Collecting and aggregating multimedia knowledge is of fundamental importance for every organisation in order to gain competitiveness and to reduce costs. It is possible that knowledge contained in just one medium E.G. text documents, does not carry the full evidence looked for. Therefore connecting information stored in more than one medium is often required. It is clear that current knowledge management technologies and practises cannot cope with such situations, as they mainly provide simple mechanisms (E.G. keyword searching). Currently knowledge workers manually pierce together the information from different sources. In this report we focus and envisage research methodologies that will enable the semantic enrichment of multimedia documents, both on multiple media and across media through annotation. Annotation of a document is a complex and labour intensive task. So far, research has focused [1][2][4] on supporting the annotation of single media. Much less attention has been paid to the issue of annotating material across media. For this reason there is a growing interest in developing methodologies able to capture the content and the context of multimedia documents, in order to enable effective searching (and document-based knowledge management in general). Previous research in personal image management [6] and text annotation [3] demonstrated how annotating images or documents could be a way to organise information and transform it into knowledge that can be used easily later. Metadata enables the creation of a knowledge base which can then be queried as a way both to retrieve documents (via content and context) and to query the structured data (E.G. creating charts illustrating trends). We address many of these problems with AKTive Media1 which is a system implemented during the PhD. AKTive Media is a user centric ontology based cross-media annotation system. The goal is to automate the process of annotation by means of knowledge sharing and reuse, thereby reducing user effort during the annotation process. The system actively queries web services and central annotational triple stores as a background service to look for context specific knowledge. The aim is to provide a seamless interface that guides the user through the process, reducing the complexity of the task. Language technologies and a web service architecture are adopted to provide a context specific annotation mechanism that uses suggestions inferred from both the ontology and from the previously stored annotations to help the user: the ontology is pre filtered to present only the top-level concepts (the most generic ones); The produced knowledge is then used as a way to establish connections with and to
navigate the information space: Example when the user annotates a part of an image
of a car engine as “abrasion-damage” on a “crank-shaft” the system uses those
annotations to retrieve other related images and documents. New relationships can
then be established with the found knowledge, E.G. the damage can be related to
other previous cases, and through free-text comments the relationship may be made
explicit (E.G. this type of failure happens constantly on this blade in hot conditions,
and this is proved by document x). AKTive Media has information extraction (IE)
plug-ins built in (T-Rex)[
This research methodology has been deployed in various research projects including AKT, Memories for Life, X-Media and we have scheduled a detailed user evaluation in Rolls Royce UK at the end of year, for the annotation of strip reports.