Search (13 results, page 1 of 1)

  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.12 
    0.12305402 = sum of:
  0.082684144 = product of:
    0.24805243 = sum of:
      0.24805243 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
        0.24805243 = score(doc=562,freq=2.0), product of:
          0.44136027 = queryWeight, product of:
            8.478011 = idf(docFreq=24, maxDocs=44218)
            0.052059412 = queryNorm
          0.56201804 = fieldWeight in 562, 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=562)
    0.33333334 = coord(1/3)
  0.019209886 = weight(_text_:the in 562) [ClassicSimilarity], result of:
    0.019209886 = score(doc=562,freq=10.0), product of:
      0.08213748 = queryWeight, product of:
        1.5777643 = idf(docFreq=24812, maxDocs=44218)
        0.052059412 = queryNorm
      0.2338748 = fieldWeight in 562, product of:
        3.1622777 = tf(freq=10.0), with freq of:
          10.0 = termFreq=10.0
        1.5777643 = idf(docFreq=24812, maxDocs=44218)
        0.046875 = fieldNorm(doc=562)
  0.021159993 = product of:
    0.042319987 = sum of:
      0.042319987 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
        0.042319987 = score(doc=562,freq=2.0), product of:
          0.18230319 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.052059412 = queryNorm
          0.23214069 = fieldWeight in 562, 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=562)
    0.5 = coord(1/2)
    Abstract
    Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
    Source
    Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK
  2. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.09 
    0.08876976 = product of:
  0.13315465 = sum of:
    0.110245526 = product of:
      0.33073658 = sum of:
        0.33073658 = weight(_text_:3a in 230) [ClassicSimilarity], result of:
          0.33073658 = score(doc=230,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.7493574 = fieldWeight in 230, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0625 = fieldNorm(doc=230)
      0.33333334 = coord(1/3)
    0.022909125 = weight(_text_:the in 230) [ClassicSimilarity], result of:
      0.022909125 = score(doc=230,freq=8.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.27891195 = fieldWeight in 230, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.0625 = fieldNorm(doc=230)
  0.6666667 = coord(2/3)
    Abstract
    In this lecture I intend to challenge those who uphold a monist or even a dualist view of the universe; and I will propose, instead, a pluralist view. I will propose a view of the universe that recognizes at least three different but interacting sub-universes.
    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  3. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.08 
    0.07925092 = product of:
  0.118876375 = sum of:
    0.09646484 = product of:
      0.28939453 = sum of:
        0.28939453 = weight(_text_:3a in 306) [ClassicSimilarity], result of:
          0.28939453 = score(doc=306,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.65568775 = fieldWeight in 306, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0546875 = fieldNorm(doc=306)
      0.33333334 = coord(1/3)
    0.022411533 = weight(_text_:the in 306) [ClassicSimilarity], result of:
      0.022411533 = score(doc=306,freq=10.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.2728539 = fieldWeight in 306, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.0546875 = fieldNorm(doc=306)
  0.6666667 = coord(2/3)
    Abstract
    Although service-oriented architectures go a long way toward providing interoperability in distributed, heterogeneous environments, managing semantic differences in such environments remains a challenge. We give an overview of the issue of semantic interoperability (integration), provide a semantic characterization of services, and discuss the role of ontologies. Then we analyze four basic models of semantic interoperability that differ in respect to their mapping between service descriptions and ontologies and in respect to where the evaluation of the integration logic is performed. We also provide some guidelines for selecting one of the possible interoperability models.
    Content
    Vgl.: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5386707&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5386707.
