CEFAT4Cities targets the development of multilingual cross-border e-Government services, facilitating the conversion of natural-language administrative procedures into machine-readable data. We showcase the integration of CEFAT4Cities results into SmartBot, a prototype of a multilingual chatbot, developed for the Vienna Business Agency (VBA) in scope of the project. SmartBot makes VBA's services discoverable in a user-friendly way, fine-targeting such topics as starting a new business and finding relevant grants among hundreds of funding opportunities. It is driven by multilingual AI that contains the results of CEFAT4Cities workflows, integrated into its domain knowledge along with multilingual domain-specific vocabularies, represented in a language-agnostic knowledge graph in Coreon. Thanks to the integrated multilingual knowledge system (MKS), SmartBot is able to infer connections between language-agnostic concepts and deal with terms, previously unseen by the bot's language model.
Nowadays, tools and data provided by public sector and private organizations still tend to be
institutionally fragmented. The fragmentation of European e-government fabric triggered the
emergence of interoperability solutions, to unify and simplify interaction between cross-border
and cross-sector services. EuroVoc1 and ISA22 belong to such inter-operable solutions, fostering
uniformity within technical, semantic, organizational, and legal layers across the EU [
In this paper, we showcase how this resource is leveraged in a prototype of a real-life chatbot
application, SmartBot, developed for the Vienna Business Agency (VBA)5 in scope of the
CEFAT4Cities project. Lately, chatbots have started to emerge in various fields, featuring use-cases
like information retrieval, service discoverability, customer service, and administrative
worklfows [
4https://ec.europa.eu/isa2/solutions/core-public-service-vocabulary-application-profile-cpsv-ap_en
5https://viennabusinessagency.at/
6https://github.com/RasaHQ/
7https://www.coreon.com/
utterances. The dialogue management component predicts the next action in a conversation
based on the context. Rasa SDK handles all of our custom code: it is organised as custom
actions that search databases, make API calls, trigger a handover of the conversation to a human,
etc. Rasa Open Source is therefore adjustable to developers’ needs, featuring straightforward
integration and data control [
On top of Rasa, our architecture features an integration with Coreon Multilingual Knowledge
System (MKS) [
A big part of any chatbot’s implementation is associated with domain data. In our case, a smooth cooperation in knowledge transfer is facilitated by MKS: VBA domain experts used it to model their domain knowledge, populating and curating it as a graph (see Data Curation side in Figure 1). The repository also incorporates the interoperability layer and public servicethemed multilingual vocabularies. Aside from easy knowledge drafting, there are 4 concrete challenges that are tackled by the incorporation of language-agnostic knowledge graphs in virtual assistants: i) multilingualism; ii) language-independent entity management; iii) enabling semantic search; iv) dealing with homonymy and unseen terms.
In the European context, multilingualism is a big asset, yet it also brings along a conceptual challenge: the kind of multilingualism served tends to heavily influence the architecture and scalability of a solution. We decided to go with individual NLU models per language, i.e. keeping them language-specific, while making dialogue management – Stories – universal, adding an extra layer of abstraction to maintain consistency in bot’s behaviour across languages. It implies that the core model should not have a single language-specific string among the training data, but rather an abstraction for the representation of entities, like languageindependent IDs. We abstracted from entity maintenance in distinct languages, replacing language-specific terms in the NLU training data with their unique Coreon concept IDs. Maintaining entities in each language separately would be tedious and not consistent, particularly since the VBA domain knowledge is not static. Also, agnostic entities are crucial for keeping the Core module language-agnostic, abstracted from entity names in a specific language. Once VBA decides to expand SmartBot’s language capabilities with a new language, this method of universal entities will ensure smooth model development and minimization of the labeling efort.
The core goal of SmartBot is to serve the user relevant grant recommendations based on previously provided input (see Figure 2 for a conversation snippet). This implies that the bot will have to fetch records with relevant VBA grants. To achieve this, we match information drawn from the user’s input that influences the funding outcome (e.g., intents, entities, and their types extracted by Rasa NLU).
Since grant information was also imported into MKS and each grant entry linked with relevant entity types, we can leverage these relations between concepts in the repository. With this functionality, SmartBot is able to fetch relevant funding entries even when terms extracted from the user’s input are not explicitly appearing among VBA funding entries: the bot navigates parental and associative relations of the extracted entity and infers if there are any semantically close or connected concepts, linked with specific funding entries. Ultimately, we cover this scenario: given a VBA grant for SMEs focusing on environmental protection, a user X, searching for grants for small businesses doing roof planting/vertical gardening, and a user
Y, looking for funding to support a startup that calFigure 2: A demo dialogue snippet. culates CO2 footprint for businesses, would both land at the aforementioned grant.
Unseen terms and homonymy is tackled by the KG in the same fashion. If users choose to use terminology previously unknown to the model, SmartBot will first try to get its meaning using the connector to Coreon rather than taking a standard fallback. If a German user enquires about the amount of money they can get from VBA; they refer to money as Kohle, a slang term homonymous to Kohle, "coal", a fossil. The NLU model does not know this term, so the bot makes an API request, searches for it in MKS, and finds two hits in two distinct concepts. The ifrst one belongs to CO2 concept in a branch dealing with resource-saving and sustainability. The second one is found among synonyms for Geldmittel, denoting financial funds and has a more generic parent Geld, "money". Since quite a few terms of the concept Geldmittel are known to the NLU model and the context of the conversation is corresponding, the meaning of Kohle is disambiguated for the chatbot; subsequently, SmartBot informs the user about the amount of money they can qualify for.
Chatbots are becoming a turning point for rationalizing of business processes. Here we investigated technical feasibility and described the implementation of the prototype that can support VBA and serve the needs of Vienna residents, catering to the interaction in the language of their choice and understanding the intents of their requests.
Combining Rasa Open Source with reusable multilingual KG data, we delivered the intelligent chatbot solution, robust, extendable, and modular – a steady reference point for similar activities to facilitate provision of PSI. Accommodating the chatbot interaction to the user’s needs, VBA SmartBot automatically overcomes the language gap, contributing to the elevation of local public services to the European scale and red tape reduction.
This project utilises the results of CEFAT4Cities Action, funded by the European Commission’s CEF Telecom programme under Grant 2019-EU-IA-0015.