Search (148 results, page 1 of 8)

0.1439759 = product of:
  0.2879518 = sum of:
    0.07198795 = product of:
      0.21596384 = sum of:
        0.21596384 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.21596384 = score(doc=862,freq=2.0), product of:
            0.38426498 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.045324896 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
      0.33333334 = coord(1/3)
    0.21596384 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
      0.21596384 = score(doc=862,freq=2.0), product of:
        0.38426498 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.045324896 = queryNorm
        0.56201804 = fieldWeight in 862, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=862)
  0.5 = coord(2/4)

Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

0.043901104 = product of:
  0.08780221 = sum of:
    0.07244997 = weight(_text_:retrieval in 5843) [ClassicSimilarity], result of:
      0.07244997 = score(doc=5843,freq=20.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.5284309 = fieldWeight in 5843, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5843)
    0.0153522445 = product of:
      0.030704489 = sum of:
        0.030704489 = weight(_text_:22 in 5843) [ClassicSimilarity], result of:
          0.030704489 = score(doc=5843,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.19345059 = fieldWeight in 5843, 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=5843)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Purpose Personal information management (PIM) is an activity in which people store information items in order to retrieve them later. The purpose of this paper is to test and quantify the effect of factors related to collection size, file properties and workload on file retrieval success and efficiency. Design/methodology/approach In the study, 289 participants retrieved 1,557 of their shared files in a naturalistic setting. The study used specially developed software designed to collect shared files' names and present them as targets for the retrieval task. The dependent variables were retrieval success, retrieval time and misstep/s. Findings Various factors compromise shared files retrieval including: collection size (large number of files), file properties (multiple versions, size of team sharing the file, time since most recent retrieval and folder depth) and workload (daily e-mails sent and received). The authors discuss theoretical reasons for these negative effects and suggest possible ways to overcome them. Originality/value Retrieval is the main reason people manage personal information. It is essential for retrieval to be successful and efficient, as information cannot be used unless it can be re-accessed. Prior PIM research has assumed that factors related to collection size, file properties and workload affect file retrieval. However, this is the first study to systematically quantify the negative effects of these factors. As each of these factors is expected to be exacerbated in the future, this study is a necessary first step toward addressing these problems.

Date

20. 1.2015 18:30:22

0.028651714 = product of:
  0.05730343 = sum of:
    0.038880736 = weight(_text_:retrieval in 941) [ClassicSimilarity], result of:
      0.038880736 = score(doc=941,freq=4.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.2835858 = fieldWeight in 941, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=941)
    0.018422693 = product of:
      0.036845386 = sum of:
        0.036845386 = weight(_text_:22 in 941) [ClassicSimilarity], result of:
          0.036845386 = score(doc=941,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.23214069 = fieldWeight in 941, 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=941)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Inferring the gender of named entities present in a text has several practical applications in information sciences. Existing approaches toward name gender identification rely exclusively on using the gender distributions from labeled data. In the absence of such labeled data, these methods fail. In this article, we propose a two-stage model that is able to infer the gender of names present in text without requiring explicit name-gender labels. We use coreference resolution as the backbone for our proposed model. To aid coreference resolution where the existing contextual information does not suffice, we use a retrieval-assisted context aggregation framework. We demonstrate that state-of-the-art name gender inference is possible without supervision. Our proposed method matches or outperforms several supervised approaches and commercially used methods on five English language datasets from different domains.

