João Miguel Baptista Boné
Master’s in Integrated Business Intelligence Systems
October, 2020
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Tese em MSIAD, classificada com 20 valores
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Abstract
This dissertation is aimed at devising a disaster-support chatbot system with the capacity to enhance citizens and first responders’ resilience in disaster scenarios, by gathering and processing information from crowd-sensing sources, and informing its users with relevant knowledge about detected disasters, and how to deal with them.
This system is composed of two artifacts that interact via a mediator graph-structured knowledge base. Our first artifact is a crowd-sourced disaster-related knowledge extraction system, which uses social media as a means to exploit humans behaving as sensors. It consists in a pipeline of natural language processing (NLP) tools, and a mixture of convolutional neural networks (CNNs) and lexicon-based models for classifying and extracting disasters. It then outputs the extracted information to the knowledge graph (KG), for presenting connected insights. The second artifact, the disaster-support chatbot, uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, the chatbot uses local and official disaster-related knowledge, and infers the knowledge graph for dynamic knowledge extracted by the first artifact.
According to the achieved results, our devised system is on par with the state-ofthe-art on Disaster Extraction systems. Both artifacts have also been validated by field specialists, who have considered them to be valuable assets in disaster-management.
Keywords: Disaster-Management, Natural Language Processing, Artificial Intelligence, Machine Learning, Chatbots, Graph Databases
Ler mais aqui.
1º artigo produzido no âmbito desta tese:
DisKnow: A Social-Driven Disaster Support Knowledge Extraction System
2º artigo produzido no âmbito desta tese:
DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot
Candidatar a MSIAD aqui.
