# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 6 Conclusions  
This study highlights the revolutionary possibilities of incorporating AI technologies, such as ChatGPT and Gemini, in the exploration and understanding of large Knowledge Graphs. ULYSSES is the first system that enables automatic construction of FAQ lists by exploiting existing query logs and the corresponding SPARQL endpoints to answer the most frequent queries. In addition, it leverages the strengths of ChatGPT and Gemini for translating SPARQL queries and query results into plain text. ULYSSES is available online, and both the code and datasets are also available, contributing a novel ground truth dataset in the field. In essence, to the best of our knowledge, there is no other work currently available that generates FAQ lists for Knowledge Graphs. As such, this study demonstrates the unique potential of AI-driven techniques in managing KGs and opens a new direction of research, laying the groundwork for further study and advancement. Furthermore, the modular architecture of our solution enables the uninterrupted replacement of the current LLM models by new, more powerful ones that might arise or new versions of the existing ones. Limitations. We have to acknowledge the potential limitations of the various models that were used in our approach. First, it is true that those models were trained on natural language data. While they excel in understanding and generating text, their proficiency in handling structured data, such as SPARQL queries, or understanding the intricacies of RDF (Resource Description Framework) schemas can be limited. They might struggle to accurately interpret the nuances of SPARQL queries, especially when complex joins, filters, or aggregations are involved. Furthermore, the results of the queries might be too many, and a standard approach of the endpoints is to limit the results without a prioritization of the importance on the returned part. This might generate challenges in the processing of those large results, as well as incomplete answers. Another problem with the large answers is that the free version of the LLMs can only process a limited number of tokens, also potentially leading to incomplete answers. Finally, we only explored four transformer models; newer versions of those models or alternative selections might further boost the quality of the generated FAQs. Future Work. As future work, we intend to explore personalized FAQ lists for users of FAQ lists for specific domains/categories. As KGs contain a vast amount of information, they cannot be summarized in essence with a set of the \(k\) most frequent questions and answers. Different users might want a different FAQ overall or might even like to select a specific different domain or topic. As such, FAQ personalization is the direction we will next explore. Furthermore, in cases of large query results, optimal selection of a subset to use for the text would enhance the quality of the returned results. We also leave such an exploration for future work. Conceptualization, N.S., G.B., F.D., A.K., G.S., N.O., D.D., E.M., G.P., K.A., S.K., Y.K., N.V., E.S., V.D., I.G. and A.S.; methodology, N.S., G.B., F.D., A.K., G.S., N.O., D.D., G.P., K.A., S.K., Y.K., N.V., E.S., V.D., I.G. and A.S.; software, N.S., G.B., F.D., A.K., G.S., N.O., D.D., E.M., G.P., K.A., S.K., N.V., E.S., V.D., I.G. and A.S.; formal analysis, G.V., G.E.T., G.T. ; resources, N.S., G.B., F.D., A.K., G.S., N.O., D.D., E.M., G.P., K.A., S.K., Y.K., N.V., E.S., V.D., I.G. and A.S.; writing--original draft preparation, G.V., G.E.T., G.T. ; writing--review and editing, H.K. and N.P. ; visualization, G.V., G.E.T., G.T. ; supervision, H.K. and N.P. ; project administration, G.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors on request.