Ontology merging with formal concept analysis

Ontologies have been established for knowledge sharing and are widely used as a means for conceptually structuring domains of interest. With the growing usage of ontologies, the problem of overlapping knowledge in a common domain becomes critical. In this short paper, we address two methods for merging ontologies based on Formal Concept Analysis: FCA-Merge and ONTEX. --- FCA-Merge is a method for merging ontologies following a bottom-up approach which offers a structural description of the merging process. The method is guided by application-specific instances of the given source ontologies. We apply techniques from natural language processing and formal concept analysis to derive a lattice of concepts as a structural result of FCA-Merge. The generated result is then explored and transformed into the merged ontology with human interaction. --- ONTEX is a method for systematically structuring the top-down level of ontologies. It is based on an interactive, top-down- knowledge acquisition process, which assures that the knowledge engineer considers all possible cases while avoiding redundant acquisition. The method is suited especially for creating/merging the top part(s) of the ontologies, where high accuracy is required, and for supporting the merging of two (or more) ontologies on that level.

Instant Storyboarding

This thesis aims at empowering software customers with a tool to build software tests them selves, based on a gradual refinement of natural language scenarios into executable visual test models. The process is divided in five steps: 1. First, a natural language parser is used to extract a graph of grammatical relations from the textual scenario descriptions. 2. The resulting graph is transformed into an informal story pattern by interpreting structurization rules based on Fujaba Story Diagrams. 3. While the informal story pattern can already be used by humans the diagram still lacks technical details, especially type information. To add them, a recommender based framework uses web sites and other resources to generate formalization rules. 4. As a preparation for the code generation the classes derived for formal story patterns are aligned across all story steps, substituting a class diagram. 5. Finally, a headless version of Fujaba is used to generate an executable JUnit test. The graph transformations used in the browser application are specified in a textual domain specific language and visualized as story pattern. Last but not least, only the heavyweight parsing (step 1) and code generation (step 5) are executed on the server side. All graph transformation steps (2, 3 and 4) are executed in the browser by an interpreter written in JavaScript/GWT. This result paves the way for online collaboration between global teams of software customers, IT business analysts and software developers.