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  • × author_ss:"Priss, U."
  1. Priss, U.: Faceted information representation (2000) 0.02
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    Date
    22. 1.2016 17:47:06
    Pages
    S.84-94
  2. Priss, U.: Faceted knowledge representation (1999) 0.02
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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. Priss, U.: Description logic and faceted knowledge representation (1999) 0.02
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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
  4. Priss, U.: Lattice-based information retrieval (2000) 0.00
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    Abstract
    A lattice-based model for information retrieval was suggested in the 1960's but has been seen as a theoretical possibility hard to practically apply ever since. This paper attempts to revive the lattice model and demonstrate its applicability in an information retrieval system, FalR, that incorporates a graphical representation of a faceted thesaurus. It shows how Boolean queries can be lattice-theoretically related to the concepts of the thesaurus and visualized within the thesaurus display. An advantage of FaIR is that it allows for a high level of transparency of the system, which can be controlled by the user
    Source
    Knowledge organization. 27(2000) no.3, S.132-142
  5. Priss, U.: Comparing classification systems using facets (2000) 0.00
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    Pages
    S.170-175
  6. Priss, U.: ¬A semiotic-conceptual framework for knowledge representation (2004) 0.00
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    Pages
    S.91-96
  7. Priss, U.: Facet-like structures in computer science (2008) 0.00
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    Source
    Axiomathes. 18(2008) no.2, S.243-255
  8. Priss, U.; Old, L.J.: Concept neighbourhoods in knowledge organisation systems (2010) 0.00
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    Pages
    S.165-170
  9. Jacob, E.K.; Priss, U.: Nontraditional indexing structures for the management of electronic resources (2001) 0.00
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    Pages
    S.73-90
  10. Priss, U.: ¬A graphical interface for conceptually navigating faceted thesauri (1998) 0.00
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    Pages
    S.184-190
  11. Priss, U.: Alternatives to the "Semantic Web" : multi-strategy knowledge representation (2003) 0.00
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    Abstract
    This paper argues that the Semantic Web needs to incorporate both formal and associative structures (and possibly a multitude of other structures and strategies) to be successful. The arguments for this claim are based on an observation of successes and failures in the areas of artificial intelligence (AI) and natural language processing (NLP). 1. Introduction The WWW provides numerous challenges for information and knowledge processing activities. Information may be available but not accessible or retrievable because of language barriers or insufficient search strategies. Data mining techniques may discover implicit information in explicit data but these techniques do not necessarily guarantee that the discovered information is relevant, significant and trustworthy. During the last several decades hundreds or thousands of computer and information scientists have developed probably thousands of natural language processing and artificial intelligence techniques that were aimed at solving problems related to intelligent information processing only to encounter more and more new obstacles along the way. The latest solution, the Semantic Web, appears as an open declaration of defeat: since natural language processing and AI techniques did not provide sufficient results, it is now proposed to put the burden an the shoulder of the authors of webpages who are expected to populate their pages with metadata and additional markup. Metadata is essentially a new form of controlled vocabulary; markup - at least in the form of XML, XSL, etc - is essentially a programming language. Existing studies of the use of controlled vocabularies and indexing practices in information science and studies of teaching programming languages to "everybody" (Python, 2002) have shown that both are difficult and full of unsolved problems. This can further dampen the expectations of the success of the Semantic Web. In contrast to machines and despite numerous inter-cultural conflicts around the world, humans do communicate surprisingly successfully even across national, linguistic and cultural boundaries. The question then arises: why are humans successful at information processing tasks such as information integration, translation and communication, which computers find so difficult? One obvious answer is that human cognition is embodied and grounded in our shared experiences of living in the same world. AI researchers have theoretically explored the idea of symbol grounding in the early 1990's but so far, connectionist artificial agents with perceptual interfaces have not been integrated with a large-scale capability of symbolic representations.
    Pages
    S.305-310
  12. Priss, U.; Jacob, E.: Utilizing faceted structures for information systems design (1999) 0.00
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    Pages
    S.203-212