# A Simple Disaster-Related Knowledge Base for Intelligent Agents

Clark Emmanuel Paulo, Arvin Ken Ramirez, David Clarence Reducindo
Rannie Mark Mateo, Joseph Marvin Imperial
National University
Manila, Philippines
<EMAIL_ADDRESS>

###### 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.

##  1 Introduction

The Philippines is a common ground for natural disasters such as typhoons and
flooding. According to the latest statistics of Philippine Atmospheric,
Geophysical and Astronomical Services Administration (PAG-ASA)111Statistics on
annual tropical cyclones in the Philippines:
http://bagong.pagasa.dost.gov.ph/climate/tropical-cyclone-information, there
are more tropical cyclones entering the vicinity of the Philippines than
anywhere else in the world. In a year, almost 20 tropical cyclones enter with
70% chance of developing into a full-blown typhoon. As possible result,
catastrophic aftermath such as economic failure, destruction of
infrastructures, and loss of lives may be imminent without proper information
dissemination and preparedness. Thus, directing all research and technological
efforts to disaster preparedness and disaster risk reduction to mitigate the
tremendous impacts of natural calamities have been prioritized by the country
for years.

According to Statista222Number of Facebook users in the Philippines:
www.statista.com/statistics/490455/number-of-philippines-facebook-users/, the
Philippines has 44 million active Facebook users in 2019 and is predicted to
reach approximately 50 million by 2023. Thus, taking in consideration the
disaster-prone situation of the country, we note the importance of
establishing context-specific knowledge bases where it can be integrated to
commonly used digital platforms such as Facebook Messenger to answer context-
specific questions on disasters for possible information dissemination and
awareness. To make this possible, we present our initial efforts in building a
simple knowledge base composed of word concepts joined by semantic word
relationships extracted from word embeddings trained from a large online news
corpus on disasters.

The overview of the study is as follows: First, we compared and contrasted
previous works done in field of ontology learning and mining information from
word embeddings for building knowledge bases. Next, we discussed the method of
extraction of concepts and entities from our news article dataset as well as
the semantic labels used for building concept relationships. In the Results
section, we detail the outcome of using word embeddings trained from our
collected dataset for extracting semantically similar concepts using an
initial concept seedwords list. In addition, we also discussed the integrity
of the disaster ontology from expert validation. We wrap up the study by
extensively discussing the semantic changes exhibited by concepts present in
the knowledge base over time.

##  2 Previous Work

In this section we highlight significant works in using knowledge bases (KBs)
as the main source of intelligence for virtual agents as well as current
trends using word embeddings as one way of extracting information and semantic
word relationships.

###  2.1 Building Knowledge-Based Intelligent Agents

Over the years, the use of powerful knowledge bases are seen on a wide variety
of intelligent agents such as chat bots, storytelling agents, and recommender
systems. The work of [Ong et al., 2018] focused on building a commonsense
knowledge base for a storytelling agent for children using assertions
extracted from ConceptNet [Speer et al., 2017]. ConceptNet is a large semantic
network of word relationships that can be adapted by intelligent agents for
commonsense reasoning and identifying object relationships. The study
developed a knowledge base by filtering out concepts and concept relationships
that are not used in the context of children storytelling from the ConceptNet.

Similarly, ?) developed a natural language dialog agent that utilizes a
knowledge base to generate diverse but meaningful responses to the user. The
system extracts related information from knowledge base, which was adapted
from FreeBase [Bollacker et al., 2008], via an information extraction module.
The use of a large, external knowledge base allows the dialog system to expand
on the information from the users response for a more detailed and interactive
reply.

On the other hand, the work of ?) focused on the development of a system
composed of three ontology-based sub-agents for personal knowledge, fuzzy
inference, and semantic generation for evaluating a person’s health through
his/her diet. The system makes use of an ontology with an embedded knowledge
base considering the persons health statistics such as BMI, Caloric
Difference, Health Diet Status combined with rules laid down by domain
experts. Results shows that the proposed system exhibits an intelligent
behavior in helping the dietary patterns of users based on their information
from the constructed ontology.

