant definition of relaxation on structure and define primitive operators to span the space of relaxations. Query answering is now based on ranking potential answers on structural and full-text search conditions. We set out certain desirable principles for ranking schemes and propose natural ranking schemes that adhere to these principles. We develop efficient algorithms for answering top-K queries and discuss results from a comprehensive set of experiments that demonstrate the utility and scalability of the proposed framework and algorithms.

228 citations

Proceedings ArticleDOI
26 Apr 2010
TL;DR: An approximate index structure summarising graph-structured content of sources adhering to Linked data principles is developed, an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary is provided, and the system is evaluated using synthetically generated queries.
Abstract: Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

228 citations

Proceedings ArticleDOI
04 Nov 2002
TL;DR: An approach to retrieval of documents that contain of both free text and semantically enriched markup in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language is described.
Abstract: We describe an approach to retrieval of documents that contain of both free text and semantically enriched markup. In particular, we present the design and implementation prototype of a framework in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language. These statements provide both structured and semi-structured information about the documents and their content. We claim that indexing text and semantic markup together will significantly improve retrieval performance. Our approach allows inferencing to be done over this information at several points: when a document is indexed, when a query is processed and when query results are evaluated.

227 citations

Proceedings ArticleDOI
28 Mar 2011
TL;DR: It is shown that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes, and a context-sensitive query auto completion algorithm is proposed, NearestCompletion, which outputs the completions of the users' input that are most similar to the context queries.
Abstract: Query auto completion is known to provide poor predictions of the user's query when her input prefix is very short (e.g., one or two characters). In this paper we show that context, such as the user's recent queries, can be used to improve the prediction quality considerably even for such short prefixes. We propose a context-sensitive query auto completion algorithm, NearestCompletion, which outputs the completions of the user's input that are most similar to the context queries. To measure similarity, we represent queries and contexts as high-dimensional term-weighted vectors and resort to cosine similarity. The mapping from queries to vectors is done through a new query expansion technique that we introduce, which expands a query by traversing the query recommendation tree rooted at the query.In order to evaluate our approach, we performed extensive experimentation over the public AOL query log. We demonstrate that when the recent user's queries are relevant to the current query she is typing, then after typing a single character, NearestCompletion's MRR is 48% higher relative to the MRR of the standard MostPopularCompletion algorithm on average. When the context is irrelevant, however, NearestCompletion's MRR is essentially zero. To mitigate this problem, we propose HybridCompletion, which is a hybrid of NearestCompletion with MostPopularCompletion. HybridCompletion is shown to dominate both NearestCompletion and MostPopularCompletion, achieving a total improvement of 31.5% in MRR relative to MostPopularCompletion on average.

227 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