AN ONTOLOGY FOR A HYDRO-METEOROLOGICAL

OBSERVATION NETWORK

Lúcia Morais Kinceler

1,2

, Angelo Mendes Massignam

and José Leomar Todesco

Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Universidade Federal de Santa Catarina

Campus Universitário, Florianópolis/SC, Brazil

Empresa de Pesquisa Agropecuária e Extensão Rural - Epagri, Rod. Admar Gonzaga, 1347, Florianópolis/SC, Brazil

Keywords: Ontology, Data access, Web system, Weather data, Meteorological network, OntoKEM.

Abstract: The importance of meteorological and hydrological data observation stations has grown substantially in

recent years to meet the most diverse demands; ranging from environmental studies related to climate

change and hydrological studies for water use, up to traditional monitoring work to prevent disasters

through the use of forecast models of weather and water level control to contain the flow of rivers. The

effort by governmental institutions, with support from the private sector expanded the network of

observation stations in the State of Santa Catarina, Brazil. However, access to such data is still restricted to

few technical people or to a group of users that access a web information system tailored for them. This

paper presents the steps for developing a prototype of an ontology which serves to facilitate data access for

web users not familiar with either specific concepts or the domain terminology. The goal for building the

prototype is to understand the steps and implications using two complementary tools such as OntoKEM and

Protégé for ontology construction.

1 INTRODUCTION

Meteorological and hydrological observation data

have been used for centuries by several areas, some

of which with critical importance such as aviation,

shipping and agriculture. Installation of automatic

weather stations (AWS), with real time data being

transmitted and recorded every hour or in shorter

intervals, started a significant increment in the

volume of data available in the 90´s. In addition, the

easy access to these equipments increased the

number of stations to dense weather networks with a

consequent enlargement of these databases. Besides

the growing interest in the existing data, these

databases still serve primarily domestic needs.

Although the Internet brought new opportunities

and forms for data access and for dissemination of

information for a wider range of users, observation

data could be more widely open to people if a

standard terminology would be available.

The description of concepts and their relations

within a domain provided by ontology, a recent field

in computer and information sciences, foster

knowledge sharing and integration of distributive

data within a context.

Initially used by the Semantic Web to provide

easy access to information on the Internet, Ontology

has outgrown its application areas. Semantic web

aims to exchange “meanings” and this allows people

(and machines) to exchange knowledge (not data)

through a set of interoperable ontologies (El-Diraby;

Lima and Feis, 2005). Since Ontology offers

formalism in defining concepts through a

description, it makes available semantics and

inference mechanisms on data stored in databases

allowing interoperability between knowledge bases

and easy information searching and processing.

This paper aims to present a prototype of an

ontology for the network of meteorological stations

deployed in the State of Santa Catarina, Brazil, to

complement an online data visualization web

system. The goal for building the prototype is to

understand the mechanisms of ontology

implementation process using two complementary

tools such as OntoKEM and Protégé and to explore

the potential ontology presents to speed up access to

desired information in databases.

The data observation network in Santa Catarina

State, Brazil, is described in the second section of

this paper. The third section discusses about data

145

Morais Kinceler L., Mendes Massignam A. and Leomar Todesco J..

AN ONTOLOGY FOR A HYDRO-METEOROLOGICAL OBSERVATION NETWORK.

DOI: 10.5220/0003626701450151

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 145-151

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

visualization and the web system that is operational

for online state meteorological and hydrological data

access. The fourth section describes the steps for the

prototype ontology construction and the final section

presents some conclusions.

2 THE DATA OBSERVATION

NETWORK

The agricultural area has made wider and more

intense use since the earliest records of

meteorological data. In the United States, for

instance, the records of weather data started far off

in the year of 1753 about the progression of cyclones

and ocean currents (Oblack, 2011). As stated by

Conner (2004), the first formal network of weather

observers in the United States, established in 1818

by an Army Surgeon General, was motivated by

health purposes; and the network aimed to ascertain

a change in the climate of a given district in a series

of year and how far this was dependent on

cultivation of the soil, density of population, and

other factors. Since then, meteorological observation

data have been applied in planning and development

of agricultural technologies, and systems of climatic

data for agricultural prediction and in many other

areas of study. Although the first weather records in

Brazil started in 1754 with a description of weather

variations (sensory observations) in the Amazonia

region, it was after creating the Astronomic

Observatory of Rio de Janeiro in 1827 that scientific

procedures started in Brazil (Sant´Anna-Neto, 2003).

