A Simple Disaster-Related Knowledge Base for Intelligent Agents

by   Clark Emmanuel Paulo, et al.

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 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.



There are no comments yet.


page 1

page 2

page 3

page 4


TeKnowbase: Towards Construction of a Knowledge-base of Technical Concepts

In this paper, we describe the construction of TeKnowbase, a knowledge-b...

Linguistic Legal Concept Extraction in Portuguese

This work investigates legal concepts and their expression in Portuguese...

Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base

We present a new perspective on neural knowledge base (KB) embeddings, f...

Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations

We present a new knowledge-base of hasPart relationships, extracted from...

Knowledge-Base Enriched Word Embeddings for Biomedical Domain

Word embeddings have been shown adept at capturing the semantic and synt...

Neologisms on Facebook

In this paper, we present a study of neologisms and loan words frequentl...

Ontology Bulding vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are avail...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.