Search (8 results, page 1 of 1)

0.010621051 = product of:
  0.042484205 = sum of:
    0.042484205 = product of:
      0.08496841 = sum of:
        0.08496841 = weight(_text_:organization in 1104) [ClassicSimilarity], result of:
          0.08496841 = score(doc=1104,freq=8.0), product of:
            0.17974974 = queryWeight, product of:
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.050415643 = queryNorm
            0.47270393 = fieldWeight in 1104, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.5653565 = idf(docFreq=3399, maxDocs=44218)
              0.046875 = fieldNorm(doc=1104)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

In this study, we propose a way to link the scholarly contents of scientific papers by constructing a knowledge graph based on the semantic organization of argumentation units and relations in scientific papers. We carried out an argumentation graph data model aimed at linking multiple discourses, and also developed a semantic annotation platform for scientific papers and an argumentation graph visualization system. A construction experiment was performed using 12 articles. The final argumentation graph has 1,262 nodes and 1,628 edges, including 1,628 intra-article relations and 190 inter-article relations. Knowledge evolution representation, strategic reading, and automatic abstracting use cases are presented to demonstrate the application of the argumentation graph. In contrast to existing knowledge graphs used in academic fields, the argumentation graph better supports the organization and representation of scientific paper content and can be used as data infrastructure in scientific knowledge retrieval, reorganization, reasoning, and evolution. Moreover, it supports automatic abstract and strategic reading.

Content

Vgl.: https://www.nomos-elibrary.de/10.5771/0943-7444-2022-4/ko-knowledge-organization-jahrgang-49-2022-heft-4.

Source

Knowledge organization. 49(2022) no.4, S.213 - 235

0.008539819 = product of:
  0.034159277 = sum of:
    0.034159277 = weight(_text_:information in 2261) [ClassicSimilarity], result of:
      0.034159277 = score(doc=2261,freq=22.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.38596505 = fieldWeight in 2261, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2261)
  0.25 = coord(1/4)

Abstract

The emergence of information portal systems in the past few years has led to a greatly enhanced Web-based environment for users seeking information online. While considerable research has been conducted an user information-seeking behavior in regular IR environments over the past decade, this paper focuses specifically an how users in a medical science and clinical setting carry out their daily information seeking through a customizable information portal system (MyWelch). We describe our initial study an analyzing Web usage data from MyWelch to see whether the results conform to the features and patterns established in current information-seeking models, present several observations regarding user information-seeking behavior in a portal environment, outline possible long-term user information-seeking patterns based an usage data, and discuss the direction of future research an user information-seeking behavior in the MyWelch portal environment.

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.8, S.670-684

0.006785332 = product of:
  0.027141329 = sum of:
    0.027141329 = weight(_text_:information in 4184) [ClassicSimilarity], result of:
      0.027141329 = score(doc=4184,freq=20.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.30666938 = fieldWeight in 4184, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4184)
  0.25 = coord(1/4)

Abstract

Prior research has discovered that groups tend to discuss shared information while failing to discuss unique information in decision-making processes. In our study, we conducted a lab experiment to examine the effect of prediscussion note-taking on this phenomenon. The experiment used a murder-mystery hidden profile task. In all, 192 undergraduate students were recruited and randomly assigned into 48 four-person groups with gender being the matching variable (i.e., each group consisted of four same-gender participants). During the decision-making processes, some groups were asked to take notes while reading task materials and had their notes available in the following group discussion, while the other groups were not given this opportunity. Our analysis results suggest that (a) the presence of an information piece in group members' notes positively correlates with its appearance in the subsequent discussion and note-taking positively affects the group's information repetition rate; (b) group decision quality positively correlates with the group's information sampling rate and negatively correlates with the group's information sampling/repetition bias; and (c) gender has no statistically significant moderating effect on the relationship between note-taking and information sharing. These results imply that prediscussion note-taking could facilitate information sharing but could not alleviate the biased information pooling in hidden profile tasks.

