Methodology For Creating a Sample Subset of Dynamic Taxonomy to Use in Navigating Medical Text Databases

these is that human memory is better at recognising items than remembering them without cues [1]. One way to increase recall in a text browsing environment is to increase the number of ways to gain access to a certain bit of data, in other words, to increase the number of entry points into a text. Examples of this include deepening the detail of a table of contents, or increasing the number of index entries. The latter could be readily accomplished by marrying existing index terms with entries from a taxonomy. The problem with this strategy is that the utility of browsing as a strategy declines as the number of terms increase, unless there are subsequent ways of ordering or reducing the term set. Sacco uses dynamic taxonomy (DT) [2] as a solution to this problem of term overload. Firstly, DT provides standard taxonomic ordering of the index term set, allowing complexity to be hidden under higher level terms. Secondly, it provides a zoom operator, which can be used to reduce the term set. The zoom operation functions in the following manner. In response to a request to zoom in on a term, the system dynamically prunes the taxonomy, retaining only the taxonomic elements which categorise the set of text atoms which are also categorised by the zoom term. For example, figure 1 shows a sample taxonomy derived from the medical field. Figure 2 shows the pruned taxonomy after a zoom on the concept amoxycillin. In this example, if a text atom is not classified by amoxycillin, it is not included in the pruned taxonomy. More importantly, any terms in the taxonomy that do not either directly or indirectly classify the remaining text atoms are also pruned. Original DT retrieved from a database of discrete newspaper articles. The present paper describes work based on a medical therapy encyclopaedia, retrieving small chunks of text from a continuous stream of highly interrelated content. Because of complexity and interest, much text processing work has been done in the field of medical text. There are many medical term sets. Where Sacco used a hand crafted purpose-built taxonomy, our taxonomic source Abstract The amount of text available in electronic form is increasing , especially since the rise of the web. So too are the potential interconnections between concepts, given the advent of ontologies and other relationship based data sources. Text could be navigated using the structure …