Search (39 results, page 1 of 2)

  • × author_ss:"Järvelin, K."
  1. Järvelin, K.; Kristensen, J.; Niemi, T.; Sormunen, E.; Keskustalo, H.: ¬A deductive data model for query expansion (1996) 0.02
    0.022779368 = product of:
      0.045558736 = sum of:
        0.045558736 = sum of:
          0.008118451 = weight(_text_:a in 2230) [ClassicSimilarity], result of:
            0.008118451 = score(doc=2230,freq=8.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.15287387 = fieldWeight in 2230, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=2230)
          0.037440285 = weight(_text_:22 in 2230) [ClassicSimilarity], result of:
            0.037440285 = score(doc=2230,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = 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)
    
    Abstract
    We present a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and occurrence levels. Concepts and relationships among them are represented at the conceptual level. The expression level represents natural language expressions for concepts. Each expression has one or more matching models at the occurrence level. Each model specifies the matching of the expression in database indices built in varying ways. The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for environments providing heterogeneous IR systems. Expansion is controlled by adjustable matching reliability.
    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
    Type
    a
  2. Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017) 0.02
    0.021590449 = product of:
      0.043180898 = sum of:
        0.043180898 = sum of:
          0.005740611 = weight(_text_:a in 3589) [ClassicSimilarity], result of:
            0.005740611 = score(doc=3589,freq=4.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.10809815 = fieldWeight in 3589, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046875 = fieldNorm(doc=3589)
          0.037440285 = weight(_text_:22 in 3589) [ClassicSimilarity], result of:
            0.037440285 = score(doc=3589,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = 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)
    
    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.
    Type
    a
  3. Näppilä, T.; Järvelin, K.; Niemi, T.: ¬A tool for data cube construction from structurally heterogeneous XML documents (2008) 0.02
    0.020074995 = product of:
      0.04014999 = sum of:
        0.04014999 = sum of:
          0.00894975 = weight(_text_:a in 1369) [ClassicSimilarity], result of:
            0.00894975 = score(doc=1369,freq=14.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.1685276 = fieldWeight in 1369, product of:
                3.7416575 = tf(freq=14.0), with freq of:
                  14.0 = termFreq=14.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0390625 = fieldNorm(doc=1369)
          0.03120024 = weight(_text_:22 in 1369) [ClassicSimilarity], result of:
            0.03120024 = score(doc=1369,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = 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)
    
    Abstract
    Data cubes for OLAP (On-Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path-oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real-world data integration tasks. Several Lowest Common Ancestor (LCA)-based XML query evaluation strategies have recently been introduced to provide a more structure-independent way to access XML documents. We shall, however, show that this approach leads in the context of certain - not uncommon - types of XML documents to undesirable results. This article introduces a novel high-level data extraction primitive that utilizes the purpose-built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.
    Date
    9. 2.2008 17:22:42
    Type
    a
  4. 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
    0.01974305 = product of:
      0.0394861 = sum of:
        0.0394861 = sum of:
          0.008285859 = weight(_text_:a in 998) [ClassicSimilarity], result of:
            0.008285859 = score(doc=998,freq=12.0), product of:
              0.053105544 = queryWeight, product of:
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.046056706 = queryNorm
              0.15602624 = fieldWeight in 998, product of:
                3.4641016 = tf(freq=12.0), with freq of:
                  12.0 = termFreq=12.0
                1.153047 = idf(docFreq=37942, maxDocs=44218)
                0.0390625 = fieldNorm(doc=998)
          0.03120024 = weight(_text_:22 in 998) [ClassicSimilarity], result of:
            0.03120024 = score(doc=998,freq=2.0), product of:
              0.16128273 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046056706 = 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)
    
    Abstract
    The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross-sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706-1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross-sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706-1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production.
