Search (9 results, page 1 of 1)

0.07033957 = product of:
  0.10550935 = sum of:
    0.0895263 = weight(_text_:development in 1197) [ClassicSimilarity], result of:
      0.0895263 = score(doc=1197,freq=6.0), product of:
        0.16011542 = queryWeight, product of:
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.04384008 = queryNorm
        0.55913603 = fieldWeight in 1197, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.0625 = fieldNorm(doc=1197)
    0.015983047 = product of:
      0.04794914 = sum of:
        0.04794914 = weight(_text_:29 in 1197) [ClassicSimilarity], result of:
          0.04794914 = score(doc=1197,freq=2.0), product of:
            0.1542157 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04384008 = queryNorm
            0.31092256 = fieldWeight in 1197, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0625 = fieldNorm(doc=1197)
      0.33333334 = coord(1/3)
  0.6666667 = coord(2/3)

Abstract

Using citation data of articles written by some Nobel Prize winners in physics, we show that concave, convex, and straight curves represent different types of interactions between old ideas and new insights. These cases illustrate different diffusion characteristics of academic knowledge, depending on the nature of the knowledge in the new publications. This work adds to the study of the development of science and links this development to citation analysis.

Date

29. 1.2014 16:31:35

0.012922008 = product of:
  0.038766023 = sum of:
    0.038766023 = weight(_text_:development in 1764) [ClassicSimilarity], result of:
      0.038766023 = score(doc=1764,freq=2.0), product of:
        0.16011542 = queryWeight, product of:
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.04384008 = queryNorm
        0.242113 = fieldWeight in 1764, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.046875 = fieldNorm(doc=1764)
  0.33333334 = coord(1/3)

Abstract

Purpose - This paper aims to provide a new insight into the reasons why authors cite. Design/methodology/approach The authors argue that, based on philosophical ideas about the essence of things, pure rational thinking about the role of citations leads to the answer. Findings - Citations originate from the interestingness of the investigated phenomenon. The essence of citation lies in the interaction between different ideas or perspectives on a phenomenon addressed in the citing as well as in the cited articles. Research limitations/implications - The findings only apply to ethical (not whimsical or self-serving) citations. As such citations reflect interactions of scientific ideas, they can reveal the evolution of science, revive the cognitive process of an investigated scientific phenomenon and reveal political and economic factors influencing the development of science. Originality/value - This article is the first to propose interestingness and the interaction of ideas as the basic reason for citing. This view on citations allows reverse engineering from citations to ideas and hence becomes useful for science policy.

0.010768341 = product of:
  0.03230502 = sum of:
    0.03230502 = weight(_text_:development in 3642) [ClassicSimilarity], result of:
      0.03230502 = score(doc=3642,freq=2.0), product of:
        0.16011542 = queryWeight, product of:
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.04384008 = queryNorm
        0.20176083 = fieldWeight in 3642, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3642)
  0.33333334 = coord(1/3)

Abstract

With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large-scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network-based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context-Aware LDA Model (LC-LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large-scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state-of-the-art peer models.

0.010768341 = product of:
  0.03230502 = sum of:
    0.03230502 = weight(_text_:development in 98) [ClassicSimilarity], result of:
      0.03230502 = score(doc=98,freq=2.0), product of:
        0.16011542 = queryWeight, product of:
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.04384008 = queryNorm
        0.20176083 = fieldWeight in 98, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.0390625 = fieldNorm(doc=98)
  0.33333334 = coord(1/3)

Abstract

With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.

0.008614672 = product of:
  0.025844015 = sum of:
    0.025844015 = weight(_text_:development in 4239) [ClassicSimilarity], result of:
      0.025844015 = score(doc=4239,freq=2.0), product of:
        0.16011542 = queryWeight, product of:
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.04384008 = queryNorm
        0.16140866 = fieldWeight in 4239, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.03125 = fieldNorm(doc=4239)
  0.33333334 = coord(1/3)

