Search (184 results, page 1 of 10)

0.07674606 = product of:
  0.15349212 = sum of:
    0.014975886 = weight(_text_:information in 3279) [ClassicSimilarity], result of:
      0.014975886 = score(doc=3279,freq=2.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.19395474 = fieldWeight in 3279, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=3279)
    0.10871997 = weight(_text_:networks in 3279) [ClassicSimilarity], result of:
      0.10871997 = score(doc=3279,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.52258724 = fieldWeight in 3279, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.078125 = fieldNorm(doc=3279)
    0.029796265 = product of:
      0.05959253 = sum of:
        0.05959253 = weight(_text_:22 in 3279) [ClassicSimilarity], result of:
          0.05959253 = score(doc=3279,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.38690117 = fieldWeight in 3279, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3279)
      0.5 = coord(1/2)
  0.5 = coord(3/6)

Series

Communications in computer and information science; 672

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.061977558 = product of:
  0.18593267 = sum of:
    0.011980709 = weight(_text_:information in 6934) [ClassicSimilarity], result of:
      0.011980709 = score(doc=6934,freq=2.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.1551638 = fieldWeight in 6934, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=6934)
    0.17395195 = weight(_text_:networks in 6934) [ClassicSimilarity], result of:
      0.17395195 = score(doc=6934,freq=8.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.8361396 = fieldWeight in 6934, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.0625 = fieldNorm(doc=6934)
  0.33333334 = coord(2/6)

Abstract

The equivalence of semantic networks with spreading activation and vector spaces with dot product is investigated under ranked retrieval. Semantic networks are viewed as networks of concepts organized in terms of abstraction and packaging relations. It is shown that the two models can be effectively constructed from each other. A formal method is suggested to analyze the models in terms of their relative performance in the same universe of objects

Source

Journal of the American Society for Information Science and technology. 52(2001) no.14, S.1224-1233

0.0589638 = product of:
  0.1179276 = sum of:
    0.020966241 = weight(_text_:information in 1319) [ClassicSimilarity], result of:
      0.020966241 = score(doc=1319,freq=8.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.27153665 = fieldWeight in 1319, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1319)
    0.07610398 = weight(_text_:networks in 1319) [ClassicSimilarity], result of:
      0.07610398 = score(doc=1319,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.36581108 = fieldWeight in 1319, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1319)
    0.020857384 = product of:
      0.04171477 = sum of:
        0.04171477 = weight(_text_:22 in 1319) [ClassicSimilarity], result of:
          0.04171477 = score(doc=1319,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.2708308 = fieldWeight in 1319, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1319)
      0.5 = coord(1/2)
  0.5 = coord(3/6)

Abstract

Keyword based querying has been an immediate and efficient way to specify and retrieve related information that the user inquired. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given. Proposes an idea to integrate 2 existing techniques, query expansion and relevance feedback to achieve a concept-based information search for the Web

Date

1. 8.1996 22:08:06

Source

Computer networks and ISDN systems. 30(1998) nos.1/7, S.621-623

0.02975843 = product of:
  0.089275286 = sum of:
    0.012707461 = weight(_text_:information in 5083) [ClassicSimilarity], result of:
      0.012707461 = score(doc=5083,freq=4.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.16457605 = fieldWeight in 5083, 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=5083)
    0.07656782 = product of:
      0.15313564 = sum of:
        0.15313564 = weight(_text_:states in 5083) [ClassicSimilarity], result of:
          0.15313564 = score(doc=5083,freq=6.0), product of:
            0.24220218 = queryWeight, product of:
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.043984205 = queryNorm
            0.63226366 = fieldWeight in 5083, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.046875 = fieldNorm(doc=5083)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

This paper describes the development and testing of a novel Automatic Search Query Enhancement (ASQE) algorithm, the Wikipedia N Sub-state Algorithm (WNSSA), which utilises Wikipedia as the sole data source for prior knowledge. This algorithm is built upon the concept of iterative states and sub-states, harnessing the power of Wikipedia's data set and link information to identify and utilise reoccurring terms to aid term selection and weighting during enhancement. This algorithm is designed to prevent query drift by making callbacks to the user's original search intent by persisting the original query between internal states with additional selected enhancement terms. The developed algorithm has shown to improve both short and long queries by providing a better understanding of the query and available data. The proposed algorithm was compared against five existing ASQE algorithms that utilise Wikipedia as the sole data source, showing an average Mean Average Precision (MAP) improvement of 0.273 over the tested existing ASQE algorithms.

