Search (5 results, page 1 of 1)

0.047309753 = product of:
  0.094619505 = sum of:
    0.01557172 = product of:
      0.06228688 = sum of:
        0.06228688 = weight(_text_:based in 2682) [ClassicSimilarity], result of:
          0.06228688 = score(doc=2682,freq=14.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.44037464 = fieldWeight in 2682, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2682)
      0.25 = coord(1/4)
    0.079047784 = product of:
      0.15809557 = sum of:
        0.15809557 = weight(_text_:assessment in 2682) [ClassicSimilarity], result of:
          0.15809557 = score(doc=2682,freq=8.0), product of:
            0.25917634 = queryWeight, product of:
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.04694356 = queryNorm
            0.60999227 = fieldWeight in 2682, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.52102 = idf(docFreq=480, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2682)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Semantic similarity assessment between concepts is an important task in many language related applications. In the past, several approaches to assess similarity by evaluating the knowledge modeled in an (or multiple) ontology (or ontologies) have been proposed. However, there are some limitations such as the facts of relying on predefined ontologies and fitting non-dynamic domains in the existing measures. Wikipedia provides a very large domain-independent encyclopedic repository and semantic network for computing semantic similarity of concepts with more coverage than usual ontologies. In this paper, we propose some novel feature based similarity assessment methods that are fully dependent on Wikipedia and can avoid most of the limitations and drawbacks introduced above. To implement similarity assessment based on feature by making use of Wikipedia, firstly a formal representation of Wikipedia concepts is presented. We then give a framework for feature based similarity based on the formal representation of Wikipedia concepts. Lastly, we investigate several feature based approaches to semantic similarity measures resulting from instantiations of the framework. The evaluation, based on several widely used benchmarks and a benchmark developed in ourselves, sustains the intuitions with respect to human judgements. Overall, several methods proposed in this paper have good human correlation and constitute some effective ways of determining similarity between Wikipedia concepts.

0.002548521 = product of:
  0.010194084 = sum of:
    0.010194084 = product of:
      0.040776335 = sum of:
        0.040776335 = weight(_text_:based in 2877) [ClassicSimilarity], result of:
          0.040776335 = score(doc=2877,freq=6.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.28829288 = fieldWeight in 2877, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2877)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

The Information Content (IC) of a concept is a fundamental dimension in computational linguistics. It enables a better understanding of concept's semantics. In the past, several approaches to compute IC of a concept have been proposed. However, there are some limitations such as the facts of relying on corpora availability, manual tagging, or predefined ontologies and fitting non-dynamic domains in the existing methods. Wikipedia provides a very large domain-independent encyclopedic repository and semantic network for computing IC of concepts with more coverage than usual ontologies. In this paper, we propose some novel methods to IC computation of a concept to solve the shortcomings of existing approaches. The presented methods focus on the IC computation of a concept (i.e., Wikipedia category) drawn from the Wikipedia category structure. We propose several new IC-based measures to compute the semantic similarity between concepts. The evaluation, based on several widely used benchmarks and a benchmark developed in ourselves, sustains the intuitions with respect to human judgments. Overall, some methods proposed in this paper have a good human correlation and constitute some effective ways of determining IC values for concepts and semantic similarity between concepts.

0.0024970302 = product of:
  0.009988121 = sum of:
    0.009988121 = product of:
      0.039952483 = sum of:
        0.039952483 = weight(_text_:based in 1037) [ClassicSimilarity], result of:
          0.039952483 = score(doc=1037,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.28246817 = fieldWeight in 1037, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=1037)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

This article presents a user study showing the effectiveness of a linked-based, virtual integration infrastructure that gives users access to relevant online resources, empowering them to design an information-seeking path that is specifically relevant to their context. IntegraL provides a lightweight approach to improve and augment search functionality by dynamically generating context-focused "anchors" for recognized elements of interest generated by library services. This article includes a description of how IntegraL's design supports users' information-seeking behavior. A full user study with both objective and subjective measures of IntegraL and hypothesis testing regarding IntegraL's effectiveness of the user's information-seeking experience are described along with data analysis, implications arising from this kind of virtual integration, and possible future directions.

0.0024970302 = product of:
  0.009988121 = sum of:
    0.009988121 = product of:
      0.039952483 = sum of:
        0.039952483 = weight(_text_:based in 5245) [ClassicSimilarity], result of:
          0.039952483 = score(doc=5245,freq=4.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.28246817 = fieldWeight in 5245, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.046875 = fieldNorm(doc=5245)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

A cross-level model based on the information processing perspective and trait activation theory was developed and tested in order to investigate the effects of individual-level epistemic motivation and team-level task reflexivity on three different individual contribution behaviors (i.e., adding, deleting, and revising) in the process of knowledge creation on team wikis. Using the Hierarchical Linear Modeling software package and the 2-wave data from 166 individuals in 51 wiki-based teams, we found cross-level interaction effects between individual epistemic motivation and team task reflexivity on different knowledge contribution behaviors on wikis. Epistemic motivation exerted a positive effect on adding, which was strengthened by team task reflexivity. The effect of epistemic motivation on deleting was positive only when task reflexivity was high. In addition, epistemic motivation was strongly positively related to revising, regardless of the level of task reflexivity involved.

0.0014713892 = product of:
  0.005885557 = sum of:
    0.005885557 = product of:
      0.023542227 = sum of:
        0.023542227 = weight(_text_:based in 1822) [ClassicSimilarity], result of:
          0.023542227 = score(doc=1822,freq=2.0), product of:
            0.14144066 = queryWeight, product of:
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.04694356 = queryNorm
            0.16644597 = fieldWeight in 1822, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0129938 = idf(docFreq=5906, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1822)
      0.25 = coord(1/4)
  0.25 = coord(1/4)

Abstract

User domain knowledge affects search behaviors and search success. Predicting a user's knowledge level from implicit evidence such as search behaviors could allow an adaptive information retrieval system to better personalize its interaction with users. This study examines whether user domain knowledge can be predicted from search behaviors by applying a regression modeling analysis method. We identify behavioral features that contribute most to a successful prediction model. A user experiment was conducted with 40 participants searching on task topics in the domain of genomics. Participant domain knowledge level was assessed based on the users' familiarity with and expertise in the search topics and their knowledge of MeSH (Medical Subject Headings) terms in the categories that corresponded to the search topics. Users' search behaviors were captured by logging software, which includes querying behaviors, document selection behaviors, and general task interaction behaviors. Multiple regression analysis was run on the behavioral data using different variable selection methods. Four successful predictive models were identified, each involving a slightly different set of behavioral variables. The models were compared for the best on model fit, significance of the model, and contributions of individual predictors in each model. Each model was validated using the split sampling method. The final model highlights three behavioral variables as domain knowledge level predictors: the number of documents saved, the average query length, and the average ranking position of the documents opened. The results are discussed, study limitations are addressed, and future research directions are suggested.