  4. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.07 
    0.074962646 = product of:
  0.11244397 = sum of:
    0.082684144 = product of:
      0.24805243 = sum of:
        0.24805243 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
          0.24805243 = score(doc=862,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = 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)
    0.029759828 = weight(_text_:the in 862) [ClassicSimilarity], result of:
      0.029759828 = score(doc=862,freq=24.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.36231726 = fieldWeight in 862, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.046875 = fieldNorm(doc=862)
  0.6666667 = coord(2/3)
    Abstract
    This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  5. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.07 
    0.071321964 = product of:
  0.10698294 = sum of:
    0.082684144 = product of:
      0.24805243 = sum of:
        0.24805243 = weight(_text_:3a in 2918) [ClassicSimilarity], result of:
          0.24805243 = score(doc=2918,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.56201804 = fieldWeight in 2918, 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=2918)
      0.33333334 = coord(1/3)
    0.024298795 = weight(_text_:the in 2918) [ClassicSimilarity], result of:
      0.024298795 = score(doc=2918,freq=16.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.2958308 = fieldWeight in 2918, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.046875 = fieldNorm(doc=2918)
  0.6666667 = coord(2/3)
    Abstract
    The employees of an organization often use a personal hierarchical classification scheme to organize digital documents that are stored on their own workstations. As this may make it hard for other employees to retrieve these documents, there is a risk that the organization will lose track of needed documentation. Furthermore, the inherent boundaries of such a hierarchical structure require making arbitrary decisions about which specific criteria the classification will b.e based on (for instance, the administrative activity or the document type, although a document can have several attributes and require classification in several classes).A faceted classification model to support corporate information organization is proposed. Partially based on Ranganathan's facets theory, this model aims not only to standardize the organization of digital documents, but also to simplify the management of a document throughout its life cycle for both individuals and organizations, while ensuring compliance to regulatory and policy requirements.
    Footnote
    Vgl.: http://ieeexplore.ieee.org/Xplore/login.jsp?reload=true&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4755313%2F4755314%2F04755480.pdf%3Farnumber%3D4755480&authDecision=-203.
  6. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.07 
    0.070275724 = product of:
  0.105413586 = sum of:
    0.082684144 = product of:
      0.24805243 = sum of:
        0.24805243 = weight(_text_:3a in 2514) [ClassicSimilarity], result of:
          0.24805243 = score(doc=2514,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.56201804 = fieldWeight in 2514, 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=2514)
      0.33333334 = coord(1/3)
    0.022729442 = weight(_text_:the in 2514) [ClassicSimilarity], result of:
      0.022729442 = score(doc=2514,freq=14.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.27672437 = fieldWeight in 2514, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.046875 = fieldNorm(doc=2514)
  0.6666667 = coord(2/3)
    Abstract
    In this paper, we present two ways to improve the precision of HITS-based algorithms onWeb documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
    Content
    Vgl.: http%3A%2F%2Fdelab.csd.auth.gr%2F~dimitris%2Fcourses%2Fir_spring06%2Fpage_rank_computing%2Fp527-li.pdf. Vgl. auch: http://www2002.org/CDROM/refereed/643/.
    Source
    WWW '02: Proceedings of the 11th International Conference on World Wide Web, May 7-11, 2002, Honolulu, Hawaii, USA
  7. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.07 
    0.069151685 = product of:
  0.10372752 = sum of:
    0.082684144 = product of:
      0.24805243 = sum of:
        0.24805243 = weight(_text_:3a in 400) [ClassicSimilarity], result of:
          0.24805243 = score(doc=400,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.56201804 = fieldWeight in 400, 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=400)
      0.33333334 = coord(1/3)
    0.021043373 = weight(_text_:the in 400) [ClassicSimilarity], result of:
      0.021043373 = score(doc=400,freq=12.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.25619698 = fieldWeight in 400, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.046875 = fieldNorm(doc=400)
  0.6666667 = coord(2/3)
    Abstract
    On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.
    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
    Source
    Graph-Based Methods for Natural Language Processing - proceedings of the Thirteenth Workshop (TextGraphs-13): November 4, 2019, Hong Kong : EMNLP-IJCNLP 2019. Ed.: Dmitry Ustalov
  8. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.07 
    0.06618464 = product of:
  0.09927695 = sum of:
    0.06890346 = product of:
      0.20671037 = sum of:
        0.20671037 = weight(_text_:3a in 4997) [ClassicSimilarity], result of:
          0.20671037 = score(doc=4997,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.46834838 = fieldWeight in 4997, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4997)
      0.33333334 = coord(1/3)
    0.030373493 = weight(_text_:the in 4997) [ClassicSimilarity], result of:
      0.030373493 = score(doc=4997,freq=36.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.36978847 = fieldWeight in 4997, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4997)
  0.6666667 = coord(2/3)
    Abstract
    While classifications are heavily used to categorize web content, the evolution of the web foresees a more formal structure - ontology - which can serve this purpose. Ontologies are core artifacts of the Semantic Web which enable machines to use inference rules to conduct automated reasoning on data. Lightweight ontologies bridge the gap between classifications and ontologies. A lightweight ontology (LO) is an ontology representing a backbone taxonomy where the concept of the child node is more specific than the concept of the parent node. Formal lightweight ontologies can be generated from their informal ones. The key applications of formal lightweight ontologies are document classification, semantic search, and data integration. However, these applications suffer from the following problems: the disambiguation accuracy of the state of the art NLP tools used in generating formal lightweight ontologies from their informal ones; the lack of background knowledge needed for the formal lightweight ontologies; and the limitation of ontology reuse. In this dissertation, we propose a novel solution to these problems in formal lightweight ontologies; namely, faceted lightweight ontology (FLO). FLO is a lightweight ontology in which terms, present in each node label, and their concepts, are available in the background knowledge (BK), which is organized as a set of facets. A facet can be defined as a distinctive property of the groups of concepts that can help in differentiating one group from another. Background knowledge can be defined as a subset of a knowledge base, such as WordNet, and often represents a specific domain.
