##Abstract One of the important purposes of the semantic web is to make the available resources clearly organized, structured and machine-identifiable. This facilitates accessing, understanding and linking large repositories of resources, which can be used in a variety of useful applications; moreover, it allows software agents to process and analyse the data which will likely result in the emergence of new useful information. In this thesis, an application has been developed to enrich the semantic web with machine-readable description of scientific publications related to the field of computing. This system mainly applies hierarchical classification techniques based on the local classifiers approach to categorize given documents according to the ACM classification taxonomy. The data used in this system were constructed from the data available in the ACM digital library. The approach of collecting these multi-labelled corpuses has been thoroughly discussed. Evaluating the approaches used in this work shown an improvement over previous work that classifies similar data. The thesis ends by offering suggestions likely to improve the categorization process of such data.