###  2.2 Knowledge from Word Embeddings

The advent of word embeddings as one of the modern approaches in extracting
semantic relationships of words has fueled research works to use its potential
to build more powerful knowledge bases. ?) used a supervised approach, similar
to text classification, for predicting the taxonomic relationship via
similarity of two concepts using a Word2Vec embedding [Mikolov et al., 2013b].
Results showed that combining the word embedding with an SVM classifier
outperformed baseline approaches for taxonomic relationship extraction such as
using the Jaccard similarity formula and naive string matching.

The study of ?) went as far as building a custom neural network architecture
with dynamic weighting to significantly increase the performance of
statistical and linguistic approaches in extraction word relationships from
word embeddings. The neural network considers not only the word relationship
such as hypernym and hyponym but also the contextual information between the
terms. The proposed approach exhibits generalizability for unseen word pairs
and has obtained 9% to 13% additional accuracy score using a general and
domain-specific datasets.

Likewise, the work of ?) submitted to the SemEval-2016 Task for Taxonomy
Extraction Evaluation [Bordea et al., 2016] focused on using GloVe word
embedding model [Pennington et al., 2014] with an offset feature to extract
hypernym candidates from a sample word list. Results showed that a vector
offset cannot completely capture the hypernym-hyponym relationship of words
due to complexity.

| 2017 | Tag
---|---
family | noun
fire | noun
flood | noun
update | verb
tricycle | noun
| 2018 | Tag
---|---
act | verb
announced | verb
ashfall | noun
police | noun
lava | noun
| 2019 | Tag
---|---
bulletin | noun
quake | noun
typhoon | noun
weakened | adjective
affected | verb
Table 1:  Top 5 seed words per year.

##  3 Data

For this study, we scraped over 4,500 Filipino disaster-related news articles
from years 2017 to 2019 (1,500 articles per year) from Philippine news
websites using Octoparse Webscraping Tool as our primary dataset. The corpus
covers a wide range of natural disasters that transpired in the Philippines
such as typhoons, earthquakes, landslides and also includes statistics from
damages and casualty reports. This large collection of news articles will be
the groundwork of the knowledge base as it contains disaster-related context
words as concepts. We partitioned the dataset into three by year (2017 to
2019) for the word embedding model generation. The purpose of partitioning the
dataset will allow us to analyze the temporal changes of sematic relationships
of concepts. More on this is discussed in the succeeding sections. To note,
all concepts presented in this document are translated to English for the
international audience.

##  4 Building the Knowledge Base

###  4.1 Extracting Concept Seedwords

Building a knowledge base starts with establishing concepts or words that
refer to real world entities such as nouns, adjectives, and verbs that
represent everyday objects such as apple, spoon, cake, people like mother,
police, mayor, description of objects such as beautiful, red, big, and action
words that signify an activity such as walks, eating and jumped [Ong, 2010].

To identify the grammatical category of each concept to know whether it is a
noun, an adjective, or a verb to aid the semantic relationship labelling, we
used a Filipino parts-of-speech (POS)
tagger333github.com/matthewgo/FilipinoStanfordPOSTagger developed by ?) which
is currently integrated in the Stanford CoreNLP package [Manning et al.,
2014]. We extracted the top 50 high-frequency concepts per year from the
collected news dataset, having a total of 150 initial seed words. Table 1
shows the top 5 seed words per year from the initial word list. Both common
words such as family, tricycle, police and update as well as disaster-related
words such as flood, ashfall, typhoon, and quake to name a few are present.
These concepts are then paired with other concepts to form a meaningful
representation of knowledge called a binary assertion described in the next
section.

Relation | Tag | Rule
---|---|---
Synonym | SYN | If concept1 has the same meaning with concept2
Antonym | ANT | If concept1 has the opposite meaning with concept2
Hypernym | HYP | If concept1 has a broader meaning compared to concept2
Performs | DO | If concept1 is the actor/does of concept2
Membership | PartOf | If concept1 is a member of concept2
Adjective | IS | If concept1 describes concept2
Cause | CAUSE | If concept1 is the cause of event concept2
Effect | dueTo | If concept1 the resulting effect of event concept2
Random | RAND | If concept1 has no direct relationship concept2
Table 2:  Semantic relations with its corresponding rules and examples.

