From 13-15 September, the Semantics 2022 conference took place in Vienna, together with the LTInnovate LI@Work conference. For me and many of my fellow participants, it was the first in-person event after two years of online conferencing. There were around 350 people on-site and 150 online participants. It was a real pleasure to talk to so many industry experts and to spend two evenings with them in Vienna!
The event closed with a very nice surprise for us. Together with the Vienna Business Agency, we received the ‘Best Industry Contribution Award’ for the topic of ‘Smartening Up Chatbots with Language-Agnostic Knowledge Graphs’. We showed how, with the help of Machine Translation and a Multilingual Knowledge System, a chatbot can be trained in another language with a push of a button!
How Can Knowledge Graphs Improve Chatbots?
The project, part of the CEFAT4Cities Action, was to build a chatbot which would guide through the jungle of public grants the Vienna Business Agency works with. Our key findings were:
- How to best teach chatbots, i.e. conversational agents such as Rasa, domain language (terms, synonyms) and knowledge (entities and their relations).
- No-code: Subject matter experts easily capture and share their knowledge with chatbot developers through the Coreon Multilingual Knowledge System.
- Entities are queried by Rasa in the MKS during run-time in any language.
- Through concept relations in the MKS, the chatbot becomes semantically tolerant.
- Expanding to another language is efficient. Leverage the existing stories, translate/curate entities in the multilingual knowledge graph, MT the dialogs, train the chatbot, et voilà!
If you would like to learn more, my colleague Alena has already shared in two previous blog posts how to make chatbots both smart and polyglott.
We are very proud and happy to have received an award for proofing how multilingual knowledge graphs do improve chatbots, and other applications for Multilingual AI.
