Earthquake LOD: Seismic Dataset Construction
with Ontology Oriented Design Patterns
Hiroki Uematsu∗

hiroki_u@nii.ac.jp
The Graduate University for Advanced Studies,
SOKENDAI
Hayama, Kanagawa, Japan
National Institute of Informatics
Chiyoda-ku, Tokyo, Japan

ABSTRACTThis paper describes the construction of an Earthquake Linked
Open Data (LOD) for seismological datasets using Ontology Oriented Design Patterns. In recent years, various studies have focusedon utilizing machine learning technology to detect, classify, or predict seismic intensities based on vast amounts of observed seismic
waveform data. Researchers need to collect hypocenter information, time of occurrence, and target observation stations related
to seismic waveforms to compile data for machine learning purposes. Although seismic waveform datasets for machine learning
are widely available worldwide, accessing waveform data observed
by Japanese seismic networks is limited, and metadata retrieval isdifficult. To address this, we developed the Earthquake LOD with
Ontology Oriented Design Patterns to enhance the discovery andretrieval of seismic data. Ontology Oriented Design Patterns areused to construct interoperable knowledge graphs. This method
categorizes tasks for humans and machines using a domain-specificknowledge usage checklist, enabling the efficient creation of valuable knowledge graphs. In this paper, we provide a detailed accountof constructing the Earthquake LOD for seismic datasets from both
Japan and overseas, utilizing Ontology Oriented Design Patterns.

KEYWORDS
Ontology, Linked Open Data, Ontology Oriented Design Patterns,
Earthquake
ACM Reference Format:
Hiroki Uematsu and Hideaki Takeda. 2023. Earthquake LOD: Seismic Dataset
Construction with Ontology Oriented Design Patterns. In Proceedings of
The 12th International Joint Conference on Knowledge Graphs (IJCKG 2023)(Conference IJCKG ’23). ACM, New York, NY, USA, 9 pages. https://doi.org/
XXXXXXX.XXXXXXX

1INTRODUCTION

Japan is one of the most earthquake-prone countries in the world.
Around the Japanese archipelago, four plates collide with each
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Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japan© 2023 Association for Computing Machinery.
ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00
https://doi.org/XXXXXXX.XXXXXXX

Hideaki Takeda

takeda@nii.ac.jp
National Institute of Informatics
Chiyoda-ku, Tokyo, JapanThe Graduate University for Advanced Studies,
SOKENDAI
Hayama, Kanagawa, Japanother, and more than 100,000 earthquakes occur per year, averaging
more than 300 earthquakes per day, including those that are not felt.
Although Japan accounts for about 0.25% of the world’s land area,
earthquakes exceeding magnitude 6 have occurred nearly 200 times
in 10 years, accounting for 20% of all earthquakes in the world.
Seismic motion is observed as waveform data of acceleration andis used in various research such as calculation of seismic intensity,
determination of hypocenter, emergency earthquake warning, andpredicted seismic intensity. In recent years, it has been used astraining data for research using machine learning, such as predicting the seismic intensity at a specific observation station, whether
the observed waveform is a seismic waveform, and identifying theP-wave/S-wave of an earthquake. Since machine learning requiresa large amount of high-quality training data, seismic observation
networks are useful. However, one of the networks K-NET[7] which
was established by the National Research Institute for Earth Scienceand Disaster Resilience (NIED) waveform data acquisition site does
not have an API, users need to specify the date and time, hypocenter, observation station, etc., and download the waveform data. In
order to search for waveform data independently observed by researchers and observation networks of the Japan Meteorological
Agency (JMA) and local governments, it is possible to create a database that aggregates waveform data. Although, since the waveform
data cannot be republished and there is no URI that uniquely points
to the waveform data, researchers will have their own databases,
making it difficult to create a reusable open waveform database.
Therefore, we will collect metadata of "earthquakes" such as
information on observation stations where earthquake ground motions were observed, seismic intensity, observation time, hypocenter
position, and magnitude estimated from observed waveforms, andpublish them in the form of Linked Data. In Linked Data, the link
structure of things and concepts is represented by a model called
triple using RDF, and data can be traced by following properties
links in the same way as following links on websites. Here, website links indicate simple connections, but in Linked Data triples,
properties that become links also have URIs and indicate link relationships.
In this research, we newly define the vocabulary related to seismic motion such as seismic source, seismic intensity, and observation time, and the vocabulary related to observation stations as
properties specialized for earthquake data. For example, ’Earthquake motion’ has vocabularies such as ’Seismic intensity’, ’Magnitude’, and ’Occurrence time’ as properties. In addition, by using
the term ’observed waveform’ as a property, triples of waveforms

Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japan

Hiroki Uematsu and Hideaki Takeda

obtained by observing a certain seismic motion are created. can be
expressed.

2

EARTHQUAKE OBSERVATION

In general, the word "earthquake" refers to events such as tremors
felt by people on their own, but in reality, it refers to the rapid
displacement of the bedrock due to the pushing and pulling of
the underground bedrock. Shaking occurs as a result of bedrock
displacement and is recognized by us at the ground surface. The
shaking is transmitted to the ground surface as waves, and themagnitude of the shaking observed by seismometers is expressedas the seismic intensity in Japan. The difference in the velocity of
the P and S waves of the seismic waveform observed at each station is used to estimate the hypocenter of the earthquake. Because
earthquakes occur underground, it is difficult to actually observethem. Therefore, information on the waveforms observed at eachobservation station is important, such as the hypocenter estimationand calculating the seismic intensity.
Observations of seismic activity are conducted in many countries. The International Federation of Digital Seismograph Networks(FDSN)[24] has 2196 registered seismograph networks with 24-bit
resolution with data recorded in continuous time series at a sampling rate of at least 20 samples/second are registered. STEAD[12]registers approximately 1.2 million time series of seismic waveforms observed by seismometers, covering more than 19,000 hoursof datasets. These datasets include data observed by seismic networks created by academic institutions such as the International
Seismological Center (ISC)1 and the United States Geological Surve,
as well as data from seismometers installed at home by individuals
such as Raspberry Shake[22]. The data sets contain a variety of
data, including data observed by seismometers installed at home,such as Raspberry Shake.
In Japan, seismic waveforms observed by observation networks
such as K-NET and Kik-net, which are based on data from observation stations established by NIED, JMA, and local governments,
can be obtained. However, although the acquired data can be used
for analysis and other purposes, it cannot be redistributed, andonly some of the JMA’s observation station and data are registeredin the FDSN. The JMA releases observation data only for major
earthquakes as strong-motion earthquake observation data2 . On
the other hand, the seismic intensity database search 3 allows users
to search for earthquakes on a map by date and time of occurrence,
seismic intensity, seismic intensity observed at each observation
station from the observation station, hypocenter and depth, andmagnitude. Other earthquake data is available in the Earthquake
Monthly Report (Catalog Edition)[9]. In addition, the hypocentersof earthquakes up to several years before the hypocenters were
determined are available on the hypocenter data 4 of the Earthquake Monthly Report (Catalog Edition). Although the observed
waveforms themselves cannot be obtained, they can be considered
to contain metadata on the observed seismic motions.
Using metadata of observed waveforms and seismic motions,various studies have been conducted not only to estimate seismic
1 http://www.isc.ac.uk/

2 https://www.data.jma.go.jp/eqev/data/kyoshin/jishin/
3 https://www.data.jma.go.jp/eqdb/data/shindo/

4 https://www.data.jma.go.jp/eqev/data/bulletin/hypo.html

Figure 1: Data Download after Search for Data | K-NET

intensity and hypocenter but also to calculate predicted seismic
intensity, and classify earthquakes and earthquake early warning
systems. For example, for real-time seismic intensity estimation[10],data from stations that have observed the same earthquake areused as training data for learning. When downloading data using
K-NET of NIED, it is necessary to find earthquake waveforms byusing queries such as "search by observation station," "search by
hypocenter," or "search by record start time. Figure 1 is a form forsearching and downloading data from K-NET. You can retrieve
the necessary data by specifying the observation stations and theperiod that includes the earthquake occurrence time.
However, there is no list of which stations observe which earthquakes, although multiple stations must observe the same earthquake to be selected when searching by the station. In addition,
when searching from the hypocenter, it is not known whether theobservation stations observed the earthquake that occurred at that
hypocenter or not without searching the data and making a list.
Furthermore, since the observation stations are different from eachother, it is difficult to retrieve the seismograms of earthquakes that
occurred at the same hypocenter from multiple observation stations
because the IDs are not assigned to each earthquake.

