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  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.29
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    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
  2. Mas, S.; Marleau, Y.: Proposition of a faceted classification model to support corporate information organization and digital records management (2009) 0.25
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    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.
  3. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.25
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  4. 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) 0.24
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    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.
  5. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.20
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  6. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.20
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    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.
  7. Li, L.; Shang, Y.; Zhang, W.: Improvement of HITS-based algorithms on Web documents 0.20
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    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/.
  8. Vetere, G.; Lenzerini, M.: Models for semantic interoperability in service-oriented architectures (2005) 0.17
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    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.
  9. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.15
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  10. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.15
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    Abstract
    In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization.
    Content
    A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
    Date
    10. 1.2013 19:22:47
  11. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.13
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    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.
  12. FID/CR news; 38 (1995) 0.13
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    Content
    DDC - LCC - IFLAS Section on Classification and Indexing - ALA/ALCTS Subject Analysis Committee
    Source
    Knowledge organization. 22(1995) no.1, S.41-42
  13. Malsburg, C. von der: ¬The correlation theory of brain function (1981) 0.12
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    Source
    http%3A%2F%2Fcogprints.org%2F1380%2F1%2FvdM_correlation.pdf&usg=AOvVaw0g7DvZbQPb2U7dYb49b9v_
  14. Mikacic, M.: Statistical system for subject designation (SSSD) for libraries in Croatia (1996) 0.12
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    Abstract
    Describes the developments of the Statistical System for Subject Designation (SSSD): a syntactical system for subject designation for libraries in Croatia, based on the construction of subject headings in agreement with the theory of the sentence nature of subject headings. The discussion is preceded by a brief summary of theories underlying basic principles and fundamental rules of the alphabetical subject catalogue
    Date
    31. 7.2006 14:22:21
    Source
    Cataloging and classification quarterly. 22(1996) no.1, S.77-93
  15. Connaway, L.S.; Sievert, M.C.: Comparison of three classification systems for information on health insurance (1996) 0.12
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    Abstract
    Reports results of a comparative study of 3 classification schemes: LCC, DDC and NLM Classification to determine their effectiveness in classifying materials on health insurance. Examined 2 hypotheses: that there would be no differences in the scatter of the 3 classification schemes; and that there would be overlap between all 3 schemes but no difference in the classes into which the subject was placed. There was subject scatter in all 3 classification schemes and litlle overlap between the 3 systems
    Date
    22. 4.1997 21:10:19
    Object
    NLM Classification
    Source
    Cataloging and classification quarterly. 23(1996) no.2, S.89-104
  16. Neely, T.Y.: Using subject terminology & classification to provide effective reference service to diverse populations (1995) 0.12
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    Abstract
    Discusses the current classification system and historical treatment of subject matter relating to African American subjects. Gives examples of carrying out searches on CARL catalogues, INFOTRAC, the Reader's Guide to Periodical Literature, and Education Index
    Source
    Colorado libraries. 21(1995) no.2, S.22-26
  17. Langridge, D.W.: Classification: its kinds, systems, elements and application (1992) 0.11
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    COMPASS
    Subject classification
    Date
    26. 7.2002 14:01:22
    Footnote
    Rez. in: Journal of documentation 49(1993) no.1, S.68-70. (A. Maltby); Journal of librarianship and information science 1993, S.108-109 (A.G. Curwen); Herald of library science 33(1994) nos.1/2, S.85 (P.N. Kaula); Knowledge organization 22(1995) no.1, S.45 (M.P. Satija)
    Subject
    Subject classification
  18. Chan, L.M.: Library of Congress Subject Headings : principles and application (1995) 0.11
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    Date
    25.11.2005 18:37:22
    Footnote
    Rez. in: Cataloging and classification quarterly 23(1996) no.1, S.127-130 (L. Kuyper-Rushing)
  19. Barden, B.R.; Denison, B.: Guide to the SLA loan collection of classification schemes and subject heading lists on deposit at Western Reserve Univ. as of March 20, 1961 (1961) 0.11
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    Footnote
    Rez. in: College and research libraries 22(1961) no.6, S.483-484 (A.R. Lindsay)
  20. Lim, E.: Southeast Asian subject gateways : an examination of their classification practices (2000) 0.11
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    Date
    22. 6.2002 19:42:47

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  • x 14
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  • ag 2
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