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Abstract

Facet analysis is proposed as a general theory of knowledge organization, with an associated methodology that may be applied to the development of terminology tools in a variety of contexts and formats. Faceted classifications originated as a means of representing complexity in semantic content that facilitates logical organization and effective retrieval in a physical environment. This is achieved through meticulous analysis of concepts, their structural and functional status (based on fundamental categories), and their inter-relationships. These features provide an excellent basis for the general conceptual modelling of domains, and for the generation of KOS other than systematic classifications. This is demonstrated by the adoption of a faceted approach to many web search and visualization tools, and by the emergence of a facet based methodology for the construction of thesauri. Current work on the Bliss Bibliographic Classification (Second Edition) is investigating the ways in which the full complexity of faceted structures may be represented through encoded data, capable of generating intellectually and mechanically compatible forms of indexing tools from a single source. It is suggested that a number of research questions relating to the Semantic Web could be tackled through the medium of facet analysis.

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Abstract

The paper traces the development of the discussion around ethical issues in artificial intelligence, and considers the way in which humans have affected the knowledge bases used in machine learning. The phenomenon of bias or discrimination in machine ethics is seen as inherited from humans, either through the use of biased data or through the semantics inherent in intellectually- built tools sourced by intelligent agents. The kind of biases observed in AI are compared with those identified in the field of knowledge organization, using religious adherents as an example of a community potentially marginalized by bias. A practical demonstration is given of apparent religious prejudice inherited from source material in a large database deployed widely in computational linguistics and automatic indexing. Methods to address the problem of bias are discussed, including the modelling of the moral process on neuroscientific understanding of brain function. The question is posed whether it is possible to model religious belief in a similar way, so that robots of the future may have both an ethical and a religious sense and themselves address the problem of prejudice.