Source

Information processing and management. 54(2018) no.4, S.726-739

0.029668488 = product of:
  0.08900546 = sum of:
    0.023773482 = weight(_text_:information in 919) [ClassicSimilarity], result of:
      0.023773482 = score(doc=919,freq=14.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.3078936 = fieldWeight in 919, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=919)
    0.06523198 = weight(_text_:networks in 919) [ClassicSimilarity], result of:
      0.06523198 = score(doc=919,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.31355235 = fieldWeight in 919, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.046875 = fieldNorm(doc=919)
  0.33333334 = coord(2/6)

Abstract

Document retrieval is a highly interactive process dealing with large amounts of information. Visual representations can provide both a means for managing the complexity of large information structures and an interface style well suited to interactive manipulation. The system we have designed utilizes visually displayed graphic structures and a direct manipulation interface style to supply an integrated environment for retrieval. A common visually displayed network structure is used for query, document content, and term relations. A query can be modified through direct manipulation of its visual form by incorporating terms from any other information structure the system displays. An associative thesaurus of terms and an inter-document network provide information about a document collection that can complement other retrieval aids. Visualization of these large data structures makes use of fisheye views and overview diagrams to help overcome some of the inherent difficulties of orientation and navigation in large information structures.

0.028441414 = product of:
  0.08532424 = sum of:
    0.02009226 = weight(_text_:information in 2124) [ClassicSimilarity], result of:
      0.02009226 = score(doc=2124,freq=10.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.2602176 = fieldWeight in 2124, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2124)
    0.06523198 = weight(_text_:networks in 2124) [ClassicSimilarity], result of:
      0.06523198 = score(doc=2124,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.31355235 = fieldWeight in 2124, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.046875 = fieldNorm(doc=2124)
  0.33333334 = coord(2/6)

Abstract

The concept of an "information space" provides a powerful metaphor for guiding the design of interactive retrieval systems. We present a case study of related article search, a browsing tool designed to help users navigate the information space defined by results of the PubMed® search engine. This feature leverages content-similarity links that tie MEDLINE® citations together in a vast document network. We examine the effectiveness of related article search from two perspectives: a topological analysis of networks generated from information needs represented in the TREC 2005 genomics track and a query log analysis of real PubMed users. Together, data suggest that related article search is a useful feature and that browsing related articles has become an integral part of how users interact with PubMed.

Source

Information processing and management. 44(2008) no.5, S.1771-1783

0.027734347 = product of:
  0.08320304 = sum of:
    0.017971063 = weight(_text_:information in 3374) [ClassicSimilarity], result of:
      0.017971063 = score(doc=3374,freq=8.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.23274569 = fieldWeight in 3374, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=3374)
    0.06523198 = weight(_text_:networks in 3374) [ClassicSimilarity], result of:
      0.06523198 = score(doc=3374,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.31355235 = fieldWeight in 3374, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.046875 = fieldNorm(doc=3374)
  0.33333334 = coord(2/6)

Abstract

As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain's specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource's content, user's preferences, users' social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources' semantic information. The recommendation engine allows the promotion and discovery of unknown-unknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources.

Series

Communications in computer and information science; 631

0.02244316 = product of:
  0.06732948 = sum of:
    0.0129694985 = weight(_text_:information in 3188) [ClassicSimilarity], result of:
      0.0129694985 = score(doc=3188,freq=6.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.16796975 = fieldWeight in 3188, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
    0.054359984 = weight(_text_:networks in 3188) [ClassicSimilarity], result of:
      0.054359984 = score(doc=3188,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.26129362 = fieldWeight in 3188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3188)
  0.33333334 = coord(2/6)

Abstract

Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.

