-
Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996)
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- Source
- Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al
-
Pirkola, A.; Hedlund, T.; Keskustalo, H.; Järvelin, K.: Dictionary-based cross-language information retrieval : problems, methods, and research findings (2001)
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-
Järvelin, K.; Persson, O.: ¬The DCI-index : discounted cumulated impact-based research evaluation (2008)
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- Abstract
- The article by K. Järvelin & O. Persson published in JASIST 59(9), The DCI-Index: Discounted Cumulated Impact-Based Research Evaluation, (pp. 1433-1440) contains an unfortunate error in one of its formulas, Equation 3. The present paper gives the correction and an example of impact analysis based on the corrected formula.
- Object
- h-index
-
Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017)
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- Abstract
- Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered.
-
Toivonen, J.; Pirkola, A.; Keskustalo, H.; Visala, K.; Järvelin, K.: Translating cross-lingual spelling variants using transformation rules (2005)
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-
Järvelin, K.; Persson, O.: ¬The DCI index : discounted cumulated impact-based research evaluation (2008)
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- Abstract
- Research evaluation is increasingly popular and important among research funding bodies and science policy makers. Various indicators have been proposed to evaluate the standing of individual scientists, institutions, journals, or countries. A simple and popular one among the indicators is the h-index, the Hirsch index (Hirsch 2005), which is an indicator for lifetime achievement of a scholar. Several other indicators have been proposed to complement or balance the h-index. However, these indicators have no conception of aging. The AR-index (Jin et al. 2007) incorporates aging but divides the received citation counts by the raw age of the publication. Consequently, the decay of a publication is very steep and insensitive to disciplinary differences. In addition, we believe that a publication becomes outdated only when it is no longer cited, not because of its age. Finally, all indicators treat citations as equally material when one might reasonably think that a citation from a heavily cited publication should weigh more than a citation froma non-cited or little-cited publication.We propose a new indicator, the Discounted Cumulated Impact (DCI) index, which devalues old citations in a smooth way. It rewards an author for receiving new citations even if the publication is old. Further, it allows weighting of the citations by the citation weight of the citing publication. DCI can be used to calculate research performance on the basis of the h-core of a scholar or any other publication data.
- Content
- Erratum in: Järvelin, K., O. Persson: The DCI-index: discounted cumulated impact-based research evaluation. Erratum re. In: Journal of the American Society for Information Science and Technology. 59(2008) no.14, S.2350-2352.
-
Näppilä, T.; Järvelin, K.; Niemi, T.: ¬A tool for data cube construction from structurally heterogeneous XML documents (2008)
0.02
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- Date
- 9. 2.2008 17:22:42
-
Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023)
0.02
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- Date
- 22. 6.2023 18:15:06
-
Lehtokangas, R.; Keskustalo, H.; Järvelin, K.: Experiments with transitive dictionary translation and pseudo-relevance feedback using graded relevance assessments (2008)
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-
Kristensen, J.; Järvelin, K.: ¬The effectiveness of a searching thesaurus in free-text searching in a full-text database (1990)
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-
Ferro, N.; Silvello, G.; Keskustalo, H.; Pirkola, A.; Järvelin, K.: ¬The twist measure for IR evaluation : taking user's effort into account (2016)
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-
Järvelin, K.; Vakkari, P.: ¬The evolution of library and information science 1965-1985 : a content analysis of journal titles (1993)
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-
Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004)
0.01
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-
Järvelin, K.: Evaluation (2011)
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-
Halttunen, K.; Järvelin, K.: Assessing learning outcomes in two information retrieval learning environments (2005)
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-
Ingwersen, P.; Järvelin, K.: ¬The turn : integration of information seeking and retrieval in context (2005)
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- Footnote
- Rez. in: Mitt. VÖB 59(2006) H.2, S.81-83 (O. Oberhauser): "Mit diesem Band haben zwei herausragende Vertreter der europäischen Informationswissenschaft, die Professoren Peter Ingwersen (Kopenhagen) und Kalervo Järvelin (Tampere) ein Werk vorgelegt, das man vielleicht dereinst als ihr opus magnum bezeichnen wird. Mich würde dies nicht überraschen, denn die Autoren unternehmen hier den ambitionierten Versuch, zwei informations wissenschaftliche Forschungstraditionen, die einander bisher in eher geringem Ausmass begegneten, unter einem gesamtheitlichen kognitiven Ansatz zu vereinen - das primär im sozialwissenschaftlichen Bereich verankerte Forschungsgebiet "Information Seeking and Retrieval" (IS&R) und das vorwiegend im Informatikbereich angesiedelte "Information Retrieval" (IR). Dabei geht es ihnen auch darum, den seit etlichen Jahren zwar dominierenden, aber auch als zu individualistisch kritisierten kognitiven Ansatz so zu erweitern, dass technologische, verhaltensbezogene und kooperative Aspekte in kohärenter Weise berücksichtigt werden. Dies geschieht auf folgende Weise in neun Kapiteln: - Zunächst werden die beiden "Lager" - die an Systemen und Laborexperimenten orientierte IR-Tradition und die an Benutzerfragen orientierte IS&R-Fraktion - einander gegenübergestellt und einige zentrale Begriffe geklärt. - Im zweiten Kapitel erfolgt eine ausführliche Darstellung der kognitiven Richtung der Informationswissenschaft, insbesondere hinsichtlich des Informationsbegriffes. - Daran schliesst sich ein Überblick über die bisherige Forschung zu "Information Seeking" (IS) - eine äusserst brauchbare Einführung in die Forschungsfragen und Modelle, die Forschungsmethodik sowie die in diesem Bereich offenen Fragen, z.B. die aufgrund der einseitigen Ausrichtung des Blickwinkels auf den Benutzer mangelnde Betrachtung der Benutzer-System-Interaktion. - In analoger Weise wird im vierten Kapitel die systemorientierte IRForschung in einem konzentrierten Überblick vorgestellt, in dem es sowohl um das "Labormodell" als auch Ansätze wie die Verarbeitung natürlicher Sprache und Expertensysteme geht. Aspekte wie Relevanz, Anfragemodifikation und Performanzmessung werden ebenso angesprochen wie die Methodik - von den ersten Laborexperimenten bis zu TREC und darüber hinaus.
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Kettunen, K.; Kunttu, T.; Järvelin, K.: To stem or lemmatize a highly inflectional language in a probabilistic IR environment? (2005)
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Pirkola, A.; Järvelin, K.: Employing the resolution power of search keys (2001)
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Järvelin, K.; Ingwersen, P.: User-oriented and cognitive models of information retrieval (2009)
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Niemi, T.; Hirvonen, L.; Järvelin, K.: Multidimensional data model and query language for informetrics (2003)
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