Knowledge management has become the hot new buzzword. Standards such as topic maps and Resource Description Framework (RDF) have been created to allow knowledge to be managed and interchanged. In all the hype surrounding topic maps, one of the oft- mentioned applications is the ability to create community-defined taxonomies and ontologies. Taxonomy is a fancy word meaning, "the ordering of things into groups or categories." Ontology is a description of a set of concepts and the relationships that can exist between those concepts. Topic maps provide much of what is needed to define a fairly robust taxonomy. However, there are still capabilities that are necessary to build robust ontologies that are not part of the topic map standard.

Over the past year or so, the World Wide Web consortium (W3C) and DARPA have been working to create a framework to model information ontologies contained on the Web. The result of that work is known as DARPA Agent Markup Language + Ontology Interface Layer (DAML+OIL). DAML+OIL provides a rich set of constructs, using RDF, to create ontologies and mark up information to be machine readable and processable. Some of the constructs are much more powerful than what is currently enabled by the topic map model. These include:

The topic map standard provides several features that RDF cannot match, especially in its association model and the ability to define scopes for information (even though there is still discussion about how scope should really work). While certain inferences about how objects are related can be derived from a topic map, DAML+OIL has extended RDF to do things topic maps can't. The constructs mentioned above would be very useful in topic maps to allow intelligent inferences about objects (whether or not they are topics or resources) and to accurately build a knowledge base from a set of information, be it a small corporate document repository or the entire Web.

This paper will demonstrate how DAML+OIL can be used to provide additional capabilities that are currently missing from the topic map model. It will discuss possible additions to the topic map model or its companion standard, Topic Map Constraint Language (TMCL), to enable DAML+OIL to process and enhance topic maps. It will also discuss methods for using DAML+OIL in conjunction with topic maps to take advantage of the best from both worlds.