A Simple Disaster-Related Knowledge Base for Intelligent Agents
This provides a context-specific knowledge base for intelligent agents like chatbots to answer disaster-related queries, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of creating a disaster-related knowledge base for intelligent agents by building a semantic network from Philippine news articles, resulting in an ontology with 450 word assertions and an expert agreeability rate of 64%.
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.