The pioneer meteorological network in Brazil was

installed in 1886 in São Paulo, reaching 40 points of

observation in a 14 year period.

In Santa Catarina, the first meteorological data

records date of 1874. The Agricultural Research

Institute of the State of Santa Catarina, Brazil

(Epagri) began the ordination of a network of

meteorological stations in the 70´s, whose goal was

the establishment of zoning of agricultural crops

with potential for the territory, according to

bioclimatic criteria. Meteorological observation data

were collected three times daily and recorded on

paper forms since 1911. Transcription to electronic

format began in 1986 with the development and

deployment of a meteorological database. The

installation of AWS in 1997 with real time data

being transmitted and recorded every hour, and also

the increase in the number of stations, were

significant accomplishments. The network was

shortly expanded with hydro-meteorological stations

to cover the gaps left by the meteorological and

hydrological federal agencies. This network has

been monitoring the atmosphere, rivers and sea

level, in cooperation with other national and

international institutions. From the 85 hydrological

stations of the National Water Agency currently

operational in the state, 33 of them are automatic.

Private companies from forestry, ports, agriculture,

fishery to hydro/thermal power stations have

invested in AWS to expand the network in the state

and neighboring states. There are 95 weather

stations, 71 are automatic and 24 are conventional

observation stations. Among the hydrological

rainfall and streamflow stations, 67 of them are

conventional and 129 are automatic.

The National Institute of Meteorology (INMET),

has deployed 14 new AWS in Santa Catarina to

study the changes and climatic fluctuations that

require preventive and mitigation actions to

minimize climate risks. INMET works cooperatively

in South America providing frameworks for

scientific studies, including those events that cause

climate change, with the support of WMO (World

Meteorological Organization/United Nations).

The data observation network is of indubitable

importance since hydrological data have an

extraordinary demand from technical analysis. On

the other hand, it is also evident the great importance

of weather, climate and water data. However, a

major difficulty has always been the dissemination

of information to different users in different formats.

3 DATA VISUALIZATION

Data is considered, in this context, as a signal sensed

by our sensorial system, and each data can be stored

and handled, for instance, in databases (Schreiber et

al., 2000). Information is data with some meaning

within a context and involves relations among data.

Information systems development has proven

efficiency for data and information handling, but

investments made toward leveraging access to good

infrastructure to promote fast and inexpensive access

to data and information, especially after the Internet,

created an overwhelming amount of information,

which in turn can be cumbersome to people. As a

consequence, delays can be further complicated and

costly for an organization when decisions need to be

taken rapidly.

Knowledge derived from information is richer

and more meaningful. Organizational knowledge

flows and it is recognized as patterns, in a much

more complex structure of relations, or it can also be

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

146

defined as information in action. Access to

knowledge must be available in ready-to-use

solutions to answer organizational demands in time.

Epagri´s Center of Hydro-meteorological

Information and Environmental Resources (Ciram)

has developed a web-based information system for

knowledge and information consultation which also

sends automatic warning messages to special users

and internal staff.

In house users are weather station maintenance

staff who needs information and control system for

planning purpose with quick, easy and intuitive

access to maintain data quality in the network. The

main system requirements for these users are:

• monitoring the missing data for the entire

network of stations;

• a table of the stations should provide sensors of

varying patterns with main standard variables;

• the system must differentiate the amount of

missing data, and failures – if any, for each station in

a 48 hour period;

• stations must be grouped in: meteorological,

hydrological, oceanographic and support research;

• user should be able to sort the data, choose the

number of hours to be monitored, in graphical form

or on a map, and verify the climatology;

• access through the intranet/internet should be

simple and data timely updated;

• A lightweight web application deployed to

consume little computer resources and should be

faster on the access of information and updates.

System requirements for special users vary from

user to user and it is hardly dependent on data

analysis and simulations for each application. An

application involving data visualization was then

developed to balance analysis, visualization and

interaction, which are the main components of a

decision support system.

