# R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries  
[PERSON]  
1 Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Greece; [EMAIL_ADDRESS] (D.T.); [EMAIL_ADDRESS] (G.F.); [EMAIL_ADDRESS] (E.P.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (A.F.); [EMAIL_ADDRESS] (E.T.); rallis@staffe.  
Wikimedia projects. Data are available as Linked Open Data, and one can navigate through the available information through standard web browsers, automated crawlers, recommendation engines [7; 8], or pose complex SPARQL queries. Although the information there is highly interconnected, trying to retrieve answers through browsing events to trivial queries for the interconnected information is really difficult. On the other hand, SPARQL endpoints are a no-go territory for the average web user.  
On the other hand, traditional question answering systems enable users to issue queries in natural text, which is then usually transformed internally to a query over the knowledge graph, and the results are returned to the users. However, despite the progress in those systems, still, many of the user questions remain unanswered, and even simple questions in many cases return erroneous results. Toward this direction, in this paper, we present R2D2, a novel chatbot that enables real-time conversation with end users that is able to answer complex queries using an intuitive graphical user interface. The system trades expressivity for correctness, meaning that it imposes a triple-pattern like controlled language to the end users, heavily supported by auto-completion; however, it guarantees that the correct information will be retrieved from the knowledge base.  
More specifically, the contributions of this work are the following:  
We implement R2D2, an interactive chatbot answering complex queries using an intuitive graphical user interface, employing interactive cards, clickable images, and maps to enhance user experience.  
* The chatbot accepts queries in a structured yet naive triple-like language with the help of an auto-completion service that helps the users structure their query and explore the available information and in real time.
* The query is automatically transformed into the corresponding SPARQL query and sent to the DBpedia SPARQL endpoint to be further answered.
* Then, another module receives the results and formulates the answer to the end-user using nice cards, graphs, and maps.  
Exploiting our approach for a different knowledge base only requires the availability of a SPARQL endpoint and an update in the auto-complete mechanism. As such, it has the potential to be linked to other knowledge bases as well. To the best of our knowledge, there is not currently available an alternative simple yet powerful platform enabling access to SPARQL endpoints through an intelligent chatbot offering a controlled natural language interface.  
The remainder of this paper is structured as follows. In Section 2, we present related work. Then, in Section 3, we present the architecture of the proposed system and describe the various components implemented. Section 4 presents our user-based evaluation, whereas Section 5 concludes this paper and presents directions for future work.