Source

Information processing and management. 52(2016) no.5, S.923-948

0.019038055 = product of:
  0.057114165 = sum of:
    0.014975886 = weight(_text_:information in 1352) [ClassicSimilarity], result of:
      0.014975886 = score(doc=1352,freq=2.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.19395474 = fieldWeight in 1352, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=1352)
    0.04213828 = product of:
      0.08427656 = sum of:
        0.08427656 = weight(_text_:22 in 1352) [ClassicSimilarity], result of:
          0.08427656 = score(doc=1352,freq=4.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.54716086 = fieldWeight in 1352, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=1352)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Date

22. 2.2003 17:25:39
22. 2.2003 18:17:40

0.018971303 = product of:
  0.05691391 = sum of:
    0.012707461 = weight(_text_:information in 3090) [ClassicSimilarity], result of:
      0.012707461 = score(doc=3090,freq=4.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.16457605 = fieldWeight in 3090, 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=3090)
    0.044206448 = product of:
      0.088412896 = sum of:
        0.088412896 = weight(_text_:states in 3090) [ClassicSimilarity], result of:
          0.088412896 = score(doc=3090,freq=2.0), product of:
            0.24220218 = queryWeight, product of:
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.043984205 = queryNorm
            0.3650376 = fieldWeight in 3090, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.046875 = fieldNorm(doc=3090)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

Recent Web-searching and -mining tools are combining text and link analysis to improve ranking and crawling algorithms. The central assumption behind such approaches is that there is a correiation between the graph structure of the Web and the text and meaning of pages. Here I formalize and empirically evaluate two general conjectures drawing connections from link information to lexical and semantic Web content. The link-content conjecture states that a page is similar to the pages that link to it, and the link-cluster conjecture that pages about the same topic are clustered together. These conjectures are offen simply assumed to hold, and Web search tools are built an such assumptions. The present quantitative confirmation sheds light an the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.14, S.1261-1269

0.01878385 = product of:
  0.05635155 = sum of:
    0.014673311 = weight(_text_:information in 2591) [ClassicSimilarity], result of:
      0.014673311 = score(doc=2591,freq=12.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.19003606 = fieldWeight in 2591, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.03125 = fieldNorm(doc=2591)
    0.04167824 = product of:
      0.08335648 = sum of:
        0.08335648 = weight(_text_:states in 2591) [ClassicSimilarity], result of:
          0.08335648 = score(doc=2591,freq=4.0), product of:
            0.24220218 = queryWeight, product of:
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.043984205 = queryNorm
            0.34416074 = fieldWeight in 2591, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.03125 = fieldNorm(doc=2591)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

This study examined the success and information seeking behaviors of seventh-grade science students and graduate students in information science in using Yahooligans! Web search engine/directory. It investigated these users' cognitive, affective, and physical behaviors as they sought the answer for a fact-finding task. It analyzed and compared the overall patterns of children's and graduate students' Web activities, including searching moves, browsing moves, backtracking moves, looping moves, screen scrolling, target location and deviation moves, and the time they took to complete the task. The authors applied Bilal's Web Traversal Measure to quantify these users' effectiveness, efficiency, and quality of moves they made. Results were based on 14 children's Web sessions and nine graduate students' sessions. Both groups' Web activities were captured online using Lotus ScreenCam, a software package that records and replays online activities in Web browsers. Children's affective states were captured via exit interviews. Graduate students' affective states were extracted from the journal writings they kept during the traversal process. The study findings reveal that 89% of the graduate students found the correct answer to the search task as opposed to 50% of the children. Based on the Measure, graduate students' weighted effectiveness, efficiency, and quality of the Web moves they made were much higher than those of the children. Regardless of success and weighted scores, however, similarities and differences in information seeking were found between the two groups. Yahooligans! poor structure of keyword searching was a major factor that contributed to the "breakdowns" children and graduate students experienced. Unlike children, graduate students were able to recover from "breakdowns" quickly and effectively. Three main factors influenced these users' performance: ability to recover from "breakdowns", navigational style, and focus on task. Children and graduate students made recommendations for improving Yahooligans! interface design. Implications for Web user training and system design improvements are made.

