Topic Map: An Ontology Framework for Information Retrieval

TL;DR

This paper examines the topic map standard as an ontology framework that enhances information retrieval by representing complex relationships between concepts and resources, comparing it with traditional classification techniques.

Contribution

It provides an analysis of how topic maps integrate traditional classification methods and discusses their advantages and disadvantages in various information retrieval contexts.

Findings

Topic maps effectively model complex relationships between concepts and resources.

They offer advantages in content management and knowledge representation.

Limitations include complexity and potential challenges in constraint specification.

Abstract

The basic classification techniques for organizing information are thesauri, taxonomy and faceted classification. Topic map is relatively a new entrant to this information space. Topic map standard describes how complex relationships between abstract concepts and real world resources can be represented using XML syntax. This paper explores how topic map incorporates the traditional techniques and what are its advantages and disadvantages in several dimensions such as content management, indexing, knowledge representation, constraint specification and query languages in the context of information retrieval. The constructs of topic maps are illustrated with a use-case implemented in XTM