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.
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IBM1
TL;DR: In this paper, a set of sub queries consisting of different media types are used to search a collection of multimedia documents in a database and then the interim results are combined in a global result object that is processed using a user specification.
Abstract: A query comprising of sub queries, each of which could be of different media types are used to search a collection of multimedia documents in a database. These sub queries are parsed according to media type and operators/functions between these sub queries are recorded creating a set of query objects and query operator objects. The query interface than passes the query objects to the appropriate application programming interfaces (API's) of the various search engines. Furthermore, it applies the query object operators to the respective interim results obtained by executing a query object. Then the interim results are combined in a global result object that is processed using a user specification to produce a single combined result list that conforms to user specified requirements.
261 citations
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28 Jul 2004TL;DR: In this paper, a user interface allows a user to specify queries using a combination of keywords and examples images, and the image retrieval system finds images with keywords that match the keywords in the query and/or images with similar low-level features, such as color, texture, and shape.
Abstract: An image retrieval system performs both keyword-based and content-based image retrieval. A user interface allows a user to specify queries using a combination of keywords and examples images. Depending on the input query, the image retrieval system finds images with keywords that match the keywords in the query and/or images with similar low-level features, such as color, texture, and shape. The system ranks the images and returns them to the user. The user interface allows the user to identify images that are more relevant to the query, as well as images that are less or not relevant to the query. The user may alternatively elect to refine the search by selecting one example image from the result set and submitting its low-level features in a new query. The image retrieval system monitors the user feedback and uses it to refine any search efforts and to train itself for future search queries. In the described implementation, the image retrieval system seamlessly integrates feature-based relevance feedback and semantic-based relevance feedback.
261 citations
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TL;DR: This article demonstrates that the similar terms identified by cooccurrence data in a query expansion system tend to occur very frequently in the database that is being searched.
Abstract: Term cooccurrence data has been extensively used in document retrieval systems for the identification of indexing terms that are similar to those that have been specified in a user query: these similar terms can then be used to augment the original query statement. Despite the plausibility of this approach to query expansion, the retrieval effectiveness of the expanded queries is often no greater than, or even less than, the effectiveness of the unexpanded queries. This article demonstrates that the similar terms identified by cooccurrence data in a query expansion system tend to occur very frequently in the database that is being searched. Unfortunately, frequent terms tend to discriminate poorly between relevant and nonrelevant documents, and the general effect of query expansion is thus to add terms that do little or nothing to improve the discriminatory power of the original query.
261 citations
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NEC1
TL;DR: The notion of a multi-granularity information and processing structure is used to support efficient query expansion, which involves an indexing phase, a query processing and a ranking phase.
Abstract: A method and apparatus for efficient query expansion using reduced size indices and for progressive query processing. Queries are expanded conceptually, using semantically similar and syntactically related words to those specified by the user in the query to reduce the chances of missing relevant documents. The notion of a multi-granularity information and processing structure is used to support efficient query expansion, which involves an indexing phase, a query processing and a ranking phase. In the indexing phase, semantically similar words are grouped into a concept which results in a substantial index size reduction due to the coarser granularity of semantic concepts. During query processing, the words in a query are mapped into their corresponding semantic concepts and syntactic extensions, resulting in a logical expansion of the original query. Additionally, the processing overhead is avoided. The initial query words can then be used to rank the documents in the answer set on the basis of exact, semantic and syntactic matches and also to perform progressive query processing.
260 citations
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16 Oct 2006TL;DR: XSnippet is developed, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.
Abstract: It is common practice for software developers to use examples to guide development efforts. This largely unwritten, yet standard, practice of "develop by example" is often supported by examples bundled with library or framework packages, provided in textbooks, and made available for download on both official and unofficial web sites. However, the vast number of examples that are embedded in the billions of lines of already developed library and framework code are largely untapped. We have developed XSnippet, a context-sensitive code assistant framework that allows developers to query a sample repository for code snippets that are relevant to the programming task at hand. In particular, our work makes three primary contributions. First, a range of queries is provided to allow developers to switch between a context-independent retrieval of code snippets to various degrees of context-sensitive retrieval for object instantiation queries. Second, a novel graph-based code mining algorithm is provided to support the range of queries and enable mining within and across method boundaries. Third, an innovative context-sensitive ranking heuristic is provided that has been experimentally proven to provide better ranking for best-fit code snippets than context-independent heuristics such as shortest path and frequency. Our experimental evaluation has shown that XSnippet has significant potential to assist developers, and provides better coverage of tasks and better rankings for best-fit snippets than other code assistant systems.
260 citations