3

METHOD

To solve the problem in section 2, we aim to make the observed
waveforms publicly available and searchable in the form of Linked
Data, which links data together.
Uematsu et al.[27] showed the flow of determining the schema
for improving data interoperability, transforming data, designingconnections to other data, and registering the data in Wikidata.
Using corporate databases distributed in Japan and insurance medical and pharmacy institutions as examples, they aim to make data
distributed in XML, Excel, PDF, etc. reusable as a base registry.
To improve interoperability, it is important to facilitate reuseof frequently used schemas and connection with data from otherdomains. For example, it is easy to understand data and connect
to Wikidata and DBpedia by using schemas defined in Wikidata,
DBpedia, schema.org, and also as well as owl, rdf, and SKOS. In
addition, by using LOV or DBpedia Archivo to search for ontologies

Earthquake LOD: Seismic Dataset Construction
with Ontology Oriented Design Patterns

in the same domain, and if there are data or services that utilize
those ontologies or schemas, the data can be interconnected andutilized by using a standard schema. It is natural to use a common
vocabulary such as place names, latitude/longitude, time, etc. In
addition, if there is a vocabulary such as earthquake or seismic
intensity, it should be possible to combine and use data related toearthquakes that are already available to the public by using thesame vocabulary.
On the other hand, it is important not only to use a common vocabulary but also to use a schema that is specific to that domain andcan correctly represent the nature of the data. The same vocabulary
may have different usages and meanings in various domains.
In such cases, it is necessary to create a schema tailored to thedata to be converted to Linked Data, rather than using an existing
ontology or vocabulary. Linked Data conversion is difficult without
understanding the original data structure, ontology, and schema,
and is often constructed empirically. In addition, simple methods
are used, such as anyLink5 and CSV2LOD6 , which first determine
the mapping to an existing schema based on whether or not itmatches the Resource or Property name.
Therefore, we organized these flows and created design patterns for reading data, checking domains, matching with existing
schemas, and further data cleansing. In addition, by drawing attention to the ontology and vocabulary used to express the meaningand structure of the data, it is possible to understand the data andfind the appropriate vocabulary.

Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japandomain of the data based on the ontology, correcting missing dataor formatting, and checking for uniqueness to connect with external
data. Each item and its task is itemized below.
(1) Collect original data
• PDF, Excel, CSV/TSV, RDF, etc.
• Convert to machine-readable format(2) Design data structure with ontology orientation• Automatic or manual processing• Use machine support as much as possible for manual processing• check domain of data
– find the same domain or suitable ontologies and vocabularies– create the suitable ontology• Existing domain or vocabularies– use existing vocabularies, schemas(3) Normalize• Remove unnecessary characters• Convert full-width to half-width(4) Restore missing data
• Specify domain in Wikidata and Wikipedia and so on• Restore missing data(5) Match columns between data of the same domain• Perform column matching between data to be unified• Create a mapping table manually• Evaluate the uniqueness of columns used for unification– Evaluate uniqueness and completeness
By using this design pattern, users who do not have empiricalknowledge can follow the flow to create highly interoperable data.
In particular, the flow clarifies which parts can be handled mechanically which need to be handled manually, and which tasks the user
should focus on. Among these, focusing on ontology will help users
understand the data and the domain of the data, and enable themto use vocabulary and schemas that have meaning appropriate to
the data, rather than just schema mapping.
In the following sections, we present the procedure for converting seismic data to LOD using the ontology oriented design
patterns.