Source

Journal of the Association for Information Science and Technology. 69(2018) no.4, S.566-577

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 565) [ClassicSimilarity], result of:
      0.014565565 = score(doc=565,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 565, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=565)
  0.25 = coord(1/4)

Abstract

The research provides (1) an account of the construction of a new Scientific Argument Ontology (SAO), (2) a statistical analysis of 40 articles from both fields of Library and Information Science and Biomedical Research, and (3) a summary of important differences between the article structures common to each respective field of study and characteristics of their contents as revealed by applying SAO to conduct qualitative analysis. Ontology coverage ratios as well as the ratios of different classes and evidence types were calculated in the analysis. The results show a comprehensive coverage of SAO, while also indicate that the ontological construction of scientific arguments should fully consider the characteristics of their disciplines and fields in order to better facilitate extraction, discovery and retrieval.

Source

Proceedings of the Association for Information Science and Technology 56(2019) no.1, S.855-857

0.0036413912 = product of:
  0.014565565 = sum of:
    0.014565565 = weight(_text_:information in 1072) [ClassicSimilarity], result of:
      0.014565565 = score(doc=1072,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.16457605 = fieldWeight in 1072, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1072)
  0.25 = coord(1/4)

Abstract

This study contributes to the recent discussions on indicating interdisciplinarity, that is, going beyond catch-all metrics of interdisciplinarity. We propose a contextual framework to improve the granularity and usability of the existing methodology for interdisciplinary knowledge flow (IKF) in which scientific disciplines import and export knowledge from/to other disciplines. To characterize the knowledge exchange between disciplines, we recognize three aspects of IKF under this framework, namely broadness, intensity, and homogeneity. We show how to utilize them to uncover different forms of interdisciplinarity, especially between disciplines with the largest volume of IKF. We apply this framework in two use cases, one at the level of disciplines and one at the level of journals, to show how it can offer a more holistic and detailed viewpoint on the interdisciplinarity of scientific entities than aggregated and context-unaware indicators. We further compare our proposed framework, an indicating process, with established indicators and discuss how such information tools on interdisciplinarity can assist science policy practices such as performance-based research funding systems and panel-based peer review processes.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.11, S.1325-1340

0.0030344925 = product of:
  0.01213797 = sum of:
    0.01213797 = weight(_text_:information in 567) [ClassicSimilarity], result of:
      0.01213797 = score(doc=567,freq=4.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.13714671 = fieldWeight in 567, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=567)
  0.25 = coord(1/4)

Abstract

Scientific papers are essential manifestations of evolving scientific knowledge, and arguments are an important avenue to communicate research results. This study aims to understand how the argumentation process is represented in scientific papers, which is important for knowledge representation, discovery, and retrieval. First, based on fundamental argument theory and scientific discourse ontologies, a coding schema, including 17 categories was constructed. Thereafter, annotation experiments were conducted with 40 scientific articles randomly selected from two different research areas (library and information science and biomedical sciences). Statistical analysis and the sequential pattern mining method were then employed; the ratio of different argumentation units and evidence types were calculated, the argumentation semantics of figures and tables analyzed, and the argumentation structures extracted. A correlation analysis between argumentation and rhetorical structures was also performed to further reveal how argumentation was represented within scientific discourses. The results indicated a difference in the proportion of the argumentation units in the two types of scientific papers, as well as a similar linear construction with differences in the specific argument structures of each knowledge domain and a clear correlation between argumentation and rhetorical structure.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.6, S.863-878

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 646) [ClassicSimilarity], result of:
      0.008582841 = score(doc=646,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 646, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=646)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 73(2022) no.9, S.1201-1221

0.0021457102 = product of:
  0.008582841 = sum of:
    0.008582841 = weight(_text_:information in 1192) [ClassicSimilarity], result of:
      0.008582841 = score(doc=1192,freq=2.0), product of:
        0.08850355 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.050415643 = queryNorm
        0.09697737 = fieldWeight in 1192, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1192)
  0.25 = coord(1/4)

Source

Journal of the Association for Information Science and Technology. 74(2023) no.13, S.1515-1531