Date

22. 3.2023 12:00:14

0.019131469 = product of:
  0.038262937 = sum of:
    0.022910692 = weight(_text_:retrieval in 5839) [ClassicSimilarity], result of:
      0.022910692 = score(doc=5839,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 5839, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5839)
    0.0153522445 = product of:
      0.030704489 = sum of:
        0.030704489 = weight(_text_:22 in 5839) [ClassicSimilarity], result of:
          0.030704489 = score(doc=5839,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.19345059 = fieldWeight in 5839, 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=5839)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Purpose In The Invisible Substrate of Information Science, a landmark article about the discipline of information science, Marcia J. Bates wrote that ".we are always looking for the red thread of information in the social texture of people's lives" (1999a, p. 1048). To sharpen our understanding of information science and to elaborate Bates' idea, the work at hand answers the question: Just what does the red thread of information entail? Design/methodology/approach Through a close reading of Bates' oeuvre and by applying concepts from the reference literature of information science, nine composite entities that qualify as the red thread of information are identified, elaborated, and related to existing concepts in the information science literature. In the spirit of a scientist-poet (White, 1999), several playful metaphors related to the color red are employed. Findings Bates' red thread of information entails: terms, genres, literatures, classification systems, scholarly communication, information retrieval, information experience, information institutions, and information policy. This same constellation of phenomena can be found in resonant visions of information science, namely, domain analysis (Hjørland, 2002), ethnography of infrastructure (Star, 1999), and social epistemology (Shera, 1968). Research limitations/implications With the vital vermilion filament in clear view, newcomers can more easily engage the material, conceptual, and social machinery of information science, and specialists are reminded of what constitutes information science as a whole. Future researchers and scientist-poets may wish to supplement the nine composite entities with additional, emergent information phenomena. Originality/value Though the explication of information science that follows is relatively orthodox and time-bound, the paper offers an imaginative, accessible, yet technically precise way of understanding the field.

Date

30. 4.2020 21:03:22

0.019131469 = product of:
  0.038262937 = sum of:
    0.022910692 = weight(_text_:retrieval in 34) [ClassicSimilarity], result of:
      0.022910692 = score(doc=34,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 34, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=34)
    0.0153522445 = product of:
      0.030704489 = sum of:
        0.030704489 = weight(_text_:22 in 34) [ClassicSimilarity], result of:
          0.030704489 = score(doc=34,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.19345059 = fieldWeight in 34, 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=34)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

For laypeople, searching online health information resources can be challenging due to topic complexity and the large number of online sources with differing quality. The goal of this article is to examine, among all the available online sources, which online sources laypeople select to address their health-related information needs, and whether or how much the severity of a health condition influences their selection. Twenty-four participants were recruited individually, and each was asked (using a retrieval system called HIS) to search for information regarding a severe health condition and a mild health condition, respectively. The selected online health information sources were automatically captured by the HIS system and classified at both the website and webpage levels. Participants' selection behavior patterns were then plotted across the whole information-seeking process. Our results demonstrate that laypeople's source selection fluctuates during the health information-seeking process, and also varies by the severity of health conditions. This study reveals laypeople's real usage of different types of online health information sources, and engenders implications to the design of search engines, as well as the development of health literacy programs.

Date

12.11.2020 13:22:09

0.019131469 = product of:
  0.038262937 = sum of:
    0.022910692 = weight(_text_:retrieval in 1012) [ClassicSimilarity], result of:
      0.022910692 = score(doc=1012,freq=2.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.16710453 = fieldWeight in 1012, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1012)
    0.0153522445 = product of:
      0.030704489 = sum of:
        0.030704489 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
          0.030704489 = score(doc=1012,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.19345059 = fieldWeight in 1012, 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=1012)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.

Date

22. 6.2023 14:55:20

0.019101143 = product of:
  0.038202286 = sum of:
    0.02592049 = weight(_text_:retrieval in 566) [ClassicSimilarity], result of:
      0.02592049 = score(doc=566,freq=4.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.18905719 = fieldWeight in 566, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=566)
    0.012281795 = product of:
      0.02456359 = sum of:
        0.02456359 = weight(_text_:22 in 566) [ClassicSimilarity], result of:
          0.02456359 = score(doc=566,freq=2.0), product of:
            0.15872006 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045324896 = queryNorm
            0.15476047 = fieldWeight in 566, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=566)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Content