###  4.2 Semantic Relationship Labelling

Once the set of context-specific words are obtained from the dataset, in the
case of this study, disaster-related concepts, the next process to establish
the correct semantic relationship of words. These semantic relationships can
be structured in the form of a binary assertion as previously stated. ?)
stated that the binary assertions of concepts are needed by virtual agents
such as a storytelling agent or a chatbot to be able to generate responses
from a commonsense knowledge base. Semantic relationships can also be used for
other tasks such query expansion of words as well as information retrieval
[Attia et al., 2016]. For this study, we adapt the binary assertion format
used by ConceptNet [Speer et al., 2017] shown below:

[concept1 semantic-rel concept2]

where semantic-rel stands for the specified semantic relationship of the two
concepts ( concept1 and concept2) that contains meaning. We adapted the six
semantic relation labels from the CogALex-2016 Shared Task on Corpus-Based
Identification of Semantic Relations [Santus et al., 2016] which are Synonym
(SYN) Antonym (ANT), Hypernym (HYP), Membership (PartOf), and Random (RAND) as
shown in Table 2. In addition, we also added a few semantic labels of our own
with respect to the concepts that we will be working on this study which are
in the field of disasters. Thus, we added Performs (DO) to signify action,
Adjective (IS) to signify description, and Cause (CAUSE) and Effect (dueTo) to
signify consequences of events in a disaster setting.

###  4.3 Concept Expansion using Word Embeddings

Word embeddings are representations of an entire document vocabulary in a
mathematical vector space. Each word is represented with a set of real-valued
numbers given a specific set of dimensions. Word embedding architectures such
as Word2Vec [Mikolov et al., 2013a, Mikolov et al., 2013b] and Global Vectors
or GloVe [Pennington et al., 2014] capture various relationships of words in a
corpus such as the semantic similarity, syntactic similarity, and word co-
occurrence to name a few. Thus, if two words are commonly used together in the
same context, such as in disaster-related articles, we can expect them to be
close together when represented in the vector space. For example, finding the
most similar terms using the query word warning from a word embedding model
trained on a disaster-related dataset will output words such as typhoon,
flooding, and signal since the word warning is commonly used in texts to
notify people of possible natural disasters.

For this study, we generated a word embedding model for each year of
partitioned news article dataset from 2017 to 2019. A total of three models
were generated using the Word2Vec architecture [Mikolov et al., 2013b]. In
addition, we used the initial seedwords list which contains 150 concepts (50
for each year) as query words to extract semantically similar terms which
occur in the context of disaster.

Seedword | Semantic Label | Assertions
---|---|---
police | DO | [police DO risk]
IS | [police IS armed]
DO | [police DO commitment]
tremor | SYN | [tremor SYN aftershock]
dueTo | [tremor dueTo quake]
dueTo | [tremor dueTo earthquake]
rescue | partOf | [rescue partOf operations]
HYP | [rescue HYP retrieval]
HYP | [rescue HYP aid]
experts | IS | [experts IS supporting]
DO | [experts DO recommend]
DO | [experts DO impose]
Table 3:  Expansion of knowledge base using word embeddings.

##  5 Results

Table 3 shows the expanded ontology by querying four sample seedwords from the
word embedding model. We only filtered the top three semantically similar
resulting words from each query word that falls under a manually-annotated and
qualified semantic label discussed in Section 4.2. From this, we arrived at an
expanded ontology composed of 450 assertions for our disaster-related
knowledge base.

Figure 1: Hierarchy of hypernyms formed from the expanded ontology.

From the resulting expanded ontology, it already provides us the knowledge
that an intelligent agent can piece together or form when asked about
something in the context of disasters. Take in, for example, the word tremor.
Obtaining the top three assertions with semantic labels and expanded using
word embeddings informs us that the word tremor is synonymous and
interchangeable with the word aftershock which scientifically means the
involuntary movement of the surface due to breaking of underground rocks.
Likewise, the words quake and earthquake are annotated with the semantic label
dueTo which denotes effect since earthquakes is root cause of tremors
according to scientific definition [Yose, 2013].

