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ISBN: 978-972-8939-25-0 © 2010 IADISAUTOMATIC SEMANTIC IMAGE ANNOTATION WITHINTHE DOMAIN OF SOFTWARE MODELINGMatthias Heinrich and Antje Boehm-PetersSAP AG, SAP Research, Dresden, GermanyABSTRACTThe information society requires capable means to access the vast set of digital content. Currently, search engines are thepredominant approach to find suitable data. Although state-of-the-art search engines deliver decent results for textualcontent, algorithms to retrieve appropriate bitmap data fall short on providing proper image selections. Therefore, wepropose a semantic image annotation solution that automatically classifies raw bitmap data visualizing software diagramswith an ontological formalism. Rather than applying pattern recognition techniques to an existing image pool, bitmapdata is enhanced by semantic annotations at image creation time. Thus, the underlying data model can be exploited toderive semantic annotations. In contrast to pattern recognition algorithms, our approach implies a precise ontologicalclassification which is the foundation of accurate bitmap search.KEYWORDSImage Annotation, Ontology-Based Annotation, Automated Annotation1. INTRODUCTIONThe information age brings along a rapid increase of digital content. For example in 2000, the total of diskstorage summed up to 3,000,000 TB and this capacity is doubling each year [Coffman et al. 2002]. In orderto leverage the broad knowledge base, information technology has to provide appropriate means to access theavailable information. Thus, search engines have evolved and nowadays satisfy search queries operating ontextual content.However, bitmap search still delivers poor results. This is due to the fact that retrieving meaningfulkeywords from bitmap data in an automated fashion is a cumbersome task. Numerous research activitiesfocus on pattern recognition techniques [Schreiber et al. 2001] which are able to identify and classify objectsvisualized on images. Nevertheless, fully-automated classification systems are not adequate to achieve 100%accuracy.Therefore, we propose an automated image annotation platform capable of associating bitmaps withprecise semantic annotations. The platform serves as an image annotator as well as an image generator forbitmaps derived from software models (e.g. UML, BPMN). Since image creation and image annotation isexecuted by one integrated platform, annotation derivation algorithms have full access to the underlying datamodel. This data model is a typed, well-defined and programmatically accessible model representing aprecise semantic description of the visualization. Consequently, the association of images with semanticannotations breaks down to the task of preserving knowledge captured in the data model within the targetexport format. In the proposed solution, we have combined the semantic formalism supplied by the SemanticMediaWiki system with widespread bitmap formats such as JPEG. Eventually, this enforces a semanticallyenriched and consistent image library which allows for precise semantic search.In this paper, we proceed with a brief discussion of related work in Section 2. Section 3 introduces thehigh-level architecture of the semantic image annotation platform and describes the concrete implementationin detail. Finally, in Section 4 we summarize the benefits of the presented solution and propose furtherapplicable domains.406