    Date
    22. 6.2023 18:15:06
    Type
    a
  5. Kristensen, J.; Järvelin, K.: ¬The effectiveness of a searching thesaurus in free-text searching in a full-text database (1990) 0.00
    0.0046871896 = product of:
      0.009374379 = sum of:
        0.009374379 = product of:
          0.018748758 = sum of:
            0.018748758 = weight(_text_:a in 2043) [ClassicSimilarity], result of:
              0.018748758 = score(doc=2043,freq=6.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.3530471 = fieldWeight in 2043, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.125 = fieldNorm(doc=2043)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  6. Kettunen, K.; Kunttu, T.; Järvelin, K.: To stem or lemmatize a highly inflectional language in a probabilistic IR environment? (2005) 0.00
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = product of:
          0.01353075 = sum of:
            0.01353075 = weight(_text_:a in 4395) [ClassicSimilarity], result of:
              0.01353075 = score(doc=4395,freq=32.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.25478977 = fieldWeight in 4395, product of:
                  5.656854 = tf(freq=32.0), with freq of:
                    32.0 = termFreq=32.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4395)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Purpose - To show that stem generation compares well with lemmatization as a morphological tool for a highly inflectional language for IR purposes in a best-match retrieval system. Design/methodology/approach - Effects of three different morphological methods - lemmatization, stemming and stem production - for Finnish are compared in a probabilistic IR environment (INQUERY). Evaluation is done using a four-point relevance scale which is partitioned differently in different test settings. Findings - Results show that stem production, a lighter method than morphological lemmatization, compares well with lemmatization in a best-match IR environment. Differences in performance between stem production and lemmatization are small and they are not statistically significant in most of the tested settings. It is also shown that hitherto a rather neglected method of morphological processing for Finnish, stemming, performs reasonably well although the stemmer used - a Porter stemmer implementation - is far from optimal for a morphologically complex language like Finnish. In another series of tests, the effects of compound splitting and derivational expansion of queries are tested. Practical implications - Usefulness of morphological lemmatization and stem generation for IR purposes can be estimated with many factors. On the average P-R level they seem to behave very close to each other in a probabilistic IR system. Thus, the choice of the used method with highly inflectional languages needs to be estimated along other dimensions too. Originality/value - Results are achieved using Finnish as an example of a highly inflectional language. The results are of interest for anyone who is interested in processing of morphological variation of a highly inflected language for IR purposes.
    Type
    a
  7. Järvelin, K.; Vakkari, P.: ¬The evolution of library and information science 1965-1985 : a content analysis of journal titles (1993) 0.00
    0.00334869 = product of:
      0.00669738 = sum of:
        0.00669738 = product of:
          0.01339476 = sum of:
            0.01339476 = weight(_text_:a in 4649) [ClassicSimilarity], result of:
              0.01339476 = score(doc=4649,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.25222903 = fieldWeight in 4649, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.109375 = fieldNorm(doc=4649)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  8. Pirkola, A.; Hedlund, T.; Keskustalo, H.; Järvelin, K.: Dictionary-based cross-language information retrieval : problems, methods, and research findings (2001) 0.00
    0.00334869 = product of:
      0.00669738 = sum of:
        0.00669738 = product of:
          0.01339476 = sum of:
            0.01339476 = weight(_text_:a in 3908) [ClassicSimilarity], result of:
              0.01339476 = score(doc=3908,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.25222903 = fieldWeight in 3908, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.109375 = fieldNorm(doc=3908)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  9. Pirkola, A.; Järvelin, K.: Employing the resolution power of search keys (2001) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 5907) [ClassicSimilarity], result of:
              0.011600202 = score(doc=5907,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 5907, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=5907)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Search key resolution power is analyzed in the context of a request, i.e., among the set of search keys for the request. Methods of characterizing the resolution power of keys automatically are studied, and the effects search keys of varying resolution power have on retrieval effectiveness are analyzed. It is shown that it often is possible to identify the best key of a query while the discrimination between the remaining keys presents problems. It is also shown that query performance is improved by suitably using the best key in a structured query. The tests were run with InQuery in a subcollection of the TREC collection, which contained some 515,000 documents
    Type
    a
  10. Järvelin, K.; Persson, O.: ¬The DCI index : discounted cumulated impact-based research evaluation (2008) 0.00
    0.0028047764 = product of:
      0.005609553 = sum of:
        0.005609553 = product of:
          0.011219106 = sum of:
            0.011219106 = weight(_text_:a in 2694) [ClassicSimilarity], result of:
              0.011219106 = score(doc=2694,freq=22.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21126054 = fieldWeight in 2694, product of:
                  4.690416 = tf(freq=22.0), with freq of:
                    22.0 = termFreq=22.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2694)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a
  11. Niemi, T.; Hirvonen, L.; Järvelin, K.: Multidimensional data model and query language for informetrics (2003) 0.00
    0.0026849252 = product of:
      0.0053698504 = sum of:
        0.0053698504 = product of:
          0.010739701 = sum of:
            0.010739701 = weight(_text_:a in 1753) [ClassicSimilarity], result of:
              0.010739701 = score(doc=1753,freq=14.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20223314 = fieldWeight in 1753, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1753)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Multidimensional data analysis or On-line analytical processing (OLAP) offers a single subject-oriented source for analyzing summary data based an various dimensions. We demonstrate that the OLAP approach gives a promising starting point for advanced analysis and comparison among summary data in informetrics applications. At the moment there is no single precise, commonly accepted logical/conceptual model for multidimensional analysis. This is because the requirements of applications vary considerably. We develop a conceptual/logical multidimensional model for supporting the complex and unpredictable needs of informetrics. Summary data are considered with respect of some dimensions. By changing dimensions the user may construct other views an the same summary data. We develop a multidimensional query language whose basic idea is to support the definition of views in a way, which is natural and intuitive for lay users in the informetrics area. We show that this view-oriented query language has a great expressive power and its degree of declarativity is greater than in contemporary operation-oriented or SQL (Structured Query Language)-like OLAP query languages.