    Content
    PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.
  9. Piros, A.: Az ETO-jelzetek automatikus interpretálásának és elemzésének kérdései (2018) 0.06 
    0.063793585 = product of:
  0.09569037 = sum of:
    0.06890346 = product of:
      0.20671037 = sum of:
        0.20671037 = weight(_text_:3a in 855) [ClassicSimilarity], result of:
          0.20671037 = score(doc=855,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.46834838 = fieldWeight in 855, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=855)
      0.33333334 = coord(1/3)
    0.026786905 = weight(_text_:the in 855) [ClassicSimilarity], result of:
      0.026786905 = score(doc=855,freq=28.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.3261228 = fieldWeight in 855, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.0390625 = fieldNorm(doc=855)
  0.6666667 = coord(2/3)
    Abstract
    Converting UDC numbers manually to a complex format such as the one mentioned above is an unrealistic expectation; supporting building these representations, as far as possible automatically, is a well-founded requirement. An additional advantage of this approach is that the existing records could also be processed and converted. In my dissertation I would like to prove also that it is possible to design and implement an algorithm that is able to convert pre-coordinated UDC numbers into the introduced format by identifying all their elements and revealing their whole syntactic structure as well. In my dissertation I will discuss a feasible way of building a UDC-specific XML schema for describing the most detailed and complicated UDC numbers (containing not only the common auxiliary signs and numbers, but also the different types of special auxiliaries). The schema definition is available online at: http://piros.udc-interpreter.hu#xsd. The primary goal of my research is to prove that it is possible to support building, retrieving, and analyzing UDC numbers without compromises, by taking the whole syntactic richness of the scheme by storing the UDC numbers reserving the meaning of pre-coordination. The research has also included the implementation of a software that parses UDC classmarks attended to prove that such solution can be applied automatically without any additional effort or even retrospectively on existing collections.
    Content
    Vgl. auch: New automatic interpreter for complex UDC numbers. Unter: <https%3A%2F%2Fudcc.org%2Ffiles%2FAttilaPiros_EC_36-37_2014-2015.pdf&usg=AOvVaw3kc9CwDDCWP7aArpfjrs5b>
  10. Ackermann, E.: Piaget's constructivism, Papert's constructionism : what's the difference? (2001) 0.06 
    0.06246888 = product of:
  0.093703315 = sum of:
    0.06890346 = product of:
      0.20671037 = sum of:
        0.20671037 = weight(_text_:3a in 692) [ClassicSimilarity], result of:
          0.20671037 = score(doc=692,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.46834838 = fieldWeight in 692, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=692)
      0.33333334 = coord(1/3)
    0.024799855 = weight(_text_:the in 692) [ClassicSimilarity], result of:
      0.024799855 = score(doc=692,freq=24.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.30193105 = fieldWeight in 692, product of:
          4.8989797 = tf(freq=24.0), with freq of:
            24.0 = termFreq=24.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.0390625 = fieldNorm(doc=692)
  0.6666667 = coord(2/3)
    Abstract
    What is the difference between Piaget's constructivism and Papert's "constructionism"? Beyond the mere play on the words, I think the distinction holds, and that integrating both views can enrich our understanding of how people learn and grow. Piaget's constructivism offers a window into what children are interested in, and able to achieve, at different stages of their development. The theory describes how children's ways of doing and thinking evolve over time, and under which circumstance children are more likely to let go of-or hold onto- their currently held views. Piaget suggests that children have very good reasons not to abandon their worldviews just because someone else, be it an expert, tells them they're wrong. Papert's constructionism, in contrast, focuses more on the art of learning, or 'learning to learn', and on the significance of making things in learning. Papert is interested in how learners engage in a conversation with [their own or other people's] artifacts, and how these conversations boost self-directed learning, and ultimately facilitate the construction of new knowledge. He stresses the importance of tools, media, and context in human development. Integrating both perspectives illuminates the processes by which individuals come to make sense of their experience, gradually optimizing their interactions with the world.