3.1 Implementation Results

It was initially studied all system requirements,

database variables and relationships, and the

available technology according to organizational

standards. The system development project plan

considered data visualization of great importance

since its graphic format provides better information

clarity, precision and efficiency to meet all system

requirements. Data stored in the 8i Oracle database

was studied regarding variable, structure and

input/output processes characteristics for the three

class type stations.

Once a web-based system for online data

visualization was to be developed, the technology

requirements should take that into account. The

technology selected was the J2EE (Java 2 Platform,

Enterprise Edition) from Sun Microsystem's using

JDBC to connect to the Oracle database. JavaBeans,

Servlets and Java Server Page - JSP were used to the

presentation layer; and, Tomcat on Linux operating

system used for the application server.

Some other technical requirements for the system

were: flexibilization, decentralization and

mobilization. Flexibilization made possible with

interactive interface, so people with no specific

training could use specialized software for complex

actions. Information technology provided the

possibility of decentralization, that is, specific tasks

can be moved from the back-end to the front end

computers providing better performance and leaving

servers for other tasks. The mobilization allow

anyone access information for decision making from

anywhere and in distributed computers; from mobile

phones, notebooks and mobile communication

(Heijmans, 2002). So, staff can easily do their work

not only at the office, but also at field, at home, on

the road or at the client's office.

Simulations or measurements are frequently

difficult to be interpreted by users in the application.

The main project goals are to transform the data into

something more meaningful and in a useful visual

representation so that the human observer can

quickly have a better understanding. Visualization is

usually represented by graphics, maps, diagrams, or

in the form of tables. Pictorial format system was

developed. Colors were explored to distinguish data

availability or sensor problems, tables to group

stations, maps to show data and geographic

distribution. The user surfs data on a click of the

mouse. This helps staff to plan maintenance trips

according to groups of stations, better sensor

suppliers and plan of sensor acquisitions.

Figure 1: Hydrological forecast model.

AN ONTOLOGY FOR A HYDRO-METEOROLOGICAL OBSERVATION NETWORK

147

Information had also to be delivered to other users

and through a variety of media, as soon as required

and in different situations. It was necessary to be

available the right time, at the right place, for the

right person and in the right format.

Special users are so called since they receive

special information for decision making. Data is

handled specifically to provide such information

although within a monthly cost for software

development and to maintain the service available

online, as shown in Figure 1.

The demand for this service is high as well as

development time and maintenance hours. Data

could be more useful for the public if a more

friendly system were available for data access. Web

user difficulties range from simple questions such as

identification of the existing stations in the area of

interest and to identify the parameters. This is due to

both a lack of knowledge about this specific area and

of a more friendly system. By adding meaning to

data the expectation is that information will be more

accessible and meet a greater number of people with

ease and independence that internet provides.

Besides to data sharing and integration of

distributive data, some database may interoperate

with other databases and systems.

Ontology has been primarily used to build

domain knowledge models in knowledge systems

projects (Chandrasekaran; Josephsons; Benjamins,

1998). Ontology has also been used as an

intermediary layer for mapping different

applications or applications with heterogeneous

sources of information, enabling the interoperability

among systems and also for data integration.

Among the most universally recognized benefits

presented by ontology, the easy way they represent

semantically a domain, their potential for sharing in

different domains, and re-use must be pointed out.

4 THE ONTOLOGY PROTOTYPE

Ontology is often used as a tool for Knowledge

Systems construction. Ontology in Gruber´s

definition is (1993) "a formal and explicit

specification of a shared conceptualization". It

describes in a generic way the knowledge shared

between different specialists in a field to create a

common understanding of a knowledge domain

using concepts, properties and relationships. But it

can also provide standard in domain knowledge.

Since ontology is a formal description of a domain it

can either be processed by computers and be

understood by people. Knowledge engineers use

ontology to capture the semantics of knowledge and

to put it in a format that is easy for maintenance as

well as to efficiently process inference algorithms

(Knublauch, 2002).

4.1 Methodology

The process of ontology construction is usually

supported by specification, conceptualization,

formalization, implementation, evaluation

(verification and validation) and maintenance phases

(Rautenberg et al, 2008). Some well known

methodologies for ontology construction are On-to-

Knowledge (Fensel and Hermelen, 2008) and

Methontology (Gómez-Pérez et al., 2004).