    Type
    a
  12. Ferro, N.; Silvello, G.; Keskustalo, H.; Pirkola, A.; Järvelin, K.: ¬The twist measure for IR evaluation : taking user's effort into account (2016) 0.00
    0.0026742492 = product of:
      0.0053484985 = sum of:
        0.0053484985 = product of:
          0.010696997 = sum of:
            0.010696997 = weight(_text_:a in 2771) [ClassicSimilarity], result of:
              0.010696997 = score(doc=2771,freq=20.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20142901 = fieldWeight in 2771, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2771)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    We present a novel measure for ranking evaluation, called Twist (t). It is a measure for informational intents, which handles both binary and graded relevance. t stems from the observation that searching is currently a that searching is currently taken for granted and it is natural for users to assume that search engines are available and work well. As a consequence, users may assume the utility they have in finding relevant documents, which is the focus of traditional measures, as granted. On the contrary, they may feel uneasy when the system returns nonrelevant documents because they are then forced to do additional work to get the desired information, and this causes avoidable effort. The latter is the focus of t, which evaluates the effectiveness of a system from the point of view of the effort required to the users to retrieve the desired information. We provide a formal definition of t, a demonstration of its properties, and introduce the notion of effort/gain plots, which complement traditional utility-based measures. By means of an extensive experimental evaluation, t is shown to grasp different aspects of system performances, to not require extensive and costly assessments, and to be a robust tool for detecting differences between systems.
    Type
    a
  13. Sormunen, E.; Kekäläinen, J.; Koivisto, J.; Järvelin, K.: Document text characteristics affect the ranking of the most relevant documents by expanded structured queries (2001) 0.00
    0.0025370158 = product of:
      0.0050740317 = sum of:
        0.0050740317 = product of:
          0.010148063 = sum of:
            0.010148063 = weight(_text_:a in 4487) [ClassicSimilarity], result of:
              0.010148063 = score(doc=4487,freq=18.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19109234 = fieldWeight in 4487, product of:
                  4.2426405 = tf(freq=18.0), with freq of:
                    18.0 = termFreq=18.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4487)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The increasing flood of documentary information through the Internet and other information sources challenges the developers of information retrieval systems. It is not enough that an IR system is able to make a distinction between relevant and non-relevant documents. The reduction of information overload requires that IR systems provide the capability of screening the most valuable documents out of the mass of potentially or marginally relevant documents. This paper introduces a new concept-based method to analyse the text characteristics of documents at varying relevance levels. The results of the document analysis were applied in an experiment on query expansion (QE) in a probabilistic IR system. Statistical differences in textual characteristics of highly relevant and less relevant documents were investigated by applying a facet analysis technique. In highly relevant documents a larger number of aspects of the request were discussed, searchable expressions for the aspects were distributed over a larger set of text paragraphs, and a larger set of unique expressions were used per aspect than in marginally relevant documents. A query expansion experiment verified that the findings of the text analysis can be exploited in formulating more effective queries for best match retrieval in the search for highly relevant documents. The results revealed that expanded queries with concept-based structures performed better than unexpanded queries or Ñnatural languageÒ queries. Further, it was shown that highly relevant documents benefit essentially more from the concept-based QE in ranking than marginally relevant documents.
    Type
    a
  14. Talvensaari, T.; Laurikkala, J.; Järvelin, K.; Juhola, M.: ¬A study on automatic creation of a comparable document collection in cross-language information retrieval (2006) 0.00
    0.0025370158 = product of:
      0.0050740317 = sum of:
        0.0050740317 = product of:
          0.010148063 = sum of:
            0.010148063 = weight(_text_:a in 5601) [ClassicSimilarity], result of:
              0.010148063 = score(doc=5601,freq=18.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.19109234 = fieldWeight in 5601, product of:
                  4.2426405 = tf(freq=18.0), with freq of:
                    18.0 = termFreq=18.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5601)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Purpose - To present a method for creating a comparable document collection from two document collections in different languages. Design/methodology/approach - The best query keys were extracted from a Finnish source collection (articles of the newspaper Aamulehti) with the relative average term frequency formula. The keys were translated into English with a dictionary-based query translation program. The resulting lists of words were used as queries that were run against the target collection (Los Angeles Times articles) with the nearest neighbor method. The documents were aligned with unrestricted and date-restricted alignment schemes, which were also combined. Findings - The combined alignment scheme was found the best, when the relatedness of the document pairs was assessed with a five-degree relevance scale. Of the 400 document pairs, roughly 40 percent were highly or fairly related and 75 percent included at least lexical similarity. Research limitations/implications - The number of alignment pairs was small due to the short common time period of the two collections, and their geographical (and thus, topical) remoteness. In future, our aim is to build larger comparable corpora in various languages and use them as source of translation knowledge for the purposes of cross-language information retrieval (CLIR). Practical implications - Readily available parallel corpora are scarce. With this method, two unrelated document collections can relatively easily be aligned to create a CLIR resource. Originality/value - The method can be applied to weakly linked collections and morphologically complex languages, such as Finnish.
