# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 1 Introduction  
Every day, a huge volume of new information becomes available online. RDF Knowledge Graphs (KGs) are expanding at a rapid pace, now encompassing millions, or even billions, of the triples available on the Web. For instance, the Linked Open Data Cloud currently houses over 62 billion triples, structured into vast and intricate RDF data graphs. The sheer complexity and massive scale of these data sources pose significant challenges in their utilization, highlighting the urgent need for effective and efficient methods to explore and comprehend their content [1].  
The promise. Frequently Asked Questions (FAQ), on the other hand, is an essential service for user self-assistance, offered by several websites. FAQ typically presents a list of questions, each associated with an answer, enabling users to rapidly identify key questions/answers in the context of the information provided by the website. However, although FAQ has proven its usefulness on the Web [2], it remains completely unexplored in the context of the large Knowledge Graphs now available online.  
The problems. There are several reasons why FAQ is unavailable for KGs. First, since KGs contain a large amount of information, it is unclear which information is the most interesting to present to users. Second, the most interesting Knowledge Graphs containmillions of statements, and, as such, processing them is time-consuming and resource-intensive. Although several approaches have already focused on extracting the most interesting part of a KG in the form of a summary graph [1; 3; 4], users are accustomed to seeing textual descriptions of both the questions and answers and not structured queries or subgraphs of the original graph.  
The solution. In this paper, we present ULYSSES, the first system enabling the construction of high-quality FAQ lists over large Knowledge Graphs.  
More specifically, our contributions are the following:  
1. To address the problem of selecting the relevant information from large Knowledge Graphs, ULYSSES does not use the original Knowledge Graph but instead exploits query logs. These query logs are available to the curators of the KGs through the SPARQL endpoints of the corresponding KGs.
2. ULYSSES identifies the most frequent SPARQL queries in the logs and uses the corresponding SPARQL endpoints to retrieve their answers.
3. Then, to transform both the queries and the answers into text, it exploits transformer models, i.e., the ChatGPT and the Gemini LLMs, using appropriate prompts.
4. We evaluate our approach on the DBpedia KG and we show the interesting results achieved by ULYSSES.
5. As a side effect, we also generate and offer to the community the first golden standard dataset generated by a user study.  
To the best of our knowledge, this is the first approach for automated construction of FAQ lists for large Knowledge Graphs, exploiting existing user logs and transformer models.  
The rest of this paper is organized as follows: Section 2 presents related work. Section 3 presents our solution, detailing the various steps for generating a high-quality FAQ list for a given knowledge base. In Section 4, we evaluate our approach on DBpedia and present the experimental evaluation of our work. Finally, Section 5 concludes this paper and presents directions for future work.