The ontology building process and

methodologies inspired the creation of several

development tools, such as Kaon (Oberle et al.,

2004), Protégé (Gennari et al., 2003), OntoKEM

(Rautenberg et al, 2008) and OntoSTUDIO (Weiten,

2009) among others.

Protégé is an efficient modelling tool that has

been widely embraced by the community of

Knowledge Engineering (Knublauch, 2002) once

promotes interoperability with other tools through

RDF exchanging format files. Protégé is a Java open

source software which offers several features that

can be expanded through plug-ins and provides

support for languages based both on frames and

logic. It works especially well in the ontology

implementation, evaluation and maintenance phases.

However, for the early stages of ontology

construction OntoKEM was proposed.

OntoKEM (Ontologies for Knowledge

Engineering and Management) was created by the

knowledge engineering group at Federal University

of Santa Catarina (Brazil) to support the first stages

of ontology development (Rautenberg et al, 2008). It

is a good help for requirement analysis and makes

good project documentation that covers

specification, conceptualization and formalization

phases. The final product of a project developed

with OntoKEM is an OWL (Web Ontology

Language) format file.

The domain ontology construction for

observation stations network project proposed by

this work was created to facilitate users' access to

data stored in an Oracle relational database called

SAM, administered by Epagri. This database stores

observing data or automatically recorded data of the

network. Stations have a code that identifies them as

belonging to a class type, but users who access data

through the web have no knowledge of the meaning

associated with each code.

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The project started with requirement analysis, to

detail the problem and the expectations, and to

contextualize the problem. Lately, it is defined the

terms, relations and constructed a vocabulary. A

hierarchy of classes is then structured and all

relations between classes, their properties and

restrictions are formally listed. OntoKEM

proposition tool was used in project´s first phases.

The following implementation and evaluation phases

were supported by Protégé in a straightforward

procedure since an OWL file from OntoKEM was

created to be imported into Protégé. This project had

five specialists collaborating from specification to

validation phases, described in details as follows.

4.1.1 Specification Phase

The specification phase has the purpose of defining

the ontology scope in terms of limits that will be

considered in developing the project.

During this phase competence questions are

raised, which are typical questions that the ontology

must respond appropriately and efficiently, defining

the ontology domain and scope limits.

Knowledge engineer investigates a domain,

defines the most important concepts, and makes a

formal representation of objects and relationships in

this domain (Russell and Norvig, 1995). Figure 2

shows OntoKEM´s interface for weather stations

ontology competence questions registry.

Figure 2: Competence questions.

At the beginning, the project had two objectives:

1) identification of areas covered by stations for data

collection, 2) easy data access. However, the system

for retrieving data from the database already exists

and it is efficient, and the real problem is the

identification of stations that can meet web users'

interests. The project focused then on this second

problem and competence questions were adjusted.

4.1.2 Conceptualization Phase

Conceptualization is a description of the ontology on

a conceptual model. The model consists of domain

concepts, relations between concepts and properties

of concepts (Rautenberg et al., 2008). Noy and

Mcguinness (2000) suggest that it is useful to write

down a list of all terms we would like either to make

statements about or to explain to a user: “What are

the terms we would like to talk about? What

properties do those terms have? What would we like

to say about those terms?”

From the competence questions, OntoKem

provides a mechanism to identify the terms of the

ontology and the relationships between those terms.

Subsequently terms will be incurred in classes and

subclasses that represent objects and concepts of the

ontology in a hierarchical structure. Relationships

between terms are also identified from the

competence questions and registration occurs

simultaneously. Once the set of relations defines

semantics to the domain the ontology represents,

relationships will provide consistency to classes.

Suggested names for terms from each

competence question were standardized to start with

uppercase letter. Terms with more than one word are

joined by an underscore. Relationship names are

registered in two words. The first word is a verb

followed by a class; the words are connected by

underscore and both in lowercase letters.

Terms and relationships aggregate a conceptual

or functional description which will compose a large

dictionary for project documentation. A vocabulary

for the ontology was created with 85 elements from

suggested terms, of which 36 represent classes, 22

represent relationships between classes, 22 represent

properties, 1 defines a constraint for stream order

and 4 terms were abandoned. Vocabulary registries

all concepts shared by the ontology team members.

It is an excellent documentation of the ontology

since it describes conceptually all the elements.

