A Simple Disaster-Related Knowledge Base for Intelligent Agents

TL;DR

This paper presents a simple disaster-related knowledge base built from news articles in the Philippines, using word embeddings and expert evaluation, to support intelligent agents in disaster query responses.

Contribution

It introduces a novel, context-specific disaster knowledge base derived from news data, enhanced with semantic analysis and expert validation.

Findings

450 word assertions in the knowledge base

64% expert agreement on knowledge quality

Captured semantic changes over time in disaster context

Abstract

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.