While following some references I ran across: A proposal for transformation of topic-maps into similarities of topics (pdf) by Dr. Dominik Kuropka.
Abstract:
Newer information filtering and retrieval models like the Fuzzy Set Model or the Topic-based Vector Space Model consider term dependencies by means of numerical similarities between two terms. This leads to the question from what and how these numerical values can be deduced? This paper proposes an algorithm for the transformation of topic-maps into numerical similarities of paired topics. Further the relation of this work towards the above named information filtering and retrieval models is discussed.
Based in part on his paper Topic-Based Vector Space (2003).
This usage differs from ours in part because the work is designed to work at the document level in a traditional IR type context. “Topic maps,” in the ISO sense, are not limited to retrieval of documents or comparison by a particular method, however useful that method may be.
Still, it is good to get to know one’s neighbours so I will be sending him a note about our efforts.