4
Figure 2: Ontology Oriented Design Patterns
Figure 2 shows simple data design flow using Ontology Oriented
Design Patterns. The five checks indicated by the green boxes,"Is machine readable?", "New domain?", "Reuse existed schema?","Formatted, excess or missing data?", "All data are unique?" If theanswer is "yes," the data can be processed by the machine as is, but
if the answer is "no," a human must check the contents of the dataand process it. For example, in the case of machine-unreadable data
created in PDF or Excel, the file format should be converted or thedata contained in a single cell should be split. If the existing schema
cannot be reused, a new schema should be created according to the
ontology of the relevant data. Thus, the design pattern is largely
a flow of reading the acquired data, checking and designing the
5 http://link.lodosaka.jp/

6 https://koujikozaki.github.io/CSV2LOD/

EARTHQUAKE ONTOLOGYFirst, we organized the vocabulary related to earthquakes. The
JMA’s Earthquake Monthly Report (Catalog Edition) does not provide data on observed waveforms, but it does provide metadata on
observed earthquake ground motions.
On the other hand, an example of describing USGS Earthquake
data utilizing the SOSA (Sensor, Observation, Sample, and Actuator) ontology of the SSN (Semantic Sensor Network)[5] has been
published7 . This description example shows the acceleration of
the earth’s surface observed by a sensor that is an instance of
SOSA:Sensor when the earth is the observation target. Since thestructure in which sensors detect tremors by observing the acceleration of the earth’s surface and record the waveform is the structureof information used to measure seismic intensity and to estimate
the hypocenter, the Earthquake Ontology to be constructed in thisstudy will also be constructed utilizing SOSA. However, since there
7 https://www.w3.org/TR/vocab-ssn/integrated/examples/seismograph.ttl

Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japanis no property that expresses the relationship between the observed
waveforms and the seismic intensity or hypocenter, it is necessary
to create a new property. In particular, the seismic intensity widelyused in Japan is based on the JMA seismic intensity scale definedby the Japan Meteorological Agency, which requires the creationof unique properties.
Similarly, VEO (Volcano Event Ontology)[6, 23] is an ontologythat utilizes SOSA and SSN. The VEO is intended to describe data
observed using IoT systems for seismic motions caused by volcanic
activity. In addition, it is used for machine learning-based classification to identify seismic events (underwater explosions, quarry
blasts, and thunders).
SBEO (Smart Building Evacuation Ontology)[21] defines theurgency, severity, and intensity of natural disasters such as earthquakes and tsunamis, and human disasters such as terrorism andkidnapping as contexts, and builds an ontology for route recommendation systems to evacuate buildings.
As several examples show, "Earthquake" is defined as an ontology or vocabulary and applied to each case. However, it can beseen that what the term "Earthquake" expresses differs dependingon the ontology, application, and user. The SOSA example and
VEO indicate ground shaking, while SBEO indicates natural disasters. In Japan, the terms "jishin-dou", "jishin", and "shin-sai" areused interchangeably, although the English term would be "Earthquake". Earthquakes in Japan, as discussed in Section 2, refer tophenomena of bedrock displacement, and there is not always a single hypocenter or seismic motion that is estimated to be the sourceof the earthquake. Therefore, prominent seismic activity is summarized as major damaging earthquakes that occurred in Japan8. In addition, earthquake disaster is defined as "damage directly
caused by seismic motion and damage caused by tsunamis, fires,
explosions, and other unusual phenomena that occur as a result ofsuch motion,"9 and is distinguished from earthquake and seismic
motion. However, Wikidata’s Entity is listed as "Earthquake", as in
the Great East Japan Earthquake10 .
Since each of these concepts has a different meaning, existing
ontologies and schemas with the vocabulary earthquake cannot
correctly represent the concept. In this study, "jishin-dou" as seismic motion, "jishin" as Earthquake, and "shin-sai" as Earthquake
disaster are used as separate concepts. However, the list of seismic motions that include earthquakes and earthquake disasters
is not fully published in Japan, although the definitions of eachconcept are different. Therefore, we first created an ontology as anearthquake vocabulary based on the Earthquake Monthly Report(Catalog Edition) of JMA, which records metadata about seismic
motions, such as hypocenters, intensity and observation station.
The data in the Earthquake Monthly Report (Catalog Edition)
include source data, measured data, first motion mechanism solution data, CMT solution data, seismic intensity data, tsunami data,etc. In this paper, the seismic intensity data file was first selected as
the target. The seismic intensity data contains a record called the
hypocenter record, which contains information on the hypocenter,and information on the observation stations where the earthquake
8 https://www.data.jma.go.jp/eqev/data/higai/higai1996-new.html

