## 3 Requirements for Visual Semi-automatic Alignment Approaches
### User Driven Requirements  
Automated ontology alignment is central to the interoperability of semantic systems. However, since fully automatic methods produce imperfect mappings, and will most likely do so in the foreseeable future, involving human experts in the alignment process becomes a necessity. While the evaluation of automatic methods, such as those performed by OAEI [20], provides an objective assessment of their performance and indicate directions for their improvement, additional important questions need to be answered when humans should be involved in the process:  
* How to present the mappings to the user?
* Do users consider automatically generated mappings useful and trustworthy?
* What degree of automation is feasible (when human intervention becomes necessary)?
* What processes and workflows are users following when creating, inspecting, and managing the mappings?
* What are the requirements for cognitive support for ontology mapping tasks?
* What are appropriate representations and user interactions for specific tasks and processes?
* What is the role of collaboration and how do users wish to coordinate teamwork?
* Which existing tools and interactive interfaces do users prefer and why do they prefer them?
* What are the long and short term usage patterns of the systems?
* Influence of interface usability and quality of automatic alignments on acceptance of interactive systems.
* How can one most adequately utilize machine and human advantages?  
Studies attempting to provide answers to these questions are still rare (as of 2009). In [27, 28, 29], user surveys and evaluation studies were conducted providing answers to most of the above questions. A main conclusion is that there is demand for interactive alignment systems. Users are willing to test and use various systems and do find automatically generated mappings useful. However, the task of creating the mappings is considered complex and working with tools is mostly described as hard. Interestingly, better cognitive support was expected to improve productivity more than advancements in the matching algorithms.  
Performed user surveys and evaluation studies delivered assessments on the functionality of the existing tools as well as numerous improvement suggestions. Based on these results, we derive a summarized set of requirements for interactive ontology alignment tools:  
1. Presentation of mapping candidates together with the estimated confidence and, if possible, with the inclusion of information on why the mapping was generated.
2. Navigation and exploration of ontologies providing detailed information on every element of the explored ontology.
3. Overview of the alignment results for identification of regions with promising matching candidates.  
4. Capability to adjust the level of detail for the viewed data, as well as the choosing of the area of interest which shall be explored.
5. Filtering depending on features of the mappings, such as terms describing the concepts, mapping confidence, status of the mapping (confirmed, rejected, not inspected), _etc_.
6. Confirming and rejecting automatically generated mappings as well as adding and removing mappings manually. If possible, this should be done such that the system will learn from users' interventions.
7. Collaboration via communication, commenting, tagging, and the voting on and annotating of mappings and ontology elements.
8. Ability to partition the mapping task into chunks assignable to team members and to monitor team member progress.
9. Saving and loading of users' changes.  
Looking at the requirements 1 to 5, it becomes clear that these make an excellent fit for visualization techniques. While the remaining requirements are usually not primarily addressed by visual methods, requirements 6 to 8 could also benefit from the use of visualization, in particular from visual components introduced to address requirements 1 to 5.