A Simple Disaster-Related Knowledge Base for Intelligent Agents

A Simple Disaster-Related Knowledge Base for Intelligent Agents Clark Emmanuel Paulo, Arvin Ken Ramirez, David Clarence Reducindo Rannie Mark Mateo, Joseph Marvin Imperial National University Manila, Philippines jrimperial@national-u.edu.ph Abstract In this paper, we describe our efforts in estab- lishing a simple knowledge base by building a semantic network composed of concepts and word relationships in the context of disasters in the Philippines. Our primary source of data is a collection of news articles scraped from various Philippine news websites. Using word embeddings, we extract semantically similar and co-occurring words from an initial seed words list. We arrive at an expanded ontol- ogy with a total of 450 word assertions. We let experts from the fields of linguistics, disas- ters, and weather science evaluate our knowl- edge base and arrived at an agreeability rate of 64%. We then perform a time-based analysis of the assertions to identify important seman- tic changes captured by the knowledge base such as the (a) trend of roles played by human entities, (b) memberships of human entities, and (c) common association of disaster-related words. The context-specific knowledge base developed from this study can be adapted by intelligent agents such as chat bots integrated in platforms such as Facebook Messenger for answering disaster-related queries. 1 Introduction The Philippines is a common ground for natural dis- asters such as typhoons and flooding. According to the latest statistics of Philippine Atmospheric, Geo- physical and Astronomical Services Administration (PAG-ASA)1, there are more tropical cyclones en- 1Statistics on annual tropical cyclones in the Philippines: http://bagong.pagasa.dost.gov.ph/climate/tropical-cyclone- information tering the vicinity of the Philippines than anywhere else in the world. In a year, almost 20 tropical cy- clones enter with 70% chance of developing into a full-blown typhoon. As possible result, catastrophic aftermath such as economic failure, destruction of infrastructures, and loss of lives may be imminent without proper information dissemination and pre- paredness. Thus, directing all research and techno- logical efforts to disaster preparedness and disaster risk reduction to mitigate the tremendous impacts of natural calamities have been prioritized by the coun- try for years. According to Statista2, the Philippines has 44 mil- lion active Facebook users in 2019 and is predicted to reach approximately 50 million by 2023. Thus, taking in consideration the disaster-prone situation of the country, we note the importance of establish- ing context-specific knowledge bases where it can be integrated to commonly used digital platforms such as Facebook Messenger to answer context-specific questions on disasters for possible information dis- semination and awareness. To make this possible, we present our initial efforts in building a simple knowledge base composed of word concepts joined by semantic word relationships extracted from word embeddings trained from a large online news corpus on disasters. The overview of the study is as follows: First, we compared and contrasted previous works done in field of ontology learning and mining informa- tion from word embeddings for building knowledge 2Number of Facebook users in the Philippines: www.statista.com/statistics/490455/number-of-philippines- facebook-users/ bases. Next, we discussed the method of extrac- tion …

Content truncated to 500 words