9 https://elaws.e-gov.go.jp/document?lawid=353AC0000000073&openerCode=1

10 https://www.wikidata.org/wiki/Q36204

Hiroki Uematsu and Hideaki Takeda

motion that occurred at the hypocenter was observed. Figure 3 is apart of the Earthquake Monthly Report (Catalog Edition).

Figure 3: Earthquake Monthly Report (Catalog Edition),2019.05
First, the hypocenter record is listed, and the number at the endof this record is the number of stations that observed the earthquake.
The number at the end of the record is the number of observation
points that observed the earthquake. In the example, the number is
19, so 19 lines of information on the seismic intensity and acceleration observed at the observation points are listed from the nextline of the seismic source record. Both the hypocenter, seismic intensity, and acceleration records are fixed-length data, and include
the location and depth of the hypocenter, the magnitude, the timeof occurrence (origin time), the observation point number, and thetime when the earthquake was determined to have occurred at theobservation point (trigger time). The Earthquake Monthly Report(catalog section) contains metadata recorded regarding tremors
observed at hypocenters and observation stations. To convert metadata related to earthquakes into RDF, instead of focusing on theacceleration waveforms themselves, we constructed an ontology byutilizing the column names used in the seismic source and intensity
records.
Listing 1: jp-earthquake.ttl
1 PREFIX xsd : < http :// www . w3 . org /2001/
XMLSchema #>2 PREFIX rdf : < http :// www . w3 . org/1999/02/22 - rdf - syntax - ns #>3 PREFIX rdfs : < http :// www . w3 . org /2000/01/
rdf - schema #>4 PREFIX owl : < http :// www . w3 . org /2002/07/
owl #>
5 PREFIX schema : < http :// schema . org / >6 PREFIX dcterms : < http :// purl . org / dc / terms/>
7 PREFIX foaf : < http :// xmlns . com / foaf /0.1/ >8 PREFIX sosa : < http :// www . w3 . org / ns / sosa / >9 PREFIX ssn : < http :// www . w3 . org / ns / ssn / >10
11 PREFIX jpe : < https :// seismic . balog . jp /ontology /jp - earthquake . ttl #>Earthquake LOD: Seismic Dataset Construction
with Ontology Oriented Design Patterns

12 < https :// seismic . balog . jp / ontology /jp earthquake . ttl #>13aowl : Ontology ;14rdfs : label " earthquake ontology forseismic dataset " @en ;15
dcterms : creator [foaf : homepage<
https :// seismic . balog . jp /> ;16
foaf : name"hiroki_u " ;
17
] ;
18
dcterms : issued"2022 -10 -16" ;19dcterms : modified"2023 -09 -18" .
20
21 jpe : earthquake aowl : Class ;22
rdfs : label " earthquake " @en ;23
rdfs : subClassOf schema : Event .
24
25 jpe : observeraowl : Class ;26
rdfs : label " Station " @en ;27
rdfs : subClassOf sosa : Sensor .
28
29 jpe : seismicMotionasosa : Observation;30
rdfs : label " seismic motion " @en ;31
jpe : shindo rdf : literal ;32
jpe : hasHypocenter jpe : hypocenter ;33
sosa : isObservedBy jpe : observer .
34
35 jpe : observedWaveasosa : Observation ;36
rdfs : label " observed wave " @en ;37
sosa : isObservedBy jpe : observer .
38
39 jpe : hypocenteraowl : Class ;40
rdfs : label " hypocenter " @en ;41
rdfs : subClassOf schema : Event .
42
43 jpe : hypocenteraowl : Class ;44
rdfs : label " hypocenter " @en ;45
rdfs : subClassOf schema : Event .
46
47 jpe : shindo asosa : ObservableProperty ;48
rdfs : label " intensity " @en .
49
50 jpe : originTime ardf : Property ;51
rdfs : label " origin time " @en .
52
53 jpe : magnitude a
rdf : Property ;54
rdfs : label" magnitude " @en ;55
rdfs : subPropertyOf schema : value .
56
57 jpe : magnitudeType ardf : Property ;58
rdfs : label" magnitude type " @en ;59
rdfs : subPropertyOf schema : value .
60
61 jpe : depth a
rdf : Property ;62
rdfs : label " depth " @en ;63
rdfs : subPropertyOf schema : value .

Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japan

64
65 jpe : seismicIntensity asosa :
ObservableProperty ;66
rdfs : label " Instrumental Seismic
Intensity " @en ;67
rdf : subPropertyOf schema : value .
68 ...

Figure 4: Earthquake Ontology
A graph created based on the vocabulary related to earthquakes,
which is mainly used, is shown in Figure 4.
Since the earthquake itself cannot be observed, it is important toshow the relationship between the waveform information actuallyobserved at the observation station and the hypocenter and magnitude estimated from the observed waveform as the semantics of theearthquake. The earthquake ontology was constructed based on the
hypocenter, seismic motion, observed waveforms, and observation
station that constitute an earthquake. The seismic motion and theobservation station that observes the waveforms at the ground surface were described using the SOSA and SSN. SSN is an ontology
for describing and sharing sensor data, focusing on modeling SSN
sensor networks and providing terms and relationships to describe
sensor characteristics, capabilities, and observed data. SOSA provides terms and relationships to describe sensor data and related
elements such as sensors, observations, samples, and actuators. The
earthquake ontology observation station class inherits from theConference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japan

Figure 5: Earthquake data in Hiroshima on May 25, 2019
SOSA:Sensor class, and seismicMotion and observedWave are set
to the properties observed from the observation station. This isbecause seismic motion and observed waveform are two different
classes, and observation stations, especially seismometers installedat home by individuals such as CSN or Raspberry Shake, may falsely
detect non-seismic motion such as movement of people or pets,
truck or bus traffic, etc., resulting in non-seismic waveforms being
observed. In the earthquake ontology, the hypocenter is identified
from the seismic waveforms observed by the stations, and the data
set summarizing these three relationships is intended to be captured
as an earthquake.
Figure 5 shows the hypocenter and observed waveforms graphically.
It can be seen that one earthquake is indicated by the waveform
observed at the observation point and the hypocenter estimatedfrom the observed waveform.

5

EARTHQUAKE LOD

We converted the available data from the JMA’s Earthquake Monthly
Report (Catalog Edition) and FDSN earthquake events into Linked
Data based on Earthquake Ontology. The data in the JMA’s Earthquake Monthly Report (Catalog Edition) is written in a fixed-length
format with the character code Shift-JIS. In addition, informationsuch as date-time, seismic intensity, magnitude, etc. is recorded
in a fixed-length format for each digit in the format, as describedon the JMA’s Earthquake Monthly Report (Catalog Edition) website11 . Therefore, according to the design pattern, the LOD is created through the process of data normalization including format
conversion and character code change.
Since FDSN includes observation networks registered with ISC
and STEAD, data outside Japan are using FDSN. The FDSN has an
11 https://www.data.jma.go.jp/eqev/data/bulletin/data/shindo/format_j.txt

