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Priss, U.: Description logic and faceted knowledge representation (1999) 0.07
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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
Priss, U.: Alternatives to the "Semantic Web" : multi-strategy knowledge representation (2003) 0.02
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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.

Priss, U.: Faceted information representation (2000) 0.01

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Date: 22. 1.2016 17:47:06

Priss, U.: Faceted knowledge representation (1999) 0.01

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Date: 22. 1.2016 17:30:31

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