Section 1 Context and purpose of knowledge architecture -- 1 Making the case for knowledge architecture -- 2 The landscape of knowledge assets -- 3 Knowledge architecture and design -- 4 Knowledge architecture reference model -- 5 Knowledge architecture segments -- Section 2 Designing for availability -- 6 Knowledge object modeling -- 7 Knowledge structures for encoding, formatting, and packaging -- 8 Functional architecture for identification and distinction -- 9 Functional architectures for knowledge asset disposition and destruction -- 10 Functional architecture designs for knowledge preservation and conservation -- Section 3 Designing for accessibility -- 11 Functional architectures for knowledge seeking and discovery -- 12 Functional architecture for knowledge search -- 13 Functional architecture for knowledge categorization -- 14 Functional architectures for indexing and keywording -- 15 Functional architecture for knowledge semantics -- 16 Functional architecture for knowledge abstraction and surrogation -- Section 4 Functional architectures to support knowledge consumption -- 17 Functional architecture for knowledge augmentation, derivation, and synthesis -- 18 Functional architecture to manage risk and harm -- 19 Functional architectures for knowledge authentication and provenance -- 20 Functional architectures for securing knowledge assets -- 21 Functional architectures for authorization and asset management -- Section 5 Pulling it all together - the big picture knowledge architecture -- 22 Functional architecture for knowledge metadata and metainformation -- 23 The whole knowledge architecture - pulling it all together

LCSH

Information storage and retrieval systems / Management

Subject

Information storage and retrieval systems / Management

0.016200306 = product of:
  0.06480122 = sum of:
    0.06480122 = weight(_text_:retrieval in 520) [ClassicSimilarity], result of:
      0.06480122 = score(doc=520,freq=16.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.47264296 = fieldWeight in 520, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=520)
  0.25 = coord(1/4)

Abstract

This paper reviews a large number of research achievements relevant to user privacy protection in an untrusted network environment, and then analyzes and evaluates their application limitations in personalized information retrieval, to establish the conditional constraints that an effective approach for user preference privacy protection in personalized information retrieval should meet, thus providing a basic reference for the solution of this problem. First, based on the basic framework of a personalized information retrieval platform, we establish a complete set of constraints for user preference privacy protection in terms of security, usability, efficiency, and accuracy. Then, we comprehensively review the technical features for all kinds of popular methods for user privacy protection, and analyze their application limitations in personalized information retrieval, according to the constraints of preference privacy protection. The results show that personalized information retrieval has higher requirements for users' privacy protection, i.e., it is required to comprehensively improve the security of users' preference privacy on the untrusted server-side, under the precondition of not changing the platform, algorithm, efficiency, and accuracy of personalized information retrieval. However, all kinds of existing privacy methods still cannot meet the above requirements. This paper is an important study attempt to the problem of user preference privacy protection of personalized information retrieval, which can provide a basic reference and direction for the further study of the problem.

0.016037485 = product of:
  0.06414994 = sum of:
    0.06414994 = weight(_text_:retrieval in 638) [ClassicSimilarity], result of:
      0.06414994 = score(doc=638,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.46789268 = fieldWeight in 638, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=638)
  0.25 = coord(1/4)

Abstract

Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat-B collection.

0.016037485 = product of:
  0.06414994 = sum of:
    0.06414994 = weight(_text_:retrieval in 667) [ClassicSimilarity], result of:
      0.06414994 = score(doc=667,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.46789268 = fieldWeight in 667, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0546875 = fieldNorm(doc=667)
  0.25 = coord(1/4)

Abstract

We present an overview of the NTCIR Math Tasks organized during NTCIR-10, 11, and 12. These tasks are primarily dedicated to techniques for searching mathematical content with formula expressions. In this chapter, we first summarize the task design and introduce test collections generated in the tasks. We also describe the features and main challenges of mathematical information retrieval systems and discuss future perspectives in the field.

Series

¬The Information retrieval series, vol 43

Source

Evaluating information retrieval and access tasks. Eds.: Sakai, T., Oard, D., Kando, N. [https://doi.org/10.1007/978-981-15-5554-1_12]

0.013746415 = product of:
  0.05498566 = sum of:
    0.05498566 = weight(_text_:retrieval in 5365) [ClassicSimilarity], result of:
      0.05498566 = score(doc=5365,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.40105087 = fieldWeight in 5365, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5365)
  0.25 = coord(1/4)

Abstract

Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to Wikipedia. Information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.

