## 6 Interactive Graph Exploration in SKATEBOARD
### User Journey  
In Figure 2, we illustrate the interface designed for knowledge exploration. This interface plays a crucial role in facilitating the search and exploration of the extensive knowledge base to which it is connected.  
The user experience begins with the search bar, where a keyword can be entered to initiate the exploration of the knowledge base. Once the search is initiated, the interface executes a specific query based on the connected endpoint. The results are then presented in a tabular list that lists all nodes in the reference knowledge graph containing the search string in their label and related nodes based on a similarity ranking.  
At this point, the user can select a resource of interest and drag it to the central part of the interface, which we will refer to as the \"dashboard\". Each node in the graph can be explored in two modes: through the visualisation of all primary connections, i.e., relationships closely connected to the selected node, or through a dedicated table that presents the most-relevant information related to the selected entity's type.  
Figure 2: SKATEBOARD interface—Part 1.  
A distinctive feature of the system is the presence of specific views for each type of selected entity. This allows for the visualisation of closely connected relationships, more-complex relationships, and advanced queries. For example, starting from selecting an author, you can click on a map visualisation to find all publication locations related to works published by that author.  
The system profiles the user anonymously, and thanks to this profiling, it can suggest topics relevant to the user's interests. The history of searched topics allows for tracing the user's areas of interest and accessing previously searched topics.  
An element of innovation is that the system goes beyond knowledge graphs and includes labelled property graphs such as those supported by Neo4j. This allows for visualising graphs with custom domain ontologies and integrating information from public endpoints like DBpedia with proprietary ontologies.  
Another important aspect is the collaborative validation of node associations. Users can improve data quality in connected knowledge bases by contributing to the creation of high-quality content for domain experts.  
In Figure 3, you can observe an example of selecting an entity of type \"Expression\", such as a literary work. The right panel displays the most-pertinent information for an entity of this type. In the following figure, you can see an exploration of an entity of type \"Person\", with specific information for that entity type and that specific endpoint.  
The primary goal of the SKATEBOARD interface is to highlight the connected endpoint and offer domain experts a space to share knowledge and enrich their information repository through innovative information visualisation, combining visualisation paradigms for linked data and graphs: node-link, tabular, and multilevel. This approach allows for incremental exploration of connected resources and discovering paths that link graph entities, fully leveraging the potential of semantic integration and graph reasoning in LPGs.