Visualisation of semantic enrichment

Automatically creating semantic enrichments for text may lead to annotations that allow for excellent recall but poor precision. Manual enrichment is potentially more targeted, leading to greater precision. We aim to support nonexperts in manually enriching texts with semantic annotations. Neither the visualisation of semantic enrichment nor the process of manually enriching texts has been evaluated before. This paper presents the results of our user study on visualisation of text enrichment during the annotation process. We performed extensive analysis of work related to the visualisation of semantic annotations. In a prototype implementation, we then explored two layout alternatives for visualising semantic annotations and their linkage to the text atoms. Here we summarise and discuss our results and their design implications for tools creating semantic annotations

Temporal analysis of honey bee interaction networks based on spatial proximity

The BeesBook system provides high-resolution data about bee movements within a single colony by automatically tracking individual honey bees inside a hive over their entire life. This thesis focuses on the process of designing and implementing a network pipeline to extract interaction networks from this data. Spatial proximity is used as an indicator for interactions between bees. Social network analysis methods were applied to investigate the static and dynamic properties of the resulting social networks of honey bees on a global, intermediate and local level. The resulting networks were characterized by a low hierarchical structure and a high density. The global structure of the colony seems to be stable over time. The local structure is highly dynamic, as bees change communities as they age. Communities in the honey bee network are formed by age groups that show a high spatial fidelity. The findings are in line with the established state of research that colonies are organized around age-based task division. The results of the analysis validate the implemented pipeline and the inferred networks. Consequently, this work provides an excellent foundation for future research focusing on temporal network analysis

User-defined semantic enrichment of full-text documents: Experiences and lessons learned

Semantic annotation of digital documents is typically done at meta-data level. However, for fine-grained access semantic enrichment of text elements or passages is needed. Automatic annotation is not of sufficient quality to enable focused search and retrieval: either too many or too few terms are semantically annotated. User-defined semantic enrichment allows for a more targeted approach. We developed a tool for semantic annotation of digital documents and conducted a number of studies to evaluate its acceptance by and usability for non-expert users. This paper discusses the lessons learned about both the semantic enrichment process and our methodology of exposing non-experts to semantic enrichment