Orthogonal Range Searching for Text Indexing by Moshe Lewenstein.
Abstract:
Text indexing, the problem in which one desires to preprocess a (usually large) text for future (shorter) queries, has been researched ever since the sux tree was invented in the early 70’s. With textual data continuing to increase and with changes in the way it is accessed, new data structures and new algorithmic methods are continuously required. Therefore, text indexing is of utmost importance and is a very active research domain.
Orthogonal range searching, classically associated with the computational geometry community, is one of the tools that has increasingly become important for various text indexing applications. Initially, in the mid 90’s there were a couple of results recognizing this connection. In the last few years we have seen an increase in use of this method and are reaching a deeper understanding of the range searching uses for text indexing.
From the paper:
Orthogonal range searching refers to the preprocessing of a collection of points in d-dimensional space to allow queries on ranges de ned by rectangles whose sides are aligned with the coordinate axes (orthogonal).
If you are not already familiar with this area, you may find Lecture 11: Orthogonal Range Searching useful.
In a very real sense, indexing, as in a human indexer, lies at the heart of topic maps.
A human indexer recognizes synonyms, relationships represented by synonyms and distinguishes other uses of identifiers.
Topic maps are an effort to record that process so it can be followed mechanically by a calculator.
Mechanical indexing is a powerful tool in the hands of a human indexer, whether working on a traditional index or its successor, a topic map.
What type of mechanical indexing are you using?