Hiroki Uematsu and Hideaki Takeda

API12 that can retrieve a list of stations and a list of hypocenters
for each registered network. Since the data of seismic waveforms
observed at each station must be collected within the site of the network, this time the data of hypocenters and stations were collectedfrom the API and converted to turtle format using the Earthquake
Ontology.
The earthquake ontology and the converted data from the JMA
earthquake monthly report (catalog) and FDSN earthquake eventare available at Citizen Seismology Network (CSN), which is anearthquake observation network for Yokohama citizens operated by
Yokohama City University13 , and data retrieval through SPARQL
endpoints (https://seismic.balog.jp/sparql).
For example, among the earthquakes that occurred after 2018with a maximum seismic intensity of 5-upper observed, the observed waveforms with an instrumental seismic intensity of 4 orhigher and the hypocenters of the earthquakes were converted
to LOD data using the earthquake ontology. An example query is
shown below 2. In the case of JMA’s Earthquake Monthly Report
and K-NET’s download site, it is possible to search for observed
waveforms from earthquakes and data observed by stations, butthey do not support complex queries. To perform complex searches,it was necessary to download the data by specifying the earthquake,read the file, and create a local database for the search. However,using Earthquake LOD and SPARQL Query, flexible searches are
now possible.
Listing 2: The earthquakes that occurred after 2018 with amaximum seismic intensity of 5-upper observed
1 PREFIX jpe : < https :// seismic . balog . jp /ontology /jp - earthquake . ttl #>2
3 SELECT DISTINCT * WHERE {4?s a < https :// seismic . balog . jp / ontology/jp - earthquake . ttl # hypocenter > .
5?s jpe : originTime ? origin .
6?s jpe : shindo ? shindo .
7
FILTER ( xsd : dateTime (? origin ) >"2018 -01 -01 T00 :00:00"^^ xsd :dateTime )8
FILTER CONTAINS ( xsd : string (? shindo ) ,5)9? obs_wave jpe : hasHypocenter ?s ;10
jpe : observedBy ? sta .
11? sta rdfs : labal ? name .
12 } LIMIT 100

5.1

Dataset in Earthquake LOD

This section describes the Earthquake LOD datasets. The Earthquake LOD was created using various seismological network dataregistered on FDSN and data from the JMA.
• United States National Seismic Network[2]• Hawaiian Volcano Observatory Network[8]• Montana Regional Seismic Network[15]• Southern California Seismic Network[17]12 https://www.fdsn.org/webservices/
13 https://seismic.balog.jp/

Earthquake LOD: Seismic Dataset Construction
with Ontology Oriented Design Patterns

Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japan• Nevada Seismic Network[28]• Pacific Northwest Seismic Network - University of Washington[19]• USGS Northern California Seismic Network[20]• Alaska Geophysical Network[1]• Oklahoma Seismic Network[25]• University of Utah Regional Seismic Network[18]• Raspberry Shake[22]• Alaska Volcano Observatory[13]• Texas Seismological Network[4]• Puerto Rico Seismic Network & Puerto Rico Strong Motion
Program[16]• US Geological Survey Networks[3]• Lamont-Doherty Cooperative Seismographic Network[11]• Geological Survey Networks[26]• National Tsunami Warning Center Alaska Seismic Network[14]Our earthquake LOD is available at seismic.balog.jp and can besearched through the SPARQL endpoint (https://seismic.balog.jp/
sparql).
FDSN has data registered until 2022, including stations not currently in operation, while the JMA has data from 1919 to 2019. Thisis because the JMA data is based on the Earthquake Monthly Report(catalog version), which is published only after the hypocenter isdetermined and other processes are completed, and the data is not
updated. In this paper, FDSN data since 1970 and JMA data from1919 to 2019 were collected and converted to LOD. Table 1 shows
statistics of the Earthquake LOD.
Table 1: Number of Hypocenters and Stations
Organization
FDSN
JMA

Hypocenters1602972100740Stations
60646
6795

Figure 7: Hypocenter map from 2010
Table 2: Number of hypocenters over magnitude 7
Year
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010

Hypocenters
10
10
18
9
18
33
31
42
23
19
23
24
44
27

Table 2 shows the estimated number of hypocenters observed
since 2010 with a magnitude of 7 or more. Although there arevariations from year to year, we can see that 2011, when the Great
East Japan Earthquake occurred in Japan, had the largest numberof hypocenters with a magnitude of 7 or higher.
Listing 3: All hypocenters from 2010

Figure 6: all Stations
All Stations are plotted on the map in Figure 6. STEAD, ISC, and
FDSN only display data outside of Japan, but using Earthquake
LOD makes it possible to use more than 65,000 observation stations,
including those in Japan.
Figure 7 shows the locations of hypocenters of magnitude 7
or greater that have occurred since 2010. It can be seen that the
hypocenters are located along the plate and that they are concentrated in Japan.

