# DisBot: A Portuguese Disater Support Dynamic Knowledge Chatbot
## 1 Introduction  
Disaster management is a series of procedures intended to be implemented before, during and after disasters, in order to avoid or minimize their damage [1]. It is becoming a wide and more spread out topic, as we have seen an increase in disaster occurrences, whether from extreme weather events, such as earthquakes, hurricanes or floods, or from man-made disasters like most wildfires and terrorism. According to [PERSON] [2], the demand for risk management has risen along with the growing number of risks we are facing, which directly leads to an urging need for advancements in disaster management technologies. However, the use of automatic information dissemination technologies, such as chatbots, is still very lacking in this field of study, although their potential for improving authorities' decision making and population access to information, is substantial [3]. Language is the cornerstone of human communication and sentience, and conversation is the most basic and uniquely privileged domain of that cornerstone. It is the first kind of language we know as children, and for the majority of us, it is the kind of language we most commonly nourish [4]. Chatbots are machine agents that serve as natural language user interfaces for data and service providers [5]. They make use of our attachment to natural language conversations to enable users to have a greater sense of security when exchanging information with an artificial intelligence (AI) entity. They achieve this by closely simulating what a conversation with another human being would be like, in a specific scenario. Chatbots have been around for a while as both personal assistants and information facilitators. We saw their birth in 1966, when [PERSON] [6] created a \"system which makes certain kinds of natural language conversation between man and a computer possible\". His creation, Eliza, analyzed input sentences on the basis of keywords. Chatbots have since come a long way, from Parry in the 1970s to the focus in task-completion virtual assistants in the 2000s. From 2015 on, chatbot research showed an exponential growth, displaying a progress from virtually no publications in the year 2000, to thousands of publications in 2019. Personal assistants and social chatbots have been, by far, the main focus of this field's applications in the 2000s and 2010s, led by huge developments in personal assistants of big companies, such as Alexa and Siri, and the recent social chatbots, such as Microsoft's XiaoIce [7]. These entities have, nevertheless, the tremendously challenging task of efficiently working in open domain scenarios [7]. In closed domain scenarios, however, chatbots have also been widely used. In the healthcare sector, they have been used in aiding treatment for different diseases such as cancer [8] and asthma [9], or even for promoting healthier habits [10]. Chatbots are also being used in several other closed domain trending areas, such as home automation [11], customer service [12; 13], and education [14]. As seen in these sectors of activity, chatbots have a huge potential for disseminating information that is, in many different ways, beneficial to their users. With all of this in mind, in disaster-management, which is also a closed-domain, chatbots have the potential to efficiently disseminate information that will positively impact both authorities and population's decision making. The state-of-the-art in disaster support chatbot systems is very recent and solely focused on decision makers, and formal knowledge such as official reports, for a very limited amount of disasters. There is an opportunity of creating a system that can take advantage of the large amounts of data human beings are constantly creating. The relevance of this system can be further enhanced by being able to summarize this data and inform disaster stakeholders about it, in an automatic and reliable way, which would greatly improve human resilience and facilitate decision making in disaster-related emergency scenarios.