-
Fuhr, N.: Modelle im Information Retrieval (2023)
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- Abstract
- Information-Retrieval-Modelle -(IR-Modelle) spezifizieren, wie zu einer gegebenen Anfrage die Antwortdokumente aus einer Dokumentenkollektion bestimmt werden. Ausgangsbasis jedes Modells sind dabei zunächst bestimmte Annahmen über die Wissensrepräsentation (s. Teil B Methoden und Systeme der Inhaltserschließung) von Fragen und Dokumenten. Hier bezeichnen wir die Elemente dieser Repräsentationen als Terme, wobei es aus der Sicht des Modells egal ist, wie diese Terme aus dem Dokument (und analog aus der von Benutzenden eingegebenen Anfrage) abgeleitet werden: Bei Texten werden hierzu häufig computerlinguistische Methoden eingesetzt, aber auch komplexere automatische oder manuelle Erschließungsverfahren können zur Anwendung kommen. Repräsentationen besitzen ferner eine bestimmte Struktur. Ein Dokument wird meist als Menge oder Multimenge von Termen aufgefasst, wobei im zweiten Fall das Mehrfachvorkommen berücksichtigt wird. Diese Dokumentrepräsentation wird wiederum auf eine sogenannte Dokumentbeschreibung abgebildet, in der die einzelnen Terme gewichtet sein können. Im Folgenden unterscheiden wir nur zwischen ungewichteter (Gewicht eines Terms ist entweder 0 oder 1) und gewichteter Indexierung (das Gewicht ist eine nichtnegative reelle Zahl). Analog dazu gibt es eine Fragerepräsentation; legt man eine natürlichsprachige Anfrage zugrunde, so kann man die o. g. Verfahren für Dokumenttexte anwenden. Alternativ werden auch grafische oder formale Anfragesprachen verwendet, wobei aus Sicht der Modelle insbesondere deren logische Struktur (etwa beim Booleschen Retrieval) relevant ist. Die Fragerepräsentation wird dann in eine Fragebeschreibung überführt.
- Date
- 24.11.2022 17:20:29
-
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
- Source
- Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623
-
Carpineto, C.; Romano, G.: Order-theoretical ranking (2000)
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- Theme
- Formale Begriffsanalyse
-
Kulyukin, V.A.; Settle, A.: Ranked retrieval with semantic networks and vector spaces (2001)
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- Abstract
- The equivalence of semantic networks with spreading activation and vector spaces with dot product is investigated under ranked retrieval. Semantic networks are viewed as networks of concepts organized in terms of abstraction and packaging relations. It is shown that the two models can be effectively constructed from each other. A formal method is suggested to analyze the models in terms of their relative performance in the same universe of objects
-
Zhang, D.; Dong, Y.: ¬An effective algorithm to rank Web resources (2000)
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- Source
- Computer networks. 33(2000) nos.1/6, S.449-455
-
Finding anything in the billion page Web : are algorithms the key? (1999)
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- Source
- Computer networks. 31(1999) no.11-16, S.1760-1761
-
Soulier, L.; Jabeur, L.B.; Tamine, L.; Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model (2013)
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- Date
- 22. 3.2013 19:34:49
-
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.
-
Archuby, C.G.: Interfaces se recuperacion para catalogos en linea con salidas ordenadas por probable relevancia (2000)
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- Date
- 29. 1.1996 18:23:13
- Source
- Ciencia da informacao. 29(2000) no.3, S.5-13
-
Crestani, F.: Combination of similarity measures for effective spoken document retrieval (2003)
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- Source
- Journal of information science. 29(2003) no.2, S.87-96
-
Li, J.; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks (2009)
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- Abstract
- Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm.
-
Jiang, X.; Sun, X.; Yang, Z.; Zhuge, H.; Lapshinova-Koltunski, E.; Yao, J.: Exploiting heterogeneous scientific literature networks to combat ranking bias : evidence from the computational linguistics area (2016)
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- Abstract
- It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.
-
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
-
Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009)
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-
Ding, Y.: Topic-based PageRank on author cocitation networks (2011)
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-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Zhang, W.; Korf, R.E.: Performance of linear-space search algorithms (1995)
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- Date
- 2. 8.1996 10:29:15
-
Hüther, H.: Selix im DFG-Projekt Kascade (1998)
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- Date
- 25. 8.2000 19:55:29
-
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
