-
Efthimiadis, E.N.: User choices : a new yardstick for the evaluation of ranking algorithms for interactive query expansion (1995)
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- Date
- 22. 2.1996 13:14:10
- Source
- Information processing and management. 31(1995) no.4, S.605-620
-
Song, D.; Bruza, P.D.: Towards context sensitive information inference (2003)
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- Date
- 22. 3.2003 19:35:46
- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.4, S.321-334
-
Shiri, A.A.; Revie, C.: Query expansion behavior within a thesaurus-enhanced search environment : a user-centered evaluation (2006)
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- Date
- 22. 7.2006 16:32:43
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.4, S.462-478
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
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Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
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- Date
- 1. 8.1996 22:08:06
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Klas, C.-P.; Fuhr, N.; Schaefer, A.: Evaluating strategic support for information access in the DAFFODIL system (2004)
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- Date
- 16.11.2008 16:22:48
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Srinivasan, P.: Query expansion and MEDLINE (1996)
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- Source
- Information processing and management. 32(1996) no.4, S.431-443
-
Hancock-Beaulieu, M.; Walker, S.: ¬An evaluation of automatic query expansion in an online library catalogue (1992)
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- Journal of documentation. 48(1992) no.4, S.406-421
-
Bhogal, J.; Macfarlane, A.; Smith, P.: ¬A review of ontology based query expansion (2007)
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- Information processing and management. 43(2007) no.4, S.866-886
-
Kwok, K.L.: ¬A network approach to probabilistic information retrieval (1995)
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- Abstract
- Shows how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with 4 standard small collections and a large Wall Street Journal collection show that small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve
-
Nie, J.-Y.: Query expansion and query translation as logical inference (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.4, S.335-346
-
Bhansali, D.; Desai, H.; Deulkar, K.: ¬A study of different ranking approaches for semantic search (2015)
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- Abstract
- Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.
-
Efthimiadis, E.N.: Interactive query expansion : a user-based evaluation in a relevance feedback environment (2000)
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- Abstract
- A user-centered investigation of interactive query expansion within the context of a relevance feedback system is presented in this article. Data were collected from 25 searches using the INSPEC database. The data collection mechanisms included questionnaires, transaction logs, and relevance evaluations. The results discuss issues that relate to query expansion, retrieval effectiveness, the correspondence of the on-line-to-off-line relevance judgments, and the selection of terms for query expansion by users (interactive query expansion). The main conclusions drawn from the results of the study are that: (1) one-third of the terms presented to users in a list of candidate terms for query expansion was identified by the users as potentially useful for query expansion. (2) These terms were mainly judged as either variant expressions (synonyms) or alternative (related) terms to the initial query terms. However, a substantial portion of the selected terms were identified as representing new ideas. (3) The relationships identified between the five best terms selected by the users for query expansion and the initial query terms were that: (a) 34% of the query expansion terms have no relationship or other type of correspondence with a query term; (b) 66% of the remaining query expansion terms have a relationship to the query terms. These relationships were: narrower term (46%), broader term (3%), related term (17%). (4) The results provide evidence for the effectiveness of interactive query expansion. The initial search produced on average three highly relevant documents; the query expansion search produced on average nine further highly relevant documents. The conclusions highlight the need for more research on: interactive query expansion, the comparative evaluation of automatic vs. interactive query expansion, the study of weighted Webbased or Web-accessible retrieval systems in operational environments, and for user studies in searching ranked retrieval systems in general
