The internet is a rich source of knowledge that is constantly expanding as users generate content which needs to be organized and classified. Future versions of the web are likely to include intelligent software agents capable of using metadata to understand complex relationships and better support users in discovering new knowledge. Wikipedia is a rich source of knowledge, but with limited and inconsistent structure to its content there has been limited progress in developing agents to use this information. This paper investigates using natural language processing to add metadata to Wikipedia articles, and presents an application where users could use the metadata to discover an alternative destination.

Requirements engineering's continuing dependence on natural language description has made it the focus of several efforts to apply language engineering techniques. The raw textual material that forms an input to early phase requirements engineering and which informs the subsequent formulation of the requirements is inevitably uncontrolled and this makes its processing very hard. Nevertheless, sufficiently robust techniques do exist that can be used to aid the requirements engineer provided that the scope of what can be achieved is understood. In this paper, we show how combinations of lexical and shallow semantic analysis techniques developed from corpus linguistics can help human analysts acquire the deep understanding needed as the first step towards the synthesis of requirements.

The development of large, complex systems poses a number of challenges for systems engineers, not least of which is the ability to ensure user requirements have been satisfied. Effective requirements management - an amalgam of information capture, information storage and management, and information dissemination activities - is crucial in that respect. In this paper we concentrate on one of the core issues of information management in a requirements management context - namely traceability.