Another observation from the expanded ontology is the knowledge of
understanding general actions to more specific ones. In the example, the
action word rescue is a hypernym, which means it is a general word that can be
possibly specified further, in this case, it is the hypernym of the word aid.
Consequently, the word operation is a hypernym of rescue. Combining the words
altogether, we can interpret the three assertions as a string of successive
actions as shown in Figure 1 where in an operation involves a rescue and the
meaning of rescue may vary such that it can be some form of (a) retrieval of
missing people or (b) aid for the wounded or stranded people.

Lastly, the word embedding model was able to produce words that imply
responsibilities by concepts in the form of human entities. In the two
examples of human entities shown in Table 3, experts and police, most of the
semantic labels are DO which denotes action and IS which denotes description.
For police, common known actions done in the context of disasters are risk and
commitment while being armed. For experts, the responsibilities are in the
line of supporting a claim as well as recommending and imposing future actions
based on scientific knowledge.

We observe the significant potential of using word embeddings for expanding
knowledge bases by capturing various levels of information such as (a)
understanding interchangeable context-specific words, in the case of this
study on disaster-related words, and (b) understanding roles played by human
entities described in this section. When used by an intelligent agent, it will
have an idea of what response can be produced when queried with questions such
as ”What does a police officer do?” or ”What happens after an earthquake?”.

###  5.1 Expert Validation

To properly gauge the effectivity of using word embeddings for the expansion
of knowledge bases, we performed a validation process by inviting three
experts in the fields of linguistics, disaster response, and meteorology to
evaluate the assertions of the knowledge base. Each assertion is evaluated
using a two-scale metric: Agree if the expert deems that the assertion
observes a correct relationship and semantic labelling ([doctor SYN medical
person]) or Disagree if the assertion is not properly labelled to form a
correct relationship ([flood SYN typhoon]).

Model | Aggreability Rate
---|---
Word2Vec 2019 | 0.64
Word2Vec 2018 | 0.52
Word2Vec 2017 | 0.49
Table 4:  Expert validation for the knowledge base.

Table 4 shows the averaged agreeability rate of the expert validation process.
Results show that the most accurate model with the highest rating is the
Word2Vec model trained on disaster-related news articles collected in the year
of 2019. This is followed by word embedding models using the 2018 and 2017
dataset. We attribute the semi-low agreeability scores due to the variation of
word usage of the experts in their corresponding fields of study. For example,
the assertion [tremor SYN earthquake] was evaluated by the linguist and
disaster experts as Agree while the meteorologist contested. The expert on
meteorology swears by the scientific definition of which tremors are very
different with earthquakes in a way that tremors are caused by earthquakes and
not an interchangeable term as perceived by the other two non-technical
experts. This pattern is also observed with other assertions using the synonym
labelling such as [storm SYN typhoon] and [lava SYN magma].

