presentation of the paper at the PROFILES workshop @ ISWC-2017 in Vienna Abstract: Although it is still an emerging technology, the increasing usage of chatbots (also known as bots) has opened a promising touchpoint for citizen and customer engagement. A chatbot consists of a computer program aimed at simulating a conversation between humans and machines through the formulation of appropriate answers making use of external knowledge. Therefore, managing external knowledge is a crucial task for the design and development of chatbots. To facilitate the reuse of existing data sources in chatbot applications, in this paper we propose BotDCAT-AP, an extension of the Data Catalogue (DCAT) Application Profile for describing datasets for chatbots. BotDCAT-AP enables the description of intents (i.e., the actions users want to accomplish by interacting with a chatbot) and entities (i.e., individual information units associated to an intent) supported by a dataset and the method to access it. A practical usage of BotDCAT-AP is shown to demonstrate the value of its adoption.

1. BotDCAT-AP: An Extension of the
DCAT Application Profile for Describing
Datasets for Chatbot Systems
Paolo Cappello, Marco Comerio and Irene Celino
Cefriel – Politecnico di Milano, Italy – www.cefriel.com

2. Introduction: what’s a bot
Chatbots are software programs
capable of simulating a
conversation between humans and
computers
The main goal of chatbots is
providing suitable answers, possibly
carrying out specific actions, based
on the context of conversations and
users' intentions

3. Types of Chatbots
The term “chatbot” refers to a
variety of solutions that differ w.r.t
some essential characteristics:
information domain and level of
understanding
We focus on “Standard Bots”:
• Interpret users’ utterances in
Natural Language
• Cover a specified domain
Command-Based
Bot
AI Machine
Standard Bot
UNDERSTANDING
DOMAIN
OPEN
COMMAND-BASED NLP-BASED
CLOSED

4. State of the art – Bot Frameworks
PROS: Communication channels and NLU engines are already provided
CONS: Integration of (Web) information sources is left in the hands of the developer
IBM WATSON CONVERSATION SERVICE
MICROSOFT BOT FRAMEWORK

5. Problem statement & research questions
Existing bot frameworks do not provide suitable solutions to integrate chatbot applications
with external Web information sources
Literature lacks a standard to optimally describe those sources for chatbot purposes
2
How to reduce the effort required by the integration of
Web information sources into chatbot systems?
1
How to promote the reuse and discoverability of chatbot
information sources published over the Web? DATA PUBLISHER
DEVELOPER

6. Proposed solution – Architecture
ENHANCED ARCHITECTURE
DEVELOPER
DATA
PUBLISHER

7. Proposed solution – Vocabulary
DCAT-AP
BotDCAT-AP
extension
bot:http://swa.cefriel.it/ontologies/botdcat-ap

8. BotDCAT-AP vocabulary
Vocabulary modelling process (similar to GeoDCAT-AP, StatDCAT-AP, …)
Reuse of DCAT-AP to describe basic metadata about chatbot information sources
Application profile (soft and hard constraints on the vocabulary usage)
Extension of DCAT-AP with chatbot-related concepts
Intents: goals from the user point of view and method/query from the data publisher point of view
“tell me when next train passes on U2 line from Messe-Prater to Rathaus”
Entities: named resources to be identified by the NLU engine in users’ requests
“U2 is a line of Vienna subway, Messe-Prater and Rathaus are stops of the U2 subway line”
Predicates to relate the various classes in the vocabulary

9. Evaluation – Talkin’Piazza Bot
A chatbot application exploiting
BotDCAT-AP descriptions of sources
(e.g. events, POIs, and transport) to
ease the development and
maintenance processes

10. Conclusions and Future work
BotDCAT-AP is a vocabulary to describe Web information sources so to ease the
development of chatbot applications interacting with them
Future improvements of BotDCAT-AP aim to increase reusability and interoperability
of datasets for chatbots systems; some examples:
Intent taxonomy, to describe possible intent categories (e.g. follow-up intents,
fallback intents)
Intent “normalization”, by creating an ontology that includes concepts related to
the most common intents related to a specific domain (e.g. places, help desk,
events, transportation, weather forecast, e-commerce, booking, reminders, etc.)

11. Thanks for your attention!
Any question?
bot: http://swa.cefriel.it/ontologies/botdcat-ap
Irene Celino
Cefriel – Politecnico di Milano
irene.celino@cefriel.com