0.013746415 = product of:
  0.05498566 = sum of:
    0.05498566 = weight(_text_:retrieval in 1045) [ClassicSimilarity], result of:
      0.05498566 = score(doc=1045,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.40105087 = fieldWeight in 1045, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=1045)
  0.25 = coord(1/4)

Abstract

The initial dimensions extracted by latent semantic analysis (LSA) of a document-term matrixhave been shown to mainly display marginal effects, which are irrelevant for informationretrieval. To improve the performance of LSA, usually the elements of the raw document-term matrix are weighted and the weighting exponent of singular values can be adjusted.An alternative information retrieval technique that ignores the marginal effects is correspon-dence analysis (CA). In this paper, the information retrieval performance of LSA and CA isempirically compared. Moreover, it is explored whether the two weightings also improve theperformance of CA. The results for four empirical datasets show that CA always performsbetter than LSA. Weighting the elements of the raw data matrix can improve CA; however,it is data dependent and the improvement is small. Adjusting the singular value weightingexponent often improves the performance of CA; however, the extent of the improvementdepends on the dataset and the number of dimensions. (PDF) Improving information retrieval through correspondence analysis instead of latent semantic analysis.

0.012960245 = product of:
  0.05184098 = sum of:
    0.05184098 = weight(_text_:retrieval in 249) [ClassicSimilarity], result of:
      0.05184098 = score(doc=249,freq=4.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.37811437 = fieldWeight in 249, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0625 = fieldNorm(doc=249)
  0.25 = coord(1/4)

Abstract

This paper describes an approach to path-finding in the intelligent graphs, with vertices being intelligent agents. A possible implementation of this approach is described, based on logical inference in distributed frame hierarchy. Presented approach can be used for implementing distributed intelligent information systems that include automatic navigation and path generation in hypertext, which can be used, for example in distance education, as well as for organizing intelligent web catalogues with flexible ontology-based information retrieval.

0.012123198 = product of:
  0.048492793 = sum of:
    0.048492793 = weight(_text_:retrieval in 1106) [ClassicSimilarity], result of:
      0.048492793 = score(doc=1106,freq=14.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.3536936 = fieldWeight in 1106, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.03125 = fieldNorm(doc=1106)
  0.25 = coord(1/4)

Abstract

The study's objective is to analyze the structure of knowledge organization studies conducted worldwide. This applied research has been conducted with a scientometrics approach using the co-word analysis. The research records consisted of all articles published in the journals of Knowledge Organization and Cataloging & Classification Quarterly and keywords related to the field of knowledge organization indexed in Web of Science from 1900 to 2019, in which 17,950 records were analyzed entirely with plain text format. The total number of keywords was 25,480, which was reduced to 12,478 keywords after modifications and removal of duplicates. Then, 115 keywords with a frequency of at least 18 were included in the final analysis, and finally, the co-word network was drawn. BibExcel, UCINET, VOSviewer, and SPSS software were used to draw matrices, analyze co-word networks, and draw dendrograms. Furthermore, strategic diagrams were drawn using Excel software. The keywords "information retrieval," "classification," and "ontology" are among the most frequently used keywords in knowledge organization articles. Findings revealed that "Ontology*Semantic Web", "Digital Library*Information Retrieval" and "Indexing*Information Retrieval" are highly frequent co-word pairs, respectively. The results of hierarchical clustering indicated that the global research on knowledge organization consists of eight main thematic clusters; the largest is specified for the topic of "classification, indexing, and information retrieval." The smallest clusters deal with the topics of "data processing" and "theoretical concepts of information and knowledge organization" respectively. Cluster 1 (cataloging standards and knowledge organization) has the highest density, while Cluster 5 (classification, indexing, and information retrieval) has the highest centrality. According to the findings of this research, the keyword "information retrieval" has played a significant role in knowledge organization studies, both as a keyword and co-word pair. In the co-word section, there is a type of related or general topic relationship between co-word pairs. Results indicated that information retrieval is one of the main topics in knowledge organization, while the theoretical concepts of knowledge organization have been neglected. In general, the co-word structure of knowledge organization research indicates the multiplicity of global concepts and topics studied in this field globally.