Seed | 2017 | 2018 | 2019
---|---|---|---
governor | [governor DO assure] | [governor IS political] | [governor DO declare]
[governor DO giving] | [governor DO oversees] | [governor DO resolution]
[governor DO explain] | [governor IS mandatory] | [governor DO develop]
experts | [experts SYN representative] | [experts DO work] | [experts IS supporting]
[experts DO checking] | [experts IS frantic] | [experts DO recommend]
[experts DO verify] | [experts DO announce] | [experts DO impose]
police | [police DO risk] | [police DO risk] | [police DO risk]
[police DO control] | [police IS armed] | [police DO study]
[police DO damage] | [police DO commitment] | [police DO alerts]
Table 5:  Roles played by human entities.  Seed | 2017 | 2018 | 2019
---|---|---|---
mayor | [mayor partOf hall] | [mayor partOf administration] | [mayor RAND workers]
[mayor partOf DWUP] | [mayor DO communication] | [mayor partOf government ]
[mayor partOf sitio] | [mayor partOf DENR] | [mayor RAND employers]
student | [student partOf PUP] | [student partOf elementary] | [student partOf organization]
[student IS victim] | [student partOf school] | [student partOf University]
[student IS resident] | [student IS minor] | [student partOf UP]
teacher | [teacher DO education] | [teacher DO research] | [teacher partOf house]
[teacher partOf DepEd] | [teacher DO education] | [teacher SYN employee]
[teacher partOf school] | [teacher partOf school] | [teacher partOf school]
Table 6:  Memberships of human entities.  Seed | 2017 | 2018 | 2019
---|---|---|---
earthquake | [earthquake IS magnitude] | [earthquake IS magnitude] | [earthquake IS magnitude]
[earthquake SYN quake] | [earthquake SYN quake] | [earthquake SYN quake]
[earthquake RAND drill] | [earthquake IS intensity] | [earthquake RAND signal]
eruption | [eruption IS amplifying] | [eruption IS happening] | [eruption IS hazardous]
[eruption IS fast] | [eruption IS explosive] | [eruption IS magmatic]
[eruption IS confirmed] | [eruption CAUSE destruction] | [eruption IS happening]
landslide | [landslide dueTo flood] | [landslide dueTo rain] | [landslide RAND mountain]
[landslide HYP mudslide] | [landslide IS torrential] | [landslide dueTo rains]
[landslide RAND area] | [landslide HYP mudslide] | [landslide IS widespread]
typhoon | [typhoon IS expected] | [typhoon SYN storm] | [typhoon dueTo Amihan]
[typhoon partOf calamity] | [typhoon IS super] | [typhoon SYN hurricane]
[typhoon SYN onslaught] | [typhoon IS powerful] | [typhoon SYN cyclone]
Table 7:  Disaster-related terms and its associated words.

##  6 Discussion

In this section, we perform an even more in-depth analysis by considering the
changes in semantic meaning or semantic information of the assertions of the
knowledge base over time. We break the discussion into three categories we
observed from the analysis.

###  6.1 Roles Played by Human Entities

We observe the changing roles played by three essential human entities,
governor, experts, and police over time in the setting of a natural disaster
as written in news articles. These entities are expected to be on full alert
and their responsibilities are crucial towards mitigating the consequences of
disasters.

The entity governor, or the highest commanding individual of Philippine
province, performs various roles over time. As seen in Table 5, the entity is
mostly connected by the semantic label DO to denote action with concepts such
as assure, explain, oversee, develop, and declare. There are also a few
description words connected by the label IS such as political and mandatory
which is obvious since holding a gubernatorial position is indeed political
and resolutions filed in a governor’s office is in its essence mandatory.

Similarly, the entity experts is also expected to have various changing roles.
For 2017 as seen in the Table, common action words associated are checking and
verifying which provides one of the most important roles of experts in the
field of disasters: validating the integrity of information being publicized.
For 2018, experts play more of an information dissemination role with the
concept announce as the connecting action word. In 2019, experts assumed a
more stricter role as observed with the connected concepts such as recommend
and impose.

For the entity police, there is consistent associated descriptor across the
years regarding their responsibility: risk. This provides us a concrete idea
of a consequence when assuming the role of a police. In 2017 and 2018, strong
action words such as control, damage, and armed are tied with a policeman’s
job. In 2019, however, the entity police assumed a more passive role as more
of an informant with the connected action words being study and alerts.

###  6.2 Memberships of Human Entities

We also observed changes in memberships of human entities in context of
disasters over time as seen in Table 6. Memberships are denoted by the
semantic labels HYP for hypernyms or general words and partOf for hyponyms
more specific words. We observe memberships played of three entities: mayor,
student, teacher.

For the entity mayor, memberships are more specific in 2017 and 2018. The
entity is expected to partner with small groups in a municipality such as a
sitio or a barangay cite as well as with large government agencies such as
Division on the Welfare of the Urban Poor (DWUP) and Department of Environment
and Natural Resources (DENR) in times of disasters. There are also general
memberships such as administration and government to which the entity is an
obvious member.

