# R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries
## 3 Architecture
### Results2 Text
We measured the session success rate of using the two systems and the results are shown in Figure 3: the session success rate measures the ratio of user sessions that lead to a success. As shown, all queries were able to be appropriately formulated and answered using the R2D2 system, whereas for the DBpedia chatbot, only one query returned an actual answer (despite the fact that three of the queries were examples presented in the DBpedia chatbot site). This corresponds to only 5% of the cases in our evaluation. Overall, what we observe is that 5 min were enough for the untrained students to use the chatbot, enabling students to formulate simple triple-like queries and effectively returning the corresponding answers. As triple pattern formulation is guided by an auto-complete service, the engine guarantees that if there is a corresponding result available in DBpedia, the information will always be retrieved and presented to the user. This is not true for the DBpedia chatbot as previously shown, which struggles to answer textual user input. ## 5 Discussion and Conclusions

This paper presents a novel chatbot that is able to respond to user queries regarding information stored in the DBpedia. User input is received in the form of triple-pattern like queries. The chatbot accepts structured input and guides users on query formulation. Then, those queries are transformed to SPARQL queries and are sent to the DBpedia SPARQL endpoint to be answered. The answers are collected by another service, which enriches the results with natural text, graphical images, maps, and cards, and it sends the corresponding information to the GUI to be finally visualized and presented to the user. Our system differs from a traditional question-answering system in the fact that it employees a cleaner, user-friendly interface, it provides multimedia answers, and it also provides answers to trivial queries without even going to the DBpedia. The user evaluation performed shows the high usability of our solution and the advantages gained when R2D2 is used for searching information available through DBpedia. An advantage of the system is that it requires no complex, costly mapping of natural language input to templates, SPARQL queries, or parts of the graph, as the triples patterns are provided as input by the user through a triple-pattern like language. The simplicity of the triple-pattern like queries makes the system usable by non-trained experts, trading expressivity with usability. **Limitations. ** As SPARQL cannot handle queries with missing label parts, to mitigate partially provided labels by the users, the auto-complete service is used/enforced. As such, the end user has to select and complete the missing part of each label. However, a more natural approach would be to allow partially provided labels and do a search and importance ranking (e.g., [27]) for automatically solving the problem without relying on user input. In addition, another problem is that when the

Figure 3: Session success rates of R2D2 and DBpedia chatbot. returned results are many, then the text returned by our algorithms can have a considerable length with obvious information repetition. Further, solution modifiers (for example queries including how long, how many, average, count, etc.) performing calculation over the retrieved data are not currently supported. Although SPARQL queries are supporting this functionality, more research is required on ways that the user would prefer to optimally formulate these requests in our triple-pattern like queries. Finally, in several cases, we experienced unavailability of the external services used from DBpedia, which impacts the reliability of the system. **Future Work. ** Our immediate plans include extending the functionality of the chatbot for tackling the aforementioned limitations. We are already in the process of installing locally the services of the DBpedia so as not to rely in external services.