Abstract

Providing access to cultural heritage data beyond book digitization and information retrieval projects is important for delivering advanced semantic support to end users, in order to address their specific needs. We introduce a separation of concerns for heritage data management by explicitly defining different user groups and analyzing their particular requirements. Based on this analysis, we developed a comprehensive system architecture for accessing, annotating, and querying textual historic data. Novel features are the deployment of a Wiki user interface, natural language processing services for end users, metadata generation in OWL ontology format, SPARQL queries on textual data, and the integration of external clients through Web Services. We illustrate these ideas with the management of a historic encyclopedia of architecture.

Abstract

We investigate the integration of Wiki systems with automated natural language processing (NLP) techniques. The vision is that of a "self-aware" Wiki system reading, understanding, transforming, and writing its own content, as well as supporting its users in information analysis and content development. We provide a number of practical application examples, including index generation, question answering, and automatic summarization, which demonstrate the practicability and usefulness of this idea. A system architecture providing the integration is presented, as well as first results from an initial implementation based on the GATE framework for NLP and the MediaWiki system.

General Terms: Design, Human Factors, Languages
Keywords: Self-aware Wiki System, Wiki/NLP Integration

Abstract

We analyse the requirements for an advanced semantic support of users—building historians and architects—of a multi-volume encyclopedia of architecture from the late 19th century. Novel requirements include the integration of content retrieval, content development, and automated content analysis based on natural language processing.

We present a system architecture for the detected requirements and its current implementation. A complex scenario demonstrates how a desktop supporting semantic analysis can contribute to specific, relevant user tasks.