The Rich News system, that can automatically annotate radio and television news with the aid of resources retrieved from the World Wide Web, is described. Automatic speech recognition gives a temporally precise but conceptually inaccurate annotation model. Information extraction from related web news sites gives the opposite: conceptual accuracy but no temporal data. Our approach combines the two for temporally accurate conceptual semantic annotation of broadcast news. First low quality transcripts of the broadcasts are produced using speech recognition, and these are then automatically divided into sections corresponding to individual news stories. A key phrases extraction component finds key phrases for each story and uses these to search for web pages reporting the same event. The text and meta-data of the web pages is then used to create index documents for the stories in the original broadcasts, which are semantically annotated using the KIM knowledge management platform. A web interface then allows conceptual search and browsing of news stories, and playing of the parts of the media files corresponding to each news story. The use of material from the World Wide Web allows much higher quality textual descriptions and semantic annotations to be produced than would have been possible using the ASR transcript directly. The semantic annotations can form a part of the Semantic Web, and an evaluation shows that the system operates with high precision, and with a moderate level of recall.

The KIM platform provides a novel Knowledge and Information Management framework and services for automatic semantic annotation, indexing, and retrieval of documents. It provides a mature and semantically enabled infrastructure for scalable and customizable information extraction (IE) as well as annotation and document management, based on GATE.General Architecture for Text Engineering (GATE) (http://gate.ac.uk), leading NLP and IE platform developed at the University of Sheffield. Our understanding is that a system for semantic annotation should be based upon a simple model of real-world entity concepts, complemented with quasi-exhaustive instance knowledge. To ensure efficiency, easy sharing, and reusability of the metadata we introduce an upper-level ontology. Based on the ontology, a large-scale instance base of entity descriptions is maintained. The knowledge resources involved are handled by use of state-of-the-art Semantic Web technology and standards, including RDF(S) repositories, ontology middleware and reasoning. From a technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, for content retrieval based on semantic queries, and for semantic repository access. As a framework, KIM also allows various IE modules, semantic repositories and information retrieval engines to be plugged into it. This paper presents the KIM platform, with an emphasis on its architecture, interfaces, front-ends, and other technical issues.