Search (9 results, page 1 of 1)

  • × author_ss:"Järvelin, K."
  1. Vakkari, P.; Järvelin, K.: Explanation in information seeking and retrieval (2005) 0.02
    0.016645778 = product of:
      0.049937334 = sum of:
        0.049937334 = weight(_text_:social in 643) [ClassicSimilarity], result of:
          0.049937334 = score(doc=643,freq=4.0), product of:
            0.20037155 = queryWeight, product of:
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.050248925 = queryNorm
            0.24922368 = fieldWeight in 643, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.03125 = fieldNorm(doc=643)
      0.33333334 = coord(1/3)
    
    Abstract
    Information Retrieval (IR) is a research area both within Computer Science and Information Science. It has by and large two communities: a Computer Science oriented experimental approach and a user-oriented Information Science approach with a Social Science background. The communities hold a critical stance towards each other (e.g., Ingwersen, 1996), the latter suspecting the realism of the former, and the former suspecting the usefulness of the latter. Within Information Science the study of information seeking (IS) also has a Social Science background. There is a lot of research in each of these particular areas of information seeking and retrieval (IS&R). However, the three communities do not really communicate with each other. Why is this, and could the relationships be otherwise? Do the communities in fact belong together? Or perhaps each community is better off forgetting about the existence of the other two? We feel that the relationships between the research areas have not been properly analyzed. One way to analyze the relationships is to examine what each research area is trying to find out: which phenomena are being explained and how. We believe that IS&R research would benefit from being analytic about its frameworks, models and theories, not just at the level of meta-theories, but also much more concretely at the level of study designs. Over the years there have been calls for more context in the study of IS&R. Work tasks as well as cultural activities/interests have been proposed as the proper context for information access. For example, Wersig (1973) conceptualized information needs from the tasks perspective. He argued that in order to learn about information needs and seeking, one needs to take into account the whole active professional role of the individuals being investigated. Byström and Järvelin (1995) analysed IS processes in the light of tasks of varying complexity. Ingwersen (1996) discussed the role of tasks and their descriptions and problematic situations from a cognitive perspective on IR. Most recently, Vakkari (2003) reviewed task-based IR and Järvelin and Ingwersen (2004) proposed the extension of IS&R research toward the task context. Therefore there is much support to the task context, but how should it be applied in IS&R?
  2. Pharo, N.; Järvelin, K.: ¬The SST method : a tool for analysing Web information search processes (2004) 0.01
    0.014712928 = product of:
      0.04413878 = sum of:
        0.04413878 = weight(_text_:social in 2533) [ClassicSimilarity], result of:
          0.04413878 = score(doc=2533,freq=2.0), product of:
            0.20037155 = queryWeight, product of:
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.050248925 = queryNorm
            0.22028469 = fieldWeight in 2533, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2533)
      0.33333334 = coord(1/3)
    
    Abstract
    The article presents the search situation transition (SST) method for analysing Web information search (WIS) processes. The idea of the method is to analyse searching behaviour, the process, in detail and connect both the searchers' actions (captured in a log) and his/her intentions and goals, which log analysis never captures. On the other hand, ex post factor surveys, while popular in WIS research, cannot capture the actual search processes. The method is presented through three facets: its domain, its procedure, and its justification. The method's domain is presented in the form of a conceptual framework which maps five central categories that influence WIS processes; the searcher, the social/organisational environment, the work task, the search task, and the process itself. The method's procedure includes various techniques for data collection and analysis. The article presents examples from real WIS processes and shows how the method can be used to identify the interplay of the categories during the processes. It is shown that the method presents a new approach in information seeking and retrieval by focusing on the search process as a phenomenon and by explicating how different information seeking factors directly affect the search process.
  3. Ingwersen, P.; Järvelin, K.: ¬The turn : integration of information seeking and retrieval in context (2005) 0.01
    0.010403612 = product of:
      0.031210834 = sum of:
        0.031210834 = weight(_text_:social in 1323) [ClassicSimilarity], result of:
          0.031210834 = score(doc=1323,freq=4.0), product of:
            0.20037155 = queryWeight, product of:
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.050248925 = queryNorm
            0.1557648 = fieldWeight in 1323, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.9875789 = idf(docFreq=2228, maxDocs=44218)
              0.01953125 = fieldNorm(doc=1323)
      0.33333334 = coord(1/3)
    
