Dynamic Taxonomies for Intelligent Information Access

Dynamic Taxonomies for Intelligent Information Access

Giovanni M. Sacco (Università di Torino, Italy)
DOI: 10.4018/978-1-60566-026-4.ch191
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Abstract

End-user interactive access to complex information is a key requirement in most applications, from knowledge management, to e-commerce, to portals. Traditionally, only access paradigms based on the retrieval of data on the basis of precise specifications have been supported. Examples include queries on structured databases and information retrieval. There is now a growing perception that this type of paradigm does not model a large number of search tasks, such as product selection in e-commerce sites among many others, that are imprecise and require exploration, weighting of alternatives and information thinning. The recent debate on findability (Morville, 2002) and the widespread feeling that “search does not work” and “information is too hard to find” shows evidence of the crisis of traditional access paradigms. New access paradigms supporting exploration are needed. Because the goal is end-user interactive access, a holistic approach in which modeling, interface and interaction issues are considered together, must be used and will be discussed in the following.
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Dynamic Taxonomies

Dynamic taxonomies (Sacco, 1987, 2000), also called faceted search systems, are a general knowledge management model based on a multidimensional classification of heterogeneous data items and are used to explore/browse complex information bases in a guided yet unconstrained way through a visual interface.

The intension of a dynamic taxonomy is a taxonomy designed by an expert. This taxonomy is a concept hierarchy going from the most general to the most specific concepts. A dynamic taxonomy does not require any other relationships in addition to subsumptions (e.g., IS-A and PART-OF relationships). Directed acyclic graph taxonomies modeling multiple inheritance are supported but rarely required.

Key Terms in this Chapter

Taxonomy, multidimensional: Taxonomy where an item can be classified under several concepts

Extension, deep: Of a concept C, denotes the shallow extension of C union the deep extension of C’s sons.

Subsumption: A subsumes B if the set denoted by B is a subset of the set denoted by A ()

Extension, shallow: Of a concept C, denotes the set of documents classified directly under C.

Taxonomy, Monodimensional: Taxonomy where an item can be classified under a single concept only

Extensional Inference Rule: Two concepts A and B are related if there is at least one item d in the knowledge base which is classified at the same time under A (or under one of A’s descendants) and under B (or under one of B’s descendants).

Taxonomy, reduced: In a dynamic taxonomy, a taxonomy, describing the current user focus set F, which is derived from the original taxonomy by pruning from it all the concepts not related to F.

Zoom: A user interface operation, that defines a new user focus by OR’ing user-selected concepts and AND’ing them with the previous focus; a reduced taxonomy is then computed and shown to the user.

Facet: One of several top level (most general) concepts in a multidimensional taxonomy. In general, facets are independent and define a set of “orthogonal” conceptual coordinates.

User Focus: The set of documents corresponding to a user-defined composition of concepts; initially, the entire knowledge base.

Taxonomy: A hierarchical organization of concepts going from the most general (topmost) to the most specific concepts. A taxonomy supports abstraction and models subsumption (IS-A and/or PART-OF) relations between a concept and its father. Tree taxonomies can be extended to support multiple inheritance (i.e., a concept having several fathers).

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