Footnote

Beitrag in einem Themenheft: "Issues of context in information retrieval (IR)"

Source

Information processing and management. 38(2002) no.5, S.649-670

0.015884697 = product of:
  0.047654092 = sum of:
    0.018157298 = weight(_text_:information in 5295) [ClassicSimilarity], result of:
      0.018157298 = score(doc=5295,freq=6.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.23515764 = fieldWeight in 5295, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5295)
    0.029496796 = product of:
      0.058993593 = sum of:
        0.058993593 = weight(_text_:22 in 5295) [ClassicSimilarity], result of:
          0.058993593 = score(doc=5295,freq=4.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.38301262 = fieldWeight in 5295, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5295)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

A new search paradigm, in which the primary user activity is the guided exploration of a complex information space rather than the retrieval of items based on precise specifications, is proposed. The author claims that this paradigm is the norm in most practical applications, and that solutions based on traditional search methods are not effective in this context. He then presents a solution based on dynamic taxonomies, a knowledge management model that effectively guides users to reach their goal while giving them total freedom in exploring the information base. Applications, benefits, and current research are discussed.

Date

22. 7.2006 17:56:22

Source

Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.792-796

0.015692377 = product of:
  0.09415426 = sum of:
    0.09415426 = weight(_text_:networks in 3810) [ClassicSimilarity], result of:
      0.09415426 = score(doc=3810,freq=6.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.45257387 = fieldWeight in 3810, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3810)
  0.16666667 = coord(1/6)

Abstract

Nowadays, online data shows an astonishing increase and the issue of semantic indexing remains an open question. Ontologies and knowledge bases have been widely used to optimize performance. However, researchers are placing increased emphasis on internal relations of ontologies but neglect latent semantic relations between ontologies and documents. They generally annotate instances mentioned in documents, which are related to concepts in ontologies. In this paper, we propose an Ontology-based Latent Semantic Indexing approach utilizing Long Short-Term Memory networks (LSTM-OLSI). We utilize an importance-aware topic model to extract document-level semantic features and leverage ontologies to extract word-level contextual features. Then we encode the above two levels of features and match their embedding vectors utilizing LSTM networks. Finally, the experimental results reveal that LSTM-OLSI outperforms existing techniques and demonstrates deep comprehension of instances and articles.

0.014924051 = product of:
  0.04477215 = sum of:
    0.014975886 = weight(_text_:information in 3280) [ClassicSimilarity], result of:
      0.014975886 = score(doc=3280,freq=2.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.19395474 = fieldWeight in 3280, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=3280)
    0.029796265 = product of:
      0.05959253 = sum of:
        0.05959253 = weight(_text_:22 in 3280) [ClassicSimilarity], result of:
          0.05959253 = score(doc=3280,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.38690117 = fieldWeight in 3280, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=3280)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Series

Communications in computer and information science; 672

Source

Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou

0.014354773 = product of:
  0.04306432 = sum of:
    0.015884325 = weight(_text_:information in 1211) [ClassicSimilarity], result of:
      0.015884325 = score(doc=1211,freq=36.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.20572007 = fieldWeight in 1211, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
    0.027179992 = weight(_text_:networks in 1211) [ClassicSimilarity], result of:
      0.027179992 = score(doc=1211,freq=2.0), product of:
        0.20804176 = queryWeight, product of:
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.043984205 = queryNorm
        0.13064681 = fieldWeight in 1211, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.72992 = idf(docFreq=1060, maxDocs=44218)
          0.01953125 = fieldNorm(doc=1211)
  0.33333334 = coord(2/6)

Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

0.013295909 = product of:
  0.039887726 = sum of:
    0.022009967 = weight(_text_:information in 2419) [ClassicSimilarity], result of:
      0.022009967 = score(doc=2419,freq=12.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.2850541 = fieldWeight in 2419, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2419)
    0.017877758 = product of:
      0.035755515 = sum of:
        0.035755515 = weight(_text_:22 in 2419) [ClassicSimilarity], result of:
          0.035755515 = score(doc=2419,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.23214069 = fieldWeight in 2419, 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=2419)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

The digital library system Daffodil is targeted at strategic support of users during the information search process. For searching, exploring and managing digital library objects it provides user-customisable information seeking patterns over a federation of heterogeneous digital libraries. In this paper evaluation results with respect to retrieval effectiveness, efficiency and user satisfaction are presented. The analysis focuses on strategic support for the scientific work-flow. Daffodil supports the whole work-flow, from data source selection over information seeking to the representation, organisation and reuse of information. By embedding high level search functionality into the scientific work-flow, the user experiences better strategic system support due to a more systematic work process. These ideas have been implemented in Daffodil followed by a qualitative evaluation. The evaluation has been conducted with 28 participants, ranging from information seeking novices to experts. The results are promising, as they support the chosen model.

