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  1. 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
  2. Wilson, T.: ¬The strict faceted classification model (2006) 0.03
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    Abstract
    Faceted classification, at its core, implies orthogonality - that every facet axis exists at right angles to (i.e., independently of) every other facet axis. That's why a faceted classification is sometimes represented with a chart. This set of desserts has been classified by their confection types and, orthogonally, by their flavors.
  3. Quick Guide to Publishing a Classification Scheme on the Semantic Web (2008) 0.02
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    Abstract
    This document describes in brief how to express the content and structure of a classification scheme, and metadata about a classification scheme, in RDF using the SKOS vocabulary. 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. Publishing classifications schemes in SKOS will unify the great many of existing classification efforts in the framework of the Semantic Web.
  4. Giunchiglia, F.; Zaihrayeu, I.; Farazi, F.: Converting classifications into OWL ontologies (2009) 0.02
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    Abstract
    Classification schemes, such as the DMoZ web directory, provide a convenient and intuitive way for humans to access classified contents. While being easy to be dealt with for humans, classification schemes remain hard to be reasoned about by automated software agents. Among other things, this hardness is conditioned by the ambiguous na- ture of the natural language used to describe classification categories. In this paper we describe how classification schemes can be converted into OWL ontologies, thus enabling reasoning on them by Semantic Web applications. The proposed solution is based on a two phase approach in which category names are first encoded in a concept language and then, together with the structure of the classification scheme, are converted into an OWL ontology. We demonstrate the practical applicability of our approach by showing how the results of reasoning on these OWL ontologies can help improve the organization and use of web directories.
  5. Putkey, T.: Using SKOS to express faceted classification on the Semantic Web (2011) 0.02
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    Abstract
    This paper looks at Simple Knowledge Organization System (SKOS) to investigate how a faceted classification can be expressed in RDF and shared on the Semantic Web. Statement of the Problem Faceted classification outlines facets as well as subfacets and facet values. Hierarchical relationships and associative relationships are established in a faceted classification. RDF is used to describe how a specific URI has a relationship to a facet value. Not only does RDF decompose "information into pieces," but by incorporating facet values RDF also given the URI the hierarchical and associative relationships expressed in the faceted classification. Combining faceted classification and RDF creates more knowledge than if the two stood alone. An application understands the subjectpredicate-object relationship in RDF and can display hierarchical and associative relationships based on the object (facet) value. This paper continues to investigate if the above idea is indeed useful, used, and applicable. If so, how can a faceted classification be expressed in RDF? What would this expression look like? Literature Review This paper used the same articles as the paper A Survey of Faceted Classification: History, Uses, Drawbacks and the Semantic Web (Putkey, 2010). In that paper, appropriate resources were discovered by searching in various databases for "faceted classification" and "faceted search," either in the descriptor or title fields. Citations were also followed to find more articles as well as searching the Internet for the same terms. To retrieve the documents about RDF, searches combined "faceted classification" and "RDF, " looking for these words in either the descriptor or title.
  6. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.02
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    Date
    13.12.2017 14:17:22
  7. Tudhope, D.; Hodge, G.: Terminology registries (2007) 0.02
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    Date
    26.12.2011 13:22:07
  8. Panzer, M.: Towards the "webification" of controlled subject vocabulary : a case study involving the Dewey Decimal Classification (2007) 0.01
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    Abstract
    The presentation will briefly introduce a series of major principles for bringing subject terminology to the network level. A closer look at one KOS in particular, the Dewey Decimal Classification, should help to gain more insight into the perceived difficulties and potential benefits of building taxonomy services out and on top of classic large-scale vocabularies or taxonomies.
  9. OWL Web Ontology Language Test Cases (2004) 0.01
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    Date
    14. 8.2011 13:33:22
  10. Hauff-Hartig, S.: Wissensrepräsentation durch RDF: Drei angewandte Forschungsbeispiele : Bitte recht vielfältig: Wie Wissensgraphen, Disco und FaBiO Struktur in Mangas und die Humanities bringen (2021) 0.01
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    Date
    22. 5.2021 12:43:05
  11. Wang, Y.-H.; Jhuo, P.-S.: ¬A semantic faceted search with rule-based inference (2009) 0.01
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    Abstract
    Semantic Search has become an active research of Semantic Web in recent years. The classification methodology plays a pretty critical role in the beginning of search process to disambiguate irrelevant information. However, the applications related to Folksonomy suffer from many obstacles. This study attempts to eliminate the problems resulted from Folksonomy using existing semantic technology. We also focus on how to effectively integrate heterogeneous ontologies over the Internet to acquire the integrity of domain knowledge. A faceted logic layer is abstracted in order to strengthen category framework and organize existing available ontologies according to a series of steps based on the methodology of faceted classification and ontology construction. The result showed that our approach can facilitate the integration of inconsistent or even heterogeneous ontologies. This paper also generalizes the principles of picking appropriate facets with which our facet browser completely complies so that better semantic search result can be obtained.
  12. Knorz, G.; Rein, B.: Semantische Suche in einer Hochschulontologie : Ontologie-basiertes Information-Filtering und -Retrieval mit relationalen Datenbanken (2005) 0.01
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    Date
    11. 2.2011 18:22:25
  13. Mayfield, J.; Finin, T.: Information retrieval on the Semantic Web : integrating inference and retrieval 0.01
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    Date
    12. 2.2011 17:35:22
  14. Priss, U.: Faceted knowledge representation (1999) 0.01
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    Date
    22. 1.2016 17:30:31
  15. Definition of the CIDOC Conceptual Reference Model (2003) 0.01
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    Date
    6. 8.2010 14:22:28
  16. Hollink, L.; Assem, M. van: Estimating the relevance of search results in the Culture-Web : a study of semantic distance measures (2010) 0.01
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    Date
    26.12.2011 13:40:22
  17. Bittner, T.; Donnelly, M.; Winter, S.: Ontology and semantic interoperability (2006) 0.01
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    Date
    3.12.2016 18:39:22
  18. Beppler, F.D.; Fonseca, F.T.; Pacheco, R.C.S.: Hermeneus: an architecture for an ontology-enabled information retrieval (2008) 0.01
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    Date
    28.11.2016 12:43:22
  19. Frické, M.: Logical division (2016) 0.01
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    Abstract
    Division is obviously important to Knowledge Organization. Typically, an organizational infrastructure might acknowledge three types of connecting relationships: class hierarchies, where some classes are subclasses of others, partitive hierarchies, where some items are parts of others, and instantiation, where some items are members of some classes (see Z39.19 ANSI/NISO 2005 as an example). The first two of these involve division (the third, instantiation, does not involve division). Logical division would usually be a part of hierarchical classification systems, which, in turn, are central to shelving in libraries, to subject classification schemes, to controlled vocabularies, and to thesauri. Partitive hierarchies, and partitive division, are often essential to controlled vocabularies, thesauri, and subject tagging systems. Partitive hierarchies also relate to the bearers of information; for example, a journal would typically have its component articles as parts and, in turn, they might have sections as their parts, and, of course, components might be arrived at by partitive division (see Tillett 2009 as an illustration). Finally, verbal division, disambiguating homographs, is basic to controlled vocabularies. Thus Division is a broad and relevant topic. This article, though, is going to focus on Logical Division.
  20. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.01
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    Date
    16.11.2018 14:22:01