## 5 Requirement Fulfilment Summary and Suggestions for Future Research
### Suggestions for Future Work  
By analyzing existing visual semi-automatic alignment tools, it is clear that not all relevant requirements are actually supported by available systems (see Table 1). Hence, the full potential of information visualization and visual analytics is not realized yet. Comparing the three groups of visual alignment systems, one can see that treemap interfaces, and in particular, the graph-based interfaces, come closer to fulfilling the requirements than the tree-based interfaces. Although graph-based and treemap-based approaches both have weaknesses and strong points, these are, to a certain degree, complementary, so a combination of these two approaches appears reasonable: a treemap could be used for providing an overview, while a graph visualization would provide navigation and exploration of the ontologies. A point common to all three interface groups is that no matter how mappings are incorporated into a visualization, a table displaying detailed mapping information is indispensable. A requirement which is not addressed in a completely satisfactory manner by any system is filtering. We propose an interface consisting of graph visualizations for ontology navigation and exploration, a table component displaying detailed information on the mappings, and a dedicated overview component, such as a treemap, for providing an overview and for identifying regions of interest containing promising matching candidates. To address the issue with filtering, the treemap could be replaced with another component suitable for providing an overview, such as an information landscape. An information landscape is a visualization paradigm based on a geographic map metaphor, suitable for providing an overview. It is typically applied for the discovery of patterns in large data sets by conveying relatedness in the data through spatial proximity in the visualization [43]. Due to the fact that mapped concepts would be grouped together because of the high relatedness computed by the alignment algorithm, identification of mapping candidate-rich regions would be supported (requirement 3). Filtering and highlighting (requirement 5) are naturally supported because all  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Requirement/Interface**} & \multicolumn{1}{c}{**Linked**} & \multicolumn{1}{c}{**Graph-based**} & \multicolumn{1}{c}{**Treemap-**} \\  & **Trees-based** & & **based** \\ \hline
1. Detailed mapping information provided & \(+\)1  & \(+\)1 [FOOTNOTE:1]Footnote 1: Requirement considered fully supported only if an additional table view is employed for displaying detailed mapping information. concepts involved in the alignment process would be displayed at once. Interactive zooming and panning, as well as labeling, which is automatically adjusted to the current zoom factor, would also be available (requirement 4). Therefore, a visual user interface employing graph visualization for ontology exploration, a table for the presentation of the mappings, and an information landscape as an overview component, would completely fulfil requirements 1 to 5. Employing an information landscape as an overview component could bring some additional advantages. For example, the fact that concepts identified as mapping candidates are grouped together, combined with the dynamic level of detail-dependent labeling, could be used to identify mapping candidate-heavy regions covering a particular topic of interest. This is not only relevant to requirement 4, but could also be used to assign the identified mapping candidates to an expert on that particular topic, thus supporting requirement 8. As we have seen in the above discussion, no single visual representation is capable of fulfilling all requirements. To overcome this, visual interfaces have to be built by combining several components. Coordinated multiple views (CMVs) techniques [42] are commonly used for combining multiple components.