## 8 Conclusions
### Future Directions  
The evaluation of the SKATEBOARD system provided valuable insights into its current performance and user satisfaction. To further enhance the system and ensure its continued effectiveness, several key areas deserve attention:  
**Performance optimisation:** Ensuring the system's performance is vital for user satisfaction, particularly for tasks that require responsiveness and efficiency. Future work should focus on optimising the system's performance by addressing issues related to lags, delays, and resource utilisation. Regular performance monitoring and fine-tuning are essential. **Integration and interoperability:** Improving the system's integration capabilities with other tools and systems is crucial. Future development should ensure seamless interoperability, allowing users to integrate the SKATEBOARD system effortlessly into their existing workflows. **Feature expansion and innovation:** User feedback and feature requests should be carefully considered and prioritised. Future development efforts should focus on expanding the system's features based on user needs and emerging industry trends, promoting continuous innovation and maintaining competitiveness. By focusing on these core areas and embracing these future directions, the SKATEBOARD system can evolve into a more user-centric, efficient, and adaptable platform. A commitment to continuous improvement and meeting user needs will ensure the system's long-term success and solidify its role as a valuable tool for its users. Furthermore, this paper emphasised the pivotal role of graphs in representing information and their suitability for knowledge representation and discovery. Additionally, itunderscores the transformative impact of artificial intelligence, specifically AI models like OpenAI's GPT, in enabling semantically meaningful reasoning from data. Acknowledging these advancements, it is crucial to recognise a substantial gap in the current landscape, where many AI-driven solutions lack transparency and user control. To bridge this gap effectively, SKATEBOARD is introduced as a comprehensive framework and tool that empowers users throughout the information-extraction and -manipulation process. It offers a multi-faceted approach, encompassing information extraction, ontology creation, ontology management, and interactive exploration, all guided by Linked Data principles and a graph-based exploration paradigm. SKATEBOARD's unwavering commitment to transparency ensures that users can seamlessly navigate information, gaining a clear understanding of relationships and dependencies. Furthermore, the tool introduces the potential for recommendation systems and reasoning capabilities, fostering serendipitous discoveries and the emergence of novel insights. This comprehensive approach positions SKATEBOARD as a pioneering solution in addressing the challenges posed by the evolving knowledge exploration and discovery landscape. Conceptualisation, E.B., D.D.P., S.F. and D.R. ; methodology, E.B., D.D.P., S.F. and D.R. ; software, E.B. ; validation, E.B., D.D.P., S.F. and D.R. ; formal analysis, E.B., D.D.P., S.F. and D.R. ; investigation, E.B. ; resources, E.B., D.D.P., S.F. and D.R. ; data curation, E.B., D.D.P. and D.R. ; writing--original draft preparation, E.B., D.D.P., S.F. and D.R. ; writing--review and editing, E.B., D.D.P., S.F. and D.R. ; visualisation, E.B. ; supervision, S.F. ; project administration, S.F. ; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript. This research was partially supported by the projects Future AI Research (PE0000013), spoke 6 (FAIR) Symbiotic AI, and Cultural Heritage Active innovation for Next-GEn Sustainable society (CHANGES) (PE00000020), Spoke 3 (Digital Libraries, Archives and Philology), under the NRRP MUR program funded by the NextGenerationEU. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.