It is hard for domain experts to understand what is in even a small-scale ontology. The mapping tool makes the ontology more easily available to them because it can show the causal linkages between concepts in the ontology from different angles according to the point of focus. The mapping tool was developed not for understanding strict definitions of concepts, but for exploring ‘the ocean of concepts’ described by the ontology. There are various tools for

constructing ontologies, such as Protégé.3 These tools are useful for confirming the strict definitions of individual concepts, but they are not suitable for exploring a map of concepts and for showing an overview of the linkages. BMS-777607 nmr However, there are many points to improve, such as how to show the map and how to support user interaction. Once we have realized an exhaustive SS ontology, we can imagine that an enormous number of causal chains will be found. We will explore methods for using the mapping tool Paclitaxel clinical trial to visualize maps with such large numbers of causal chains more clearly or simply to verify what types of maps are most useful to users through user experiments. In the future, we will link the chains shown on the mapping tool to the content management system, which contains only linkages between the

these data contents and the concepts of SS ontology now. We will use this linkage for scoping the contents. To do this, we will first add the relationships among keywords to the metadata, thereby, making the metadata correspond to the conceptual chains

at a higher degree. Next, we plan to show on the mapping tool the contents having a high degree of coincidence between a chain on the conceptual map and the metadata of the selected concepts. We are also developing a function for comparing multiple maps, but it is still at a prototype level. Conformity examination of an ontology-based sustainability science mapping tool Compliance with knowledge-structuring requirements In “Requirements for knowledge structuring in sustainability science”, we identified six requirements for SS knowledge structuring: reusability, versatility, reproducibility, extensibility, availability, and interpretability. Reproducibility and extensibility are satisfied due to the fact that the ontology and maps have been developed as part of a computer-based ontology generation and knowledge management system, named Hozo. Reusability is guaranteed to some degree by the relative stability and domain-independence of the SS ontology. When developing the SS ontology, we tried to choose generalized concepts that are not dependent on a specific scientific domain or field.