1 PREFIX jpe : < https :// seismic . balog . jp /ontology /jp - earthquake . ttl #>2
3 SELECT year ( xsd : dateTime (? origin )) COUNT(*) AS ? cnt WHERE {4?s a jpe : hypocenter ;5
jpe : originTime ? origin ;6
jpe : magnitude ? mag .
7
FILTER ( xsd : dateTime (? origin ) >"2010 -01 -01 T00 :00:00"^^ xsd :dateTime )8
FILTER (? mag >= 7)9 } GROUP BY year ( xsd : dateTime (? origin ) )10
ORDER BY DESC ( year ( xsd : dateTime (? origin)))

Conference IJCKG ’23, Dec 08–09, 2023, Miraikan, Tokyo, Japan

Plotting hypocenters by their own latitude and longitude on themap, we can be seen that are located along the plate and that they
are concentrated in Japan. These data can be retrieved with thefollowing SPARQL Query3.

6

CONCLUSION

In this paper, we organized earthquake data sourced from the Japan
Meteorological Agency’s Earthquake Monthly Report and the FDSN
dataset. Additionally, we developed the Earthquake Ontology using Ontology Oriented Design Patterns. The Earthquake Ontology
consists of a vocabulary related to earthquakes and has been published in the form of Linked Open Data. This was achieved byassigning URIs to earthquakes based on information from observed
waveforms and hypocenters.
At first, utilizing the ontology oriented design patterns, we defined the phenomenon of earthquakes, which is challenging to observe directly. This led to the creation of an earthquake LOD. Afterdiscovering data that should be converted to improve interoperability, it is useful to reuse existing ontologies and schemas in order toeasily convert the data into LOD. Comprehending the original datais crucial for ontology discovery, validation, and the conversion
of existing data. This complexity makes automation challenging
and requires careful consideration of the workflow. In this paper,
we show that it is possible to create seismic ontologies and seismic
LODs by applying design patterns to the example of earthquake
data that occur frequently in Japan. This paper demonstrates thefeasibility of creating Earthquake Ontology and Earthquake LODs
through the application of design patterns, using the example offrequently occurring earthquake data in Japan. We also aim to buildgraphical platforms and graphical applications for using design
patterns.
Next steps, we aim to create an infrastructure for multiple observation networks by converting the latest data published in the
JMA’s seismic intensity database, data in NIED’s seismic observation network, and seismic data in FDSN into LOD. Furthermore, thedataset will be extended by discovering the observed waveforms
that observed the hypocenters registered in the FDSN and linking
them with the source data and the observation station data. Observed seismic waveforms can be obtained from the data catalog of
the network registered in the FDSN and the site of NIED, where observation data is publicly available, but it is necessary to constructa query based on the information of the hypocenter. Therefore,making it possible to generate parameter settings for acquisition
from the Earthquake LOD created this time would promote data
distribution for seismic research. By using the Earthquake Ontology
to describe data from proprietary observation networks such as
CSN, seismometers developed by individuals, and data from seismometers installed in smart homes, which are difficult to releasein normal times, it will be possible to reuse many data in case of
emergency. Furthermore, we will promote the availability of anearthquake catalog format that can be used as earthquake data andlearning data, and LOD conversion of data observed by our own
observation network. Benchmarking based on the same dataset isimportant for source determination, calculation of predicted seismic
intensity, and training data for machine learning, but it is believedthat datasets for reproduction are not distributed due to the fact

Hiroki Uematsu and Hideaki Takeda

that Japanese data cannot be redistributed and IDs are not assigned.
By using the Earthquake ontology created in this paper to describe
the datasets used in earthquake research in LOD, it is expected thatthe availability of datasets for reconstruction will be improved.
On the other hand, while the Earthquake Ontology and Earthquake LOD developed in this study have been made available to
the Japanese seismic community, their practical implementation isstill pending. One contributing factor is the prevalent use of older
systems, such as fixed-length data in Shift-JIS format. In the future,
we aim to gain acceptance in the seismic community by developing a system to convert various seismic data into LOD and otherutilization infrastructures in conjunction with the dissemination of
the Earthquake LOD developed in this study.

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