    Content
    Vgl.: https://www.semanticscholar.org/paper/Piaget-%E2%80%99-s-Constructivism-%2C-Papert-%E2%80%99-s-%3A-What-%E2%80%99-s-Ackermann/89cbcc1e740a4591443ff4765a6ae8df0fdf5554. Darunter weitere Hinweise auf verwandte Beiträge. Auch unter: Learning Group Publication 5(2001) no.3, S.438.
  11. Stojanovic, N.: Ontology-based Information Retrieval : methods and tools for cooperative query answering (2005) 0.06 
    0.060896844 = product of:
  0.091345266 = sum of:
    0.055122763 = product of:
      0.16536829 = sum of:
        0.16536829 = weight(_text_:3a in 701) [ClassicSimilarity], result of:
          0.16536829 = score(doc=701,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.3746787 = fieldWeight in 701, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=701)
      0.33333334 = coord(1/3)
    0.036222506 = weight(_text_:the in 701) [ClassicSimilarity], result of:
      0.036222506 = score(doc=701,freq=80.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.44099852 = fieldWeight in 701, product of:
          8.944272 = tf(freq=80.0), with freq of:
            80.0 = termFreq=80.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.03125 = fieldNorm(doc=701)
  0.6666667 = coord(2/3)
    Abstract
    By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.
    Content
    Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.
  12. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.06 
    0.060253844 = product of:
  0.090380765 = sum of:
    0.06890346 = product of:
      0.20671037 = sum of:
        0.20671037 = weight(_text_:3a in 76) [ClassicSimilarity], result of:
          0.20671037 = score(doc=76,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.46834838 = fieldWeight in 76, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=76)
      0.33333334 = coord(1/3)
    0.021477304 = weight(_text_:the in 76) [ClassicSimilarity], result of:
      0.021477304 = score(doc=76,freq=18.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.26147994 = fieldWeight in 76, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.0390625 = fieldNorm(doc=76)
  0.6666667 = coord(2/3)
    Abstract
    A summary of brain theory is given so far as it is contained within the framework of Localization Theory. Difficulties of this "conventional theory" are traced back to a specific deficiency: there is no way to express relations between active cells (as for instance their representing parts of the same object). A new theory is proposed to cure this deficiency. It introduces a new kind of dynamical control, termed synaptic modulation, according to which synapses switch between a conducting and a non- conducting state. The dynamics of this variable is controlled on a fast time scale by correlations in the temporal fine structure of cellular signals. Furthermore, conventional synaptic plasticity is replaced by a refined version. Synaptic modulation and plasticity form the basis for short-term and long-term memory, respectively. Signal correlations, shaped by the variable network, express structure and relationships within objects. In particular, the figure-ground problem may be solved in this way. Synaptic modulation introduces exibility into cerebral networks which is necessary to solve the invariance problem. Since momentarily useless connections are deactivated, interference between di erent memory traces can be reduced, and memory capacity increased, in comparison with conventional associative memory
    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  13. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.05 
    0.054245643 = product of:
  0.08136846 = sum of:
    0.055122763 = product of:
      0.16536829 = sum of:
        0.16536829 = weight(_text_:3a in 5820) [ClassicSimilarity], result of:
          0.16536829 = score(doc=5820,freq=2.0), product of:
            0.44136027 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.052059412 = queryNorm
            0.3746787 = fieldWeight in 5820, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.03125 = fieldNorm(doc=5820)
      0.33333334 = coord(1/3)
    0.026245698 = weight(_text_:the in 5820) [ClassicSimilarity], result of:
      0.026245698 = score(doc=5820,freq=42.0), product of:
        0.08213748 = queryWeight, product of:
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.052059412 = queryNorm
        0.31953377 = fieldWeight in 5820, product of:
          6.4807405 = tf(freq=42.0), with freq of:
            42.0 = termFreq=42.0
          1.5777643 = idf(docFreq=24812, maxDocs=44218)
          0.03125 = fieldNorm(doc=5820)
  0.6666667 = coord(2/3)
    Abstract
    The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
    This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.
    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

Authors

Years

Languages

Types

Themes