  In this paper, we describe our efforts in establishing 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 ontology with a total
of 450 word assertions. We let experts from the fields of linguistics,
disasters, and weather science evaluate our knowledge base and arrived at an
agreeability rate of 64%. We then perform a time-based analysis of the
assertions to identify important semantic 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.
