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In this paper, we describe our efforts in establishing a simple knowledgebase by building a semantic network composed of concepts and word relationshipsin the context of disasters in the Philippines. Our primary source of data is acollection of news articles scraped from various Philippine news websites.Using word embeddings, we extract semantically similar and co-occurring wordsfrom an initial seed words list. We arrive at an expanded ontology with a totalof 450 word assertions. We let experts from the fields of linguistics,disasters, and weather science evaluate our knowledge base and arrived at anagreeability rate of 64%. We then perform a time-based analysis of theassertions to identify important semantic changes captured by the knowledgebase such as the (a) trend of roles played by human entities, (b) membershipsof human entities, and (c) common association of disaster-related words. Thecontext-specific knowledge base developed from this study can be adapted byintelligent agents such as chat bots integrated in platforms such as FacebookMessenger for answering disaster-related queries.

A Simple Disaster-Related Knowledge Base for Intelligent Agents