In the field of artificial intelligence knowledge classification offers the potential to represent, formalise and model a view of a world. These knowledge frameworks, known as ontologies, are presented using formal logic and inference rules to support complex computer tasks.

The difference between bibliographic knowledge classification schemes and ontologies resides in their particular purpose and levels of formality. However, they are both based on observation and reasoning and share some structural principles and elements: categories, concepts, properties, class relationships, roles.

The processing of knowledge classification automatically by programs is significant whenever there is a need to support intuitive services. Most importantly, ontology-like representations of classifications are recognized as potentially important facilitators in creating a web of linked data (the semantic web).

The objective of this conference is to promote collaboration and exchange of expertise between different fields dealing with knowledge classifications: bibliographic, web and AI. We hope to learn more about methods in ontology modelling and whether these may be used to improve and formalise data models of bibliographic classifications and enhance their value in information discovery.