Date

16.11.2008 16:22:48

0.013244277 = product of:
  0.03973283 = sum of:
    0.024834696 = weight(_text_:information in 1428) [ClassicSimilarity], result of:
      0.024834696 = score(doc=1428,freq=22.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.32163754 = fieldWeight in 1428, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1428)
    0.0148981325 = product of:
      0.029796265 = sum of:
        0.029796265 = weight(_text_:22 in 1428) [ClassicSimilarity], result of:
          0.029796265 = score(doc=1428,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.19345059 = fieldWeight in 1428, 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=1428)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. This article furnishes an account of information inference from a psychologistic stance. By drawing an theories from nonclassical logic and applied cognition, an information inference mechanism is proposed that makes inferences via computations of information flow through an approximation of a conceptual space. Within a conceptual space information is represented geometrically. In this article, geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept an the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favorably with two probabilistic query models, and another based an semantic association. More generally, this article can be seen as a contribution towards realizing operational systems that mimic text-based human reasoning.

Date

22. 3.2003 19:35:46

Footnote

Beitrag eines Themenheftes: Mathematical, logical, and formal methods in information retrieval

Source

Journal of the American Society for Information Science and technology. 54(2003) no.4, S.321-334

0.013004894 = product of:
  0.039014682 = sum of:
    0.018157298 = weight(_text_:information in 6958) [ClassicSimilarity], result of:
      0.018157298 = score(doc=6958,freq=6.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.23515764 = fieldWeight in 6958, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6958)
    0.020857384 = product of:
      0.04171477 = sum of:
        0.04171477 = weight(_text_:22 in 6958) [ClassicSimilarity], result of:
          0.04171477 = score(doc=6958,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.2708308 = fieldWeight in 6958, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=6958)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Source

Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon

0.010446835 = product of:
  0.031340506 = sum of:
    0.010483121 = weight(_text_:information in 1026) [ClassicSimilarity], result of:
      0.010483121 = score(doc=1026,freq=2.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.13576832 = fieldWeight in 1026, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1026)
    0.020857384 = product of:
      0.04171477 = sum of:
        0.04171477 = weight(_text_:22 in 1026) [ClassicSimilarity], result of:
          0.04171477 = score(doc=1026,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.2708308 = fieldWeight in 1026, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1026)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

We have created software applications that allow users to both author and use Semantic Web metadata. To create and use a layer of semantic content on top of the existing Web, we have (1) implemented a user interface that expedites the task of attributing metadata to resources on the Web, and (2) augmented a Web browser to leverage this semantic metadata to provide relevant information and tasks to the user. This project provides a framework for annotating and reorganizing existing files, pages, and sites on the Web that is similar to Vannevar Bushrsquos original concepts of trail blazing and associative indexing.

Source

Research and advanced technology for digital libraries : 7th European Conference, proceedings / ECDL 2003, Trondheim, Norway, August 17-22, 2003

0.010446835 = product of:
  0.031340506 = sum of:
    0.010483121 = weight(_text_:information in 1852) [ClassicSimilarity], result of:
      0.010483121 = score(doc=1852,freq=2.0), product of:
        0.0772133 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.043984205 = queryNorm
        0.13576832 = fieldWeight in 1852, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1852)
    0.020857384 = product of:
      0.04171477 = sum of:
        0.04171477 = weight(_text_:22 in 1852) [ClassicSimilarity], result of:
          0.04171477 = score(doc=1852,freq=2.0), product of:
            0.1540252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043984205 = queryNorm
            0.2708308 = fieldWeight in 1852, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1852)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Date

11. 2.2011 18:22:58

Source

Information - Wissenschaft und Praxis. 56(2005) H.5/6, S.281-290