    Type
    a
  15. Toivonen, J.; Pirkola, A.; Keskustalo, H.; Visala, K.; Järvelin, K.: Translating cross-lingual spelling variants using transformation rules (2005) 0.00
    0.0024857575 = product of:
      0.004971515 = sum of:
        0.004971515 = product of:
          0.00994303 = sum of:
            0.00994303 = weight(_text_:a in 1052) [ClassicSimilarity], result of:
              0.00994303 = score(doc=1052,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18723148 = fieldWeight in 1052, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1052)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Technical terms and proper names constitute a major problem in dictionary-based cross-language information retrieval (CLIR). However, technical terms and proper names in different languages often share the same Latin or Greek origin, being thus spelling variants of each other. In this paper we present a novel two-step fuzzy translation technique for cross-lingual spelling variants. In the first step, transformation rules are applied to source words to render them more similar to their target language equivalents. The rules are generated automatically using translation dictionaries as source data. In the second step, the intermediate forms obtained in the first step are translated into a target language using fuzzy matching. The effectiveness of the technique was evaluated empirically using five source languages and English as a target language. The two-step technique performed better, in some cases considerably better, than fuzzy matching alone. Even using the first step as such showed promising results.
    Type
    a
  16. Pirkola, A.; Puolamäki, D.; Järvelin, K.: Applying query structuring in cross-language retrieval (2003) 0.00
    0.0024857575 = product of:
      0.004971515 = sum of:
        0.004971515 = product of:
          0.00994303 = sum of:
            0.00994303 = weight(_text_:a in 1074) [ClassicSimilarity], result of:
              0.00994303 = score(doc=1074,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18723148 = fieldWeight in 1074, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1074)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    We will explore various ways to apply query structuring in cross-language information retrieval. In the first test, English queries were translated into Finnish using an electronic dictionary, and were run in a Finnish newspaper database of 55,000 articles. Queries were structured by combining the Finnish translation equivalents of the same English query key using the syn-operator of the InQuery retrieval system. Structured queries performed markedly better than unstructured queries. Second, the effects of compound-based structuring using a proximity operator for the translation equivalents of query language compound components were tested. The method was not useful in syn-based queries but resulted in decrease in retrieval effectiveness. Proper names are often non-identical spelling variants in different languages. This allows n-gram based translation of names not included in a dictionary. In the third test, a query structuring method where the Boolean and-operator was used to assign more weight to keys translated through n-gram matching gave good results.
    Type
    a
  17. Niemi, T.; Junkkari, M.; Järvelin, K.; Viita, S.: Advanced query language for manipulating complex entities (2004) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 4218) [ClassicSimilarity], result of:
              0.009471525 = score(doc=4218,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 4218, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.109375 = fieldNorm(doc=4218)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  18. Järvelin, K.: Evaluation (2011) 0.00
    0.0023678814 = product of:
      0.0047357627 = sum of:
        0.0047357627 = product of:
          0.009471525 = sum of:
            0.009471525 = weight(_text_:a in 548) [ClassicSimilarity], result of:
              0.009471525 = score(doc=548,freq=2.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.17835285 = fieldWeight in 548, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.109375 = fieldNorm(doc=548)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  19. Vakkari, P.; Järvelin, K.: Explanation in information seeking and retrieval (2005) 0.00
    0.0021393995 = product of:
      0.004278799 = sum of:
        0.004278799 = product of:
          0.008557598 = sum of:
            0.008557598 = weight(_text_:a in 643) [ClassicSimilarity], result of:
              0.008557598 = score(doc=643,freq=20.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.16114321 = fieldWeight in 643, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.03125 = fieldNorm(doc=643)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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?
    Source
    New directions in cognitive information retrieval. Eds.: A. Spink, C. Cole
    Type
    a
  20. Pharo, N.; Järvelin, K.: ¬The SST method : a tool for analysing Web information search processes (2004) 0.00
    0.0020714647 = product of:
      0.0041429293 = sum of:
        0.0041429293 = product of:
          0.008285859 = sum of:
            0.008285859 = weight(_text_:a in 2533) [ClassicSimilarity], result of:
              0.008285859 = score(doc=2533,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.15602624 = fieldWeight in 2533, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2533)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a