Search (31 results, page 1 of 2)

  • × theme_ss:"Wissensrepräsentation"
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  1. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.08 
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    Date
    26.12.2011 13:22:07
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  2. Priss, U.: Faceted knowledge representation (1999) 0.05 
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    Abstract
    Faceted Knowledge Representation provides a formalism for implementing knowledge systems. The basic notions of faceted knowledge representation are "unit", "relation", "facet" and "interpretation". Units are atomic elements and can be abstract elements or refer to external objects in an application. Relations are sequences or matrices of 0 and 1's (binary matrices). Facets are relational structures that combine units and relations. Each facet represents an aspect or viewpoint of a knowledge system. Interpretations are mappings that can be used to translate between different representations. This paper introduces the basic notions of faceted knowledge representation. The formalism is applied here to an abstract modeling of a faceted thesaurus as used in information retrieval.
    Date
    22. 1.2016 17:30:31
  3. Dextre Clarke, S.G.; Will, L.D.; Cochard, N.: ¬The BS8723 thesaurus data model and exchange format, and its relationship to SKOS (2008) 0.05 
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  4. Priss, U.: Description logic and faceted knowledge representation (1999) 0.05 
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    Abstract
    The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.
    Date
    22. 1.2016 17:30:31
  5. Quick Guide to Publishing a Thesaurus on the Semantic Web (2008) 0.04 
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    Abstract
    This document describes in brief how to express the content and structure of a thesaurus, and metadata about a thesaurus, in RDF. Using RDF allows data to be linked to and/or merged with other RDF data by semantic web applications. The Semantic Web, which is based on the Resource Description Framework (RDF), provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
    Source
    http://www.w3.org/TR/2005/WD-swbp-thesaurus-pubguide-20050517/
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  6. Will, L.D.: UML model : as given in British Standard Draft for Development DD8723-5:2008 (2008) 0.03 
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  7. Gladun, A.; Rogushina, J.: Development of domain thesaurus as a set of ontology concepts with use of semantic similarity and elements of combinatorial optimization (2021) 0.03 
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    Abstract
    We consider use of ontological background knowledge in intelligent information systems and analyze directions of their reduction in compliance with specifics of particular user task. Such reduction is aimed at simplification of knowledge processing without loss of significant information. We propose methods of generation of task thesauri based on domain ontology that contain such subset of ontological concepts and relations that can be used in task solving. Combinatorial optimization is used for minimization of task thesaurus. In this approach, semantic similarity estimates are used for determination of concept significance for user task. Some practical examples of optimized thesauri application for semantic retrieval and competence analysis demonstrate efficiency of proposed approach.
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  8. Ulrich, W.: Simple Knowledge Organisation System (2007) 0.03 
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    Content
    Semantic Web - Taxonomie und Thesaurus - SKOS - Historie - Klassen und Eigenschaften - Beispiele - Generierung - automatisiert - per Folksonomie - Fazit und Ausblick
  9. Schulz, S.; Schober, D.; Tudose, I.; Stenzhorn, H.: ¬The pitfalls of thesaurus ontologization : the case of the NCI thesaurus (2010) 0.03 
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    Abstract
    Thesauri that are "ontologized" into OWL-DL semantics are highly amenable to modeling errors resulting from falsely interpreting existential restrictions. We investigated the OWL-DL representation of the NCI Thesaurus (NCIT) in order to assess the correctness of existential restrictions. A random sample of 354 axioms using the someValuesFrom operator was taken. According to a rating performed by two domain experts, roughly half of these examples, and in consequence more than 76,000 axioms in the OWL-DL version, make incorrect assertions if interpreted according to description logics semantics. These axioms therefore constitute a huge source for unintended models, rendering most logic-based reasoning unreliable. After identifying typical error patterns we discuss some possible improvements. Our recommendation is to either amend the problematic axioms in the OWL-DL formalization or to consider some less strict representational format.
    Object
    NCI Thesaurus
  10. Fischer, D.H.: Converting a thesaurus to OWL : Notes on the paper "The National Cancer Institute's Thesaurus and Ontology" (2004) 0.03 
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    Abstract
    The paper analysed here is a kind of position paper. In order to get a better under-standing of the reported work I used the retrieval interface of the thesaurus, the so-called NCI DTS Browser accessible via the Web3, and I perused the cited OWL file4 with numerous "Find" and "Find next" string searches. In addition the file was im-ported into Protégé 2000, Release 2.0, with OWL Plugin 1.0 and Racer Plugin 1.7.14. At the end of the paper's introduction the authors say: "In the following sections, this paper will describe the terminology development process at NCI, and the issues associated with converting a description logic based nomenclature to a semantically rich OWL ontology." While I will not deal with the first part, i.e. the terminology development process at NCI, I do not see the thesaurus as a description logic based nomenclature, or its cur-rent state and conversion already result in a "rich" OWL ontology. What does "rich" mean here? According to my view there is a great quantity of concepts and links but a very poor description logic structure which enables inferences. And what does the fol-lowing really mean, which is said a few lines previously: "Although editors have defined a number of named ontologic relations to support the description-logic based structure of the Thesaurus, additional relation-ships are considered for inclusion as required to support dependent applications."
