-
Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001)
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- Date
- 29. 3.2002 17:24:03
-
Cole, C.: Intelligent information retrieval: diagnosing information need : Part II: uncertainty expansion in a prototype of a diagnostic IR tool (1998)
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- Date
- 11. 8.2001 14:48:29
-
Wechsler, M.; Schäuble, P.: ¬The probability ranking principle revisited (2000)
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Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011)
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- Abstract
- Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
- Object
- P-Rank
-
Zhang, W.; Korf, R.E.: Performance of linear-space search algorithms (1995)
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- Date
- 2. 8.1996 10:29:15
-
Perry, R.; Willett, P.: ¬A revies of the use of inverted files for best match searching in information retrieval systems (1983)
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Jascó, P.: Mapping algorithms to translate natural language questions into search queries for Web databases (1997)
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-
Robertson, S.E.: ¬The probability ranking principle in IR (1977)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willet. San Francisco: Morgan Kaufmann 1997. S.281-286.
-
Salton, G.; Buckley, C.: Term-weighting approaches in automatic text retrieval (1988)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.323-328.
-
Sparck Jones, K.: Search term relevance weighting given little relevance information (1979)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.329-338.
-
Uratani, N.; Takeda, M.: ¬A fast string-searching algorithm for multiple patterns (1993)
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- Source
- Information processing and management. 29(1993) no.6, S.775-791
-
Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008)
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- Date
- 1. 8.2008 12:29:05
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MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004)
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- Date
- 20. 1.2007 18:30:22
-
Faloutsos, C.: Signature files (1992)
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- Date
- 7. 5.1999 15:22:48
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Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003)
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- Date
- 22. 3.2003 19:27:23
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Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
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Bilal, D.: Ranking, relevance judgment, and precision of information retrieval on children's queries : evaluation of Google, Yahoo!, Bing, Yahoo! Kids, and ask Kids (2012)
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- Abstract
- This study employed benchmarking and intellectual relevance judgment in evaluating Google, Yahoo!, Bing, Yahoo! Kids, and Ask Kids on 30 queries that children formulated to find information for specific tasks. Retrieved hits on given queries were benchmarked to Google's and Yahoo! Kids' top-five ranked hits retrieved. Relevancy of hits was judged on a graded scale; precision was calculated using the precision-at-ten metric (P@10). Yahoo! and Bing produced a similar percentage in hit overlap with Google (nearly 30%), but differed in the ranking of hits. Ask Kids retrieved 11% in hit overlap with Google versus 3% by Yahoo! Kids. The engines retrieved 26 hits across query clusters that overlapped with Yahoo! Kids' top-five ranked hits. Precision (P) that the engines produced across the queries was P = 0.48 for relevant hits, and P = 0.28 for partially relevant hits. Precision by Ask Kids was P = 0.44 for relevant hits versus P = 0.21 by Yahoo! Kids. Bing produced the highest total precision (TP) of relevant hits (TP = 0.86) across the queries, and Yahoo! Kids yielded the lowest (TP = 0.47). Average precision (AP) of relevant hits was AP = 0.56 by leading engines versus AP = 0.29 by small engines. In contrast, average precision of partially relevant hits was AP = 0.83 by small engines versus AP = 0.33 by leading engines. Average precision of relevant hits across the engines was highest on two-word queries and lowest on one-word queries. Google performed best on natural language queries; Bing did the same (P = 0.69) on two-word queries. The findings have implications for search engine ranking algorithms, relevance theory, search engine design, research design, and information literacy.
-
Paris, L.A.H.; Tibbo, H.R.: Freestyle vs. Boolean : a comparison of partial and exact match retrieval systems (1998)
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- Date
- 12. 3.1999 10:29:27
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Otterbacher, J.; Erkan, G.; Radev, D.R.: Biased LexRank : passage retrieval using random walks with question-based priors (2009)
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- Date
- 22.11.2008 17:11:29
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Willett, P.: Best-match text retrieval (1993)
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