# A Graph DB-Based Solution for Semantic Technologies in the Future Internet
## 4 Connection between the LPG and SW
### SPARQL Mapping
It achieves this by employing a top-level ontology that defines ubiquitous and reusable concepts, including Person, Organization, Event, Place, Collection, IntellectualWork, and Item. These shared entities act as bridges between different domains, promoting knowledge reuse. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Domain** & **Entity Inst.** & **Entity Attr.** & **Relationship Inst.** & **Relationship Attr.** \\ \hline Overall & 337,287 & 2,089,580 & 496,839 & 41,594 \\ Overall (domain) & 2038 & 8069 & 2512 & 1958 \\ General & 102 & 573 & 222 & 132 \\ LAM & 63 & 294 & 93 & 69 \\ Retrocomputing & 1688 & 6801 & 2142 & 1757 \\ Food & 169 & 338 & 47 & 0 \\ Tourism & 14 & 56 & 8 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics on the content of the current GraphBRAIN prototype’s KG. #### 6.2.2 SKATEBOARD Evaluation Results

SKATEBOARD's quantitative evaluation revealed the following results:

* **Query Response Times:** On average, SKATEBOARD delivered query results in under five seconds, ensuring a responsive user experience. * **Efficiency:** SKATEBOARD demonstrated efficient resource utilization, with low memory and CPU consumption, even during peak usage. Qualitative Evaluation

Qualitative feedback from 35 users who engaged with SKATEBOARD within the World Literature Knowledge Graph highlighted the following aspects:

* **User Satisfaction:** Approximately 80% of the users expressed high levels of satisfaction with SKATEBOARD's interface and performance, rating their experience as \"very satisfactory\". * **Ease of Use:** The users found SKATEBOARD intuitive and easy to navigate, even when exploring complex literary relationships and connections. * **Relevance of the Results:** Feedback indicated the relevance and accuracy of the search results provided by SKATEBOARD, with 90% of users reporting that the system retrieved information that closely aligned with their queries. * **Suggestions for Improvement:** Users offered valuable suggestions for enhancement, including expanded visualization options and additional contextual information. ### Practical Implications

The evaluation results have significant practical implications for both GraphBRAIN and SKATEBOARD:

* **GraphBRAIN:** The rich knowledge representation and adaptability across domains make GraphBRAIN a valuable asset in interdisciplinary research. It can effectively support applications in fields such as Cultural Heritage, Linguistics, Open Science, Retrocomputing, Food, and Tourism. The seamless interconnections between ontologies enable knowledge reuse and cross-domain collaborations. * **SKATEBOARD:** SKATEBOARD's efficient performance and high user satisfaction in the World Literature Knowledge Graph domain demonstrate its potential for diverse applications. Collaborations with digital libraries across various domains, including ancient manuscripts, comics, and archaeological works, can benefit from SKATEBOARD's usability and effectiveness. In conclusion, the evaluation results affirm the capabilities and practical utility of GraphBRAIN and SKATEBOARD in supporting knowledge representation, exploration, and applications in interdisciplinary contexts. ## 7 Conclusions and Future Work

For complex or critical applications, knowledge-based AI systems must be used. Knowledge Graphs are currently enjoying widespread use in Semantic Web Applications, thanks to their expressive power and flexibility. This paper described GraphBRAIN, a new framework and platform for collaborative KG definition, population, and exploitation. Different from mainstream SW approaches, it is based on the LPG graph model, and adopts state-of-the-art graph DB solutions to ensure its efficiency and wide data manipulation facilities. While it can still be mapped onto standard SW representations, and thus enjoy their data repositories and reasoning tools, it was developed with the DB perspective in mind, and is open to additional functions coming from the Multistrategy Reasoning and Graph Mining branches of AI.