Abstract

With the advent of various services and applications of Semantic Web, semantic annotation has emerged as an important research topic. The application of semantically annotated ontology had been evident in numerous information processing and retrieval tasks. One of such tasks is utilizing the semantically annotated ontology in product design which is able to suggest many important applications that are critical to aid various design related tasks. However, ontology development in design engineering remains a time consuming and tedious task that demands considerable human efforts. In the context of product family design, management of different product information that features efficient indexing, update, navigation, search and retrieval across product families is both desirable and challenging. For instance, an efficient way of retrieving timely information on product family can be useful for tasks such as product family redesign and new product variant derivation when requirements change. However, the current research and application of information search and navigation in product family is mostly limited to its structural aspect which is insufficient to handle advanced information search especially when the query targets at multiple aspects of a product. This paper attempts to address this problem by proposing an information search and retrieval framework based on the semantically annotated multi-facet product family ontology. Particularly, we propose a document profile (DP) model to suggest semantic tags for annotation purpose. Using a case study of digital camera families, we illustrate how the faceted search and retrieval of product information can be accomplished. We also exemplify how we can derive new product variants based on the designer's query of requirements via the faceted search and retrieval of product family information. Lastly, in order to highlight the value of our current work, we briefly discuss some further research and applications in design decision support, e.g. commonality analysis and variety comparison, based on the semantically annotated multi-facet product family ontology.

0.008614672 = product of:
  0.025844015 = sum of:
    0.025844015 = weight(_text_:development in 1485) [ClassicSimilarity], result of:
      0.025844015 = score(doc=1485,freq=2.0), product of:
        0.16011542 = queryWeight, product of:
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.04384008 = queryNorm
        0.16140866 = fieldWeight in 1485, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.652261 = idf(docFreq=3116, maxDocs=44218)
          0.03125 = fieldNorm(doc=1485)
  0.33333334 = coord(1/3)

Abstract

Product family design is one of the prevailing approaches in realizing mass customization. With the increasing number of product offerings targeted at different market segments, the issue of information management in product family design, that is related to an efficient and effective storage, sharing and timely retrieval of design information, has become more complicated and challenging. Product family modelling schema reported in the literature generally stress the component aspects of a product family and its analysis, with a limited capability to model complex inter-relations between physical components and other required information in different semantic orientations, such as manufacturing, material and marketing wise. To tackle this problem, ontology-based representation has been identified as a promising solution to redesign product platforms especially in a semantically rich environment. However, ontology development in design engineering demands a great deal of time commitment and human effort to process complex information. When a large variety of products are available, particularly in the consumer market, a more efficient method for building a product family ontology with the incorporation of multi-faceted semantic information is therefore highly desirable. In this study, we propose a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically-related annotations based on the design and manufacturing repository. The six steps of building such ontology: formation of product family taxonomy; extraction of entities; faceted unit generation and concept identification; facet modelling and semantic annotation; formation of a semantically annotated multi-faceted product family ontology (MFPFO); and ontology validation and evaluation are discussed in detail. Using a family of laptop computers as an illustrative example, we demonstrate how our methodology can be deployed step by step to create a semantically annotated MFPFO. Finally, we briefly discuss future research issues as well as interesting applications that can be further pursued based on the MFPFO developed.

0.0052797566 = product of:
  0.01583927 = sum of:
    0.01583927 = product of:
      0.047517806 = sum of:
        0.047517806 = weight(_text_:22 in 7744) [ClassicSimilarity], result of:
          0.047517806 = score(doc=7744,freq=2.0), product of:
            0.1535205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04384008 = queryNorm
            0.30952093 = fieldWeight in 7744, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=7744)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

22. 6.1997 16:34:51

0.0033298018 = product of:
  0.009989405 = sum of:
    0.009989405 = product of:
      0.029968213 = sum of:
        0.029968213 = weight(_text_:29 in 603) [ClassicSimilarity], result of:
          0.029968213 = score(doc=603,freq=2.0), product of:
            0.1542157 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.04384008 = queryNorm
            0.19432661 = fieldWeight in 603, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=603)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

5. 6.2022 17:03:29

0.003299848 = product of:
  0.009899544 = sum of:
    0.009899544 = product of:
      0.029698629 = sum of:
        0.029698629 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
          0.029698629 = score(doc=5492,freq=2.0), product of:
            0.1535205 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04384008 = queryNorm
            0.19345059 = fieldWeight in 5492, 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=5492)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)

Date

20. 1.2015 18:30:22