    Abstract
    The Turn analyzes the research of information seeking and retrieval (IS&R) and proposes a new direction of integrating research in these two areas: the fields should turn off their separate and narrow paths and construct a new avenue of research. An essential direction for this avenue is context as given in the subtitle Integration of Information Seeking and Retrieval in Context. Other essential themes in the book include: IS&R research models, frameworks and theories; search and works tasks and situations in context; interaction between humans and machines; information acquisition, relevance and information use; research design and methodology based on a structured set of explicit variables - all set into the holistic cognitive approach. The present monograph invites the reader into a construction project - there is much research to do for a contextual understanding of IS&R. The Turn represents a wide-ranging perspective of IS&R by providing a novel unique research framework, covering both individual and social aspects of information behavior, including the generation, searching, retrieval and use of information. Regarding traditional laboratory information retrieval research, the monograph proposes the extension of research toward actors, search and work tasks, IR interaction and utility of information. Regarding traditional information seeking research, it proposes the extension toward information access technology and work task contexts. The Turn is the first synthesis of research in the broad area of IS&R ranging from systems oriented laboratory IR research to social science oriented information seeking studies. TOC:Introduction.- The Cognitive Framework for Information.- The Development of Information Seeking Research.- Systems-Oriented Information Retrieval.- Cognitive and User-Oriented Information Retrieval.- The Integrated IS&R Research Framework.- Implications of the Cognitive Framework for IS&R.- Towards a Research Program.- Conclusion.- Definitions.- References.- Index.
  4. Järvelin, K.; Ingwersen, P.; Niemi, T.: ¬A user-oriented interface for generalised informetric analysis based on applying advanced data modelling techniques (2000) 0.01
    0.010350424 = product of:
      0.03105127 = sum of:
        0.03105127 = product of:
          0.06210254 = sum of:
            0.06210254 = weight(_text_:networks in 4545) [ClassicSimilarity], result of:
              0.06210254 = score(doc=4545,freq=2.0), product of:
                0.23767339 = queryWeight, product of:
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.050248925 = queryNorm
                0.26129362 = fieldWeight in 4545, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4545)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    This article presents a novel user-oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non-first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content-bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface's general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.
  5. Saarikoski, J.; Laurikkala, J.; Järvelin, K.; Juhola, M.: ¬A study of the use of self-organising maps in information retrieval (2009) 0.01
    0.010350424 = product of:
      0.03105127 = sum of:
        0.03105127 = product of:
          0.06210254 = sum of:
            0.06210254 = weight(_text_:networks in 2836) [ClassicSimilarity], result of:
              0.06210254 = score(doc=2836,freq=2.0), product of:
                0.23767339 = queryWeight, product of:
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.050248925 = queryNorm
                0.26129362 = fieldWeight in 2836, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.72992 = idf(docFreq=1060, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2836)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Abstract
    Purpose - The aim of this paper is to explore the possibility of retrieving information with Kohonen self-organising maps, which are known to be effective to group objects according to their similarity or dissimilarity. Design/methodology/approach - After conventional preprocessing, such as transforming into vector space, documents from a German document collection were trained for a neural network of Kohonen self-organising map type. Such an unsupervised network forms a document map from which relevant objects can be found according to queries. Findings - Self-organising maps ordered documents to groups from which it was possible to find relevant targets. Research limitations/implications - The number of documents used was moderate due to the limited number of documents associated to test topics. The training of self-organising maps entails rather long running times, which is their practical limitation. In future, the aim will be to build larger networks by compressing document matrices, and to develop document searching in them. Practical implications - With self-organising maps the distribution of documents can be visualised and relevant documents found in document collections of limited size. Originality/value - The paper reports on an approach that can be especially used to group documents and also for information search. So far self-organising maps have rarely been studied for information retrieval. Instead, they have been applied to document grouping tasks.
  6. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.01
    0.006808036 = product of:
      0.020424107 = sum of:
        0.020424107 = product of:
          0.040848214 = sum of:
            0.040848214 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
              0.040848214 = score(doc=2230,freq=2.0), product of:
                0.17596318 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050248925 = queryNorm
                0.23214069 = fieldWeight in 2230, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2230)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    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
  7. Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017) 0.01
    0.006808036 = product of:
      0.020424107 = sum of:
        0.020424107 = product of:
          0.040848214 = sum of:
            0.040848214 = weight(_text_:22 in 3589) [ClassicSimilarity], result of:
              0.040848214 = score(doc=3589,freq=2.0), product of:
                0.17596318 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050248925 = queryNorm
                0.23214069 = fieldWeight in 3589, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3589)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    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.
  8. Näppilä, T.; Järvelin, K.; Niemi, T.: ¬A tool for data cube construction from structurally heterogeneous XML documents (2008) 0.01
    0.0056733633 = product of:
      0.01702009 = sum of:
        0.01702009 = product of:
          0.03404018 = sum of:
            0.03404018 = weight(_text_:22 in 1369) [ClassicSimilarity], result of:
              0.03404018 = score(doc=1369,freq=2.0), product of:
                0.17596318 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050248925 = queryNorm
                0.19345059 = fieldWeight in 1369, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1369)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    9. 2.2008 17:22:42
  9. 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) 0.01
    0.0056733633 = product of:
      0.01702009 = sum of:
        0.01702009 = product of:
          0.03404018 = sum of:
            0.03404018 = weight(_text_:22 in 998) [ClassicSimilarity], result of:
              0.03404018 = score(doc=998,freq=2.0), product of:
                0.17596318 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050248925 = queryNorm
                0.19345059 = fieldWeight in 998, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=998)
          0.5 = coord(1/2)
      0.33333334 = coord(1/3)
    
    Date
    22. 6.2023 18:15:06