In the case of the entity student, there is a mix of specific and generalized
memberships. Certain specific universities such as UP or the University of the
Philippines and PUP or Polytechnic University of the Philippines were
classified as institutions where a student may belong. General and obvious
concepts such as elementary, school, organization and University contribute to
the commonsense information of the knowledge base.

For the entity teacher, general concepts of memberships are more prominent
compared to the other two entities. The concept school is consistent for all
years. The term DepEd which means Department of Education, the government
agency responsible in shaping the educational landscape of the Philippines, is
connected to the entity. DepEd oversees elementary and intermediate level
schools which may mean that the term teacher is commonly tied with educators
from these levels. Interestingly, the concept house is also tied with the
entity teacher. Although it may already be obvious that a teacher assumes a
different role inside the house maybe as a mother or a breadwinner.

###  6.3 Common Word Association of Disaster-Related Terms

For the last category, we observe change in association of disaster-related
words over time as seen in Table 7. We highlight the importance of this
analysis to understand how a knowledge base using information extracted from
word embeddings interchange co-occurring and similar words in the context of
disasters. For this category, we analyze the four frequently occurring natural
disasters in the Philippines which are earthquakes, eruption, landslide, and
typhoon.

The first disaster-related word is earthquake. From the formed assertions for
all years, earthquakes are synonymous with the term quakes which denote a
shortened version of the word. In addition, the term magnitude is also a
consistent term connected to the seedword earthquake which tells us that
earthquakes have their own corresponding magnitudes quantified by some number.

In the case of eruption, most assertions formed are used with the semantic
label IS to denote description or property. Across the years, most of the
descriptive words associated with the term are negative such as amplifying,
hazardous, destruction, explosive, and magmatic. These word may denote a sense
of urgency compared to the other natural disasters when it happens. The term
happening has occurred both in 2018 and 2019 due to two of the most active
volcanoes in the Philippines, Mount Mayon and Mount Taal, had shown
activity444Volcano Bulletin: phivolcs.dost.gov.ph/index.php/volcano-
hazard/volcano-bulletins3 by erupting successively and spewing ash. The
eruption caused mass postponement of public activities and over 48,000
evacuated locals.

For the concept landslide, the common associated word is mudslide. Although by
scientific definition, mudslides are as specific type landslides (also called
debris flow). Thus, the landslide term conforms the the semantic label HYP for
hypernyms. The cause of landslides can be tied to floods, which in turn,
caused by rains as joined by the semantic label dueTo denoting a consequence
or an effect. In addition, landslides are also often described using the words
widespread and torrential which gives us a quantifiable idea of the magnitude
of landslides that occur in the Philippines.

Lastly, we have the word typhoon. This disaster-related concept assumes many
interchangeable and synonymous terms such as storm, hurricane, cyclone, and
onslaught. We note the frequent association and interchangeability of these
words due to geographic locations [Khadka, 2018] but may mean the same
thing—they are all tropical storms. Likewise, description words tied to the
concept typhoon are expected, super, and powerful which also provides us an
idea of the magnitude of typhoons occurring in the country similar to
landslides.

##  7 Conclusion

As a disaster-prone country, the Philippines should increase its efforts in
mitigating the effects of natural calamities with the help of technology. One
way to do this is to consider the potential of intelligent agents such as
chatbots as tools for disaster preparedness and information dissemination. In
this study, we established a simple context-specific knowledge base by doing
three important processes: (a) extracting disaster-related concepts from a
collected news article dataset, (b) building a network of binary assertions
from a curated list of semantic labels, and (c) expanding the ontology by
querying an initial seedwords list from word embeddings generated from the
original news dataset. Results show that using word embeddings captured
various levels of information that may be useful for intelligent agents to
produce responses such as information on roles of human entities,
generalization and specification of terms, and common word association when
asked in the topic of disasters. Future directions of the study include
collection of even more dataset that covers not only news articles but also
other media for finer-grained assertions. In addition, the study will also
benefit from efforts in testing the capability of the knowledge base in
practical applications.

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