Automatic Tagging as a Support Strategy for Creating Knowledge Maps

Graph organizers are powerful tools for both structuring and transmitting knowledge. Because of their unique characteristics, these organizers are valuable for cultural institutions, which own large amounts of information assets and need to constantly make sense of them. On one hand, graph organizers are tools for connecting numerous chunks of data. On the other hand, because they are visual media, they offer a bird's-eye view perspective on complexity, which is digestible to the human eye. They are effective tools for information synthesis, and are capable of providing valuable insights on data. Information synthesis is essential for Heritage Interpretation, since institutions depend on constant generation of new content to preserve relevance among their audiences. While Mind Maps are simpler to be structured and comprehended, Knowledge Maps offer challenges that require new methods to minimize the difficulties encountered during their assembly. This paper presents strategies based on manual and automatic tagging as an answer to this problem. In addition, we describe the results of a usability test and qualitative analysis performed to compare the workflows employed to construct both Mind Maps and Knowledge Maps. Furthermore, we also talk about how well concepts can be communicated through the visual representation of trees and networks. Depending on the employed method, different results can be achieved, because of their unique topological characteristics. Our findings suggest that automatic tagging supports and accelerates the construction of graphs