    According to my findings several relations available in the thesaurus query interface as "roles", are not used, i.e. there are not yet any assertions with them. And those which are used do not contribute to complete concept definitions of concepts which represent thesaurus main entries. In other words: The authors claim to already have a "description logic based nomenclature", where there is not yet one which deserves that title by being much more than a thesaurus with strict subsumption and additional inheritable semantic links. In the last section of the paper the authors say: "The most time consuming process in this conversion was making a careful analysis of the Thesaurus to understand the best way to translate it into OWL." "For other conversions, these same types of distinctions and decisions must be made. The expressive power of a proprietary encoding can vary widely from that in OWL or RDF. Understanding the original semantics and engineering a solution that most closely duplicates it is critical for creating a useful and accu-rate ontology." My question is: What decisions were made and are they exemplary, can they be rec-ommended as "the best way"? I raise strong doubts with respect to that, and I miss more profound discussions of the issues at stake. The following notes are dedicated to a critical description and assessment of the results of that conversion activity. They are written in a tutorial style more or less addressing students, but myself being a learner especially in the field of medical knowledge representation I do not speak "ex cathedra".
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  11. Bold, N.; Kim, W.-J.; Yang, J.-D.: Converting object-based thesauri into XML Topic Maps (2010) 0.02 
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    Abstract
    Constructing ontology is considerably time consuming process in general. Since there are a vast amount of thesauri currently available, it may be a feasible solution to exploit thesauri, when constructing ontology in a short period of time. This paper designs and implements a XTM (XML Topic Maps) code converter generating XTM coded ontology from an object based thesaurus. It is an extended thesaurus, which enriches the conventional thesauri with user defined associations, a notion of instances and occurrences associated with them. The reason we adopt XTM is that it is a verified and practical methodology to semantically reorganize the conceptual structure of extant web applications with minimal effort. Moreover, since XTM is conceptually similar to our object based thesauri, recommendation and inference mechanism already developed in our system could be easily applied to the generated XTM ontology. To show that the XTM ontology is correct, we also verify it with onto pia Omnigator and Vizigator, the components of Ontopia Knowledge Suite (OKS) tool.
  12. Assem, M. van; Menken, M.R.; Schreiber, G.; Wielemaker, J.; Wielinga, B.: ¬A method for converting thesauri to RDF/OWL (2004) 0.02 
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  13. Bandholtz, T.; Schulte-Coerne, T.; Glaser, R.; Fock, J.; Keller, T.: iQvoc - open source SKOS(XL) maintenance and publishing tool (2010) 0.02 
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  14. Networked Knowledge Organisation Systems and Services - TPDL 2011 : The 10th European Networked Knowledge Organisation Systems (NKOS) Workshop (2011) 0.01 
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    Content
    Programm mit Links auf die Präsentationen: Armando Stellato, Ahsan Morshed, Gudrun Johannsen, Yves Jacques, Caterina Caracciolo, Sachit Rajbhandari, Imma Subirats, Johannes Keizer: A Collaborative Framework for Managing and Publishing KOS - Christian Mader, Bernhard Haslhofer: Quality Criteria for Controlled Web Vocabularies - Ahsan Morshed, Benjamin Zapilko, Gudrun Johannsen, Philipp Mayr, Johannes Keizer: Evaluating approaches to automatically match thesauri from different domains for Linked Open Data - Johan De Smedt: SKOS extensions to cover mapping requirements - Mark Tomko: Translating biological data sets Into Linked Data - Daniel Kless: Ontologies and thesauri - similarities and differences - Antoine Isaac, Jacco van Ossenbruggen: Europeana and semantic alignment of vocabularies - Douglas Tudhope: Complementary use of ontologies and (other) KOS - Wilko van Hoek, Brigitte Mathiak, Philipp Mayr, Sascha Schüller: Comparing the accuracy of the semantic similarity provided by the Normalized Google Distance (NGD) and the Search Term Recommender (STR) - Denise Bedford: Selecting and Weighting Semantically Discovered Concepts as Social Tags - Stella Dextre Clarke, Johan De Smedt. ISO 25964-1: a new standard for development of thesauri and exchange of thesaurus data
  15. Wielinga, B.; Wielemaker, J.; Schreiber, G.; Assem, M. van: Methods for porting resources to the Semantic Web (2004) 0.01 
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    Object
    Art and architecture thesaurus
  16. Assem, M. van; Gangemi, A.; Schreiber, G.: Conversion of WordNet to a standard RDF/OWL representation (2006) 0.01 
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  17. Assem, M. van; Malaisé, V.; Miles, A.; Schreiber, G.: ¬A method to convert thesauri to SKOS (2006) 0.01 
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    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  18. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.01 
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    Date
    13.12.2017 14:17:22
  19. Assem, M. van: Converting and integrating vocabularies for the Semantic Web (2010) 0.01 
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    Object
    Art and architecture thesaurus
    Theme
    Konzeption und Anwendung des Prinzips Thesaurus
  20. Putkey, T.: Using SKOS to express faceted classification on the Semantic Web (2011) 0.01 
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    Abstract
    Methodology Based on information from research papers, more research was done on SKOS and examples of SKOS and shared faceted classifications in the Semantic Web and about SKOS and how to express SKOS in RDF/XML. Once confident with these ideas, the author used a faceted taxonomy created in a Vocabulary Design class and encoded it using SKOS. Instead of writing RDF in a program such as Notepad, a thesaurus tool was used to create the taxonomy according to SKOS standards and then export the thesaurus in RDF/XML format. These processes and tools are then analyzed. Results The initial statement of the problem was simply an extension of the survey paper done earlier in this class. To continue on with the research, more research was done into SKOS - a standard for expressing thesauri, taxonomies and faceted classifications so they can be shared on the semantic web.

Authors

Years

Languages

  • e 25
  • d 4

Types

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