The QALD-9 challenge focused on the successful and long running multilingual QA task. In particular, we wanted to create an updated dataset which is larger than ever before and has a higher quality due to the employment of QUANT's semi-automatic curation measure. [2]. The teams were required to provide web-services of their systems to participate in the challenge, which will in turn support comparable research in the future in contrast to former XML/JSON file submissions. This increased the entrance requirements for participating teams but ensures long term comparability of the system performance and a fair and open challenge. In the future, we will further simplify the participation process and offer leaderboards prior to the actual challenge to allow participants to see their performance beforehand. After feedback from the authors, we will likely add new key performance indicators for the capability of a system to know which questions it cannot answer and take confidence scores for answers into account.