Integrating Wiki Systems, Natural Language Processing, and Semantic Technologies for Cultural Heritage Data Management
Modern documents can easily be structured and augmented to have the characteristics of a semantic knowledge base. Many older documents may also hold a trove of knowledge that would deserve to be organized as such a knowledge base. In this chapter, we show that modern semantic technologies offer the means to make these heritage documents accessible by transforming them into a semantic knowledge base. Using techniques from natural language processing and Semantic Computing, we automatically populate an ontology. Additionally, all content is made accessible in a user-friendly Wiki interface, combining original text with NLP-derived metadata and adding annotation capabilities for collaborative use. All these functions are combined into a single, cohesive system architecture that addresses the different requirements from end users, software engineering aspects, and knowledge discovery paradigms. The ideas were implemented and tested with a volume from the historic Encyclopedia of Architecture and a number of different user groups.
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.
The different stages in the life-cycle of contentcreation, storage, retrieval, and analysisare usually regarded as distinct and isolated steps. In this paper we examine the synergies resulting from their integration within a single architecture.
Our goal is to employ such an architecture to improve user support for knowledge-intensive tasks. We present a case study from the area of building architecture, which is currently ongoing.
We analyse the requirements for an advanced semantic support of usersbuilding historians and architectsof 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.
Accurate lemmatization of German nouns mandates the use of a lexicon. Comprehensive lexicons, however, are expensive to build and maintain. We present a self-learning lemmatizer capable of automatically creating a full-form lexicon by processing German documents.
I'm happy to announce the first public release of our free/open source Durm Lemmatization System for the German language.
The release comes with source code, binaries, documentation, resources (German lexicon, Case Tagger probabilities), and manually annotated texts from the German Wikipedia for evaluation.