A Simple Disaster-Related Knowledge Base for Intelligent Agents
Clark Emmanuel Paulo, Arvin Ken Ramirez, David Clarence Reducindo,, Rannie Mark Mateo, Joseph Marvin Imperial

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…
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Taxonomy
TopicsTopic Modeling · Sentiment Analysis and Opinion Mining · Complex Network Analysis Techniques