0.011904745 = product of:
  0.04761898 = sum of:
    0.04761898 = weight(_text_:retrieval in 5761) [ClassicSimilarity], result of:
      0.04761898 = score(doc=5761,freq=6.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.34732026 = fieldWeight in 5761, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=5761)
  0.25 = coord(1/4)

Abstract

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

0.011904745 = product of:
  0.04761898 = sum of:
    0.04761898 = weight(_text_:retrieval in 67) [ClassicSimilarity], result of:
      0.04761898 = score(doc=67,freq=6.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.34732026 = fieldWeight in 67, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=67)
  0.25 = coord(1/4)

Abstract

In recent years, cross-modal retrieval has been a popular research topic in both fields of computer vision and natural language processing. There is a huge semantic gap between different modalities on account of heterogeneous properties. How to establish the correlation among different modality data faces enormous challenges. In this work, we propose a novel end-to-end framework named Dual Multi-Angle Self-Attention (DMASA) for cross-modal retrieval. Multiple self-attention mechanisms are applied to extract fine-grained features for both images and texts from different angles. We then integrate coarse-grained and fine-grained features into a multimodal embedding space, in which the similarity degrees between images and texts can be directly compared. Moreover, we propose a special multistage training strategy, in which the preceding stage can provide a good initial value for the succeeding stage and make our framework work better. Very promising experimental results over the state-of-the-art methods can be achieved on three benchmark datasets of Flickr8k, Flickr30k, and MSCOCO.

0.011904745 = product of:
  0.04761898 = sum of:
    0.04761898 = weight(_text_:retrieval in 185) [ClassicSimilarity], result of:
      0.04761898 = score(doc=185,freq=6.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.34732026 = fieldWeight in 185, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=185)
  0.25 = coord(1/4)

Abstract

Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides' philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus' flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato's theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato's dialectical method is an appropriate composite method for evaluating information retrieval systems.

0.011904745 = product of:
  0.04761898 = sum of:
    0.04761898 = weight(_text_:retrieval in 444) [ClassicSimilarity], result of:
      0.04761898 = score(doc=444,freq=6.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.34732026 = fieldWeight in 444, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=444)
  0.25 = coord(1/4)

Abstract

A citation index is a bibliographic database that provides citation links between documents. The first modern citation index was suggested by the researcher Eugene Garfield in 1955 and created by him in 1964, and it represents an important innovation to knowledge organization and information retrieval. This article describes citation indexes in general, considering the modern citation indexes, including Web of Science, Scopus, Google Scholar, Microsoft Academic, Crossref, Dimensions and some special citation indexes and predecessors to the modern citation index like Shepard's Citations. We present comparative studies of the major ones and survey theoretical problems related to the role of citation indexes as subject access points (SAP), recognizing the implications to knowledge organization and information retrieval. Finally, studies on citation behavior are presented and the influence of citation indexes on knowledge organization, information retrieval and the scientific information ecosystem is recognized.

0.011455346 = product of:
  0.045821384 = sum of:
    0.045821384 = weight(_text_:retrieval in 5678) [ClassicSimilarity], result of:
      0.045821384 = score(doc=5678,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.33420905 = fieldWeight in 5678, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5678)
  0.25 = coord(1/4)

Abstract

Pseudo-relevance feedback is a well-studied query expansion technique in which it is assumed that the top-ranked documents in an initial set of retrieval results are relevant and expansion terms are then extracted from those documents. When selecting expansion terms, most traditional models do not simultaneously consider term frequency and the co-occurrence relationships between candidate terms and query terms. Intuitively, however, a term that has a higher co-occurrence with a query term is more likely to be related to the query topic. In this article, we propose a kernel co-occurrence-based framework to enhance retrieval performance by integrating term co-occurrence information into the Rocchio model and a relevance language model (RM3). Specifically, a kernel co-occurrence-based Rocchio method (KRoc) and a kernel co-occurrence-based RM3 method (KRM3) are proposed. In our framework, co-occurrence information is incorporated into both the factor of the term discrimination power and the factor of the within-document term weight to boost retrieval performance. The results of a series of experiments show that our proposed methods significantly outperform the corresponding strong baselines over all data sets in terms of the mean average precision and over most data sets in terms of P@10. A direct comparison of standard Text Retrieval Conference data sets indicates that our proposed methods are at least comparable to state-of-the-art approaches.

0.011455346 = product of:
  0.045821384 = sum of:
    0.045821384 = weight(_text_:retrieval in 5732) [ClassicSimilarity], result of:
      0.045821384 = score(doc=5732,freq=8.0), product of:
        0.13710396 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.045324896 = queryNorm
        0.33420905 = fieldWeight in 5732, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5732)
  0.25 = coord(1/4)

Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).