Multiple taxonomies
"Why multiple taxonomies? As a first cut, we tried to find a taxonomy that reflects the notions of generalization and specialization within mathematics. In experiments done before the meeting, and in discussions during the meeting, it became clear that disparities in vocabulary usage among different levels of mathematics made a universal taxonomy impractical. The assumptions made by someone searching in the realm of research mathematics and someone searching in the context of a precalculus course are remarkably different, and the same vocabulary means far different things. Not only do words change their meaning as the level of mathematics changes (think of the word "sequence" and what it means to a middle school teacher, in Calculus, in Number Theory, or in homological algebra) but so can the relationships among subjects within mathematics." http://www.mathmetadata.org/ammtf/docs/june99/maintax.htm
According to Morville & Rosenfeld (2006, p. 221-222) is the difference between traditional classification systems and faceted classification that the later is based on the principle of multiple taxonomies.
Literature.
Morville, Peter & Rosenfeld, Louis (2006). Information architecture for the World Wide Web. Third edition. Sebastopol, CA: O'Reilly Media, Inc.
Spangler, S.; Kreulen, J. T.; Lessler, J. (2002).
MindMap: utilizing multiple taxonomies and
visualization to understand a document collection. System Sciences,
2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on,
7-10 Jan. 2002, 4(4), 1170-1179.
http://csdl2.computer.org/comp/proceedings/hicss/2002/1435/04/14350102.pdf
Summary:
We present a novel system and methodology for browsing and exploring topics and
concepts within a document collection. The process begins with the generation of
multiple taxonomies from the document collections, each having a unique theme.
We have developed the MindMap interface to the document collection. Starting
from an initial keyword query, the MindMap interface helps the user to explore
the concept space by first presenting the user with related terms and high level
topics in a radial graph. After refining the query by selecting any related
terms, one of the related high level concepts can be selected for further
investigation. The MindMap uses a novel binary tree interface to explore the
composition of a concept based on the presence or absence of terms. From the
binary tree a concept can be further explored and visualized. Individual
documents are presented as spatial coordinates where distance between points
relates to document similarity. As the user browses this spatial representation,
text is presented from the document that is most relevant to the user's initial
query. Individual points can be selected to pull up the relevant paragraphs from
the document with the keywords highlighted. Finally, selected documents are
displayed and the user is allowed to further interact and investigate.
Birger Hjørland
Last edited: 13-05-2007