4.1.3 Formalization Phase

The formalization phase transforms the conceptual

description into a formal model (Rautenberg et al,

2008). Axioms may constrain concept

interpretations by restricting its meanings using "is-

a" or "part-of" relationship types in hierarchically

organized structures.

Classes´ hierarchy was accomplished with a

grouping class process. For each selected class

subsequent associations are made to a parent class or

to a list of sub-classes. During the iterative process,

some terms changed. For example, the relationship

AN ONTOLOGY FOR A HYDRO-METEOROLOGICAL OBSERVATION NETWORK

149

“is_type” created to define a relationship between

the “station” and “type” classes was deleted.

Subclasses named “Automatic_station” and

“Observing_station” were created for the class

“Station”. This demonstrates that two subclasses that

were not raised up from the competence questions at

the beginning of the project were created later on.

When the hierarchy of classes is completed, the

combination of relationships between classes is set.

The relationships were created from the competence

questions and are now used to associate the

appropriate classes. It is worth to note that

subclasses inherit the relationships of a parent class.

Relationships are defined by a domain class and a

range class. OntoKEM defines constraints between

classes only as comments, to indicate that these

restrictions need to be implemented when the OWL

file created by OntoKEM is imported by Protégé.

4.1.4 Implementation Phase

The implementation phase is the actual development

of the ontology on a formal knowledge

representation language (Rautenberg et al., 2008).

The documented conceptual model created in

OntoKEM in the first three phases can be exported

to an OWL format file. The ontology is implemented

by importing the OWL file into Protégé.

Protégé is a free open source platform which

uses OWL editor for building applications based on

knowledge and domain models structured on

ontology. The Protégé platform is a foundation for

rapid development of applications and prototypes.

Figure 3: Protegé_Jambalaia´s hierarchy of classes.

When the OWL file generated by OntoKEM is

imported into Protégé, a hierarchical structure is

obtained by the Protegé_Jambalaia plug-in as shown

in Figure 3, similar to the one created by OntoKEM.

OntoKEM relationships between classes are

properties in Protégé, which means that properties

are relationships between two objects. Instances of

classes are called individuals and must be unique.

4.1.5 Evaluation Phase

Individuals, or instances of classes, must be unique

and were inserted to validate the structure of the

ontology imported into Protégé. At least 20

instances for each class were created. Many

relationships between classes were discarded or

adjusted to give semantic support to the ontology.

Thus, the iterative building process provided more

adjustments in the ontology.

Instances of classes must be inserted from the

more generic and independent to the more specific

and dependent classes, in that order. Since

relationships between classes are established when

instances of dependent classes are created,

independent classes must exist prior to the creation

of dependent classes for the relationships to be

established at this moment. Instances must be the

most representative of the ontology universe.

After inserting all instances, the ontology is

checked for its appropriateness to the purpose for

which it was created through queries or questions. It

is made a rescue of the competence questions during

this process to generate the queries that check

whether the ontology is responding appropriately to

its purpose. Queries are structured to search for

instances of classes through their relationships with

other classes or by criteria of their properties.

During the validation process it is important to

observe if the result of a query sets aside instances

that should be selected or if it really represents the

conditions established for that ontology. This

process allows adjustment of relationships between

classes or relationships to be more restrictive for

better ontology expression. Although all declarations

must be explicit, sometimes some statements go

unnoticed during the construction process. The

validation phase, therefore, is set to find those gaps.

5 CONCLUSIONS

This paper has presented a network of

meteorological and hydrological stations deployed in

the State of Santa Catarina, Brazil, and an online

data visualization web-based system. Detailed

description of an ontology prototype construction

has followed to provide a formal definition of

concepts and to make semantics and inference

mechanisms available on data stored in the database

for a wider group of people to benefit of. The

prototype constructed cleared the understanding of

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

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the mechanisms for ontology implementation.

Finding common concepts on the domain

knowledge was a good exercise during the project,

although agreement on concepts was hardly reached

among hydrologists when data scale changes.

The creation of the ontology added semantics to

data which in turn made data more easily accessible.

The ontology provided independence to internet

users who are not familiar with technical terms.

People find the information they need from the

database in straightforward manner with no use of

meaningless numbers or codes. The ontology had

also brought benefits to interoperate geographic

information system and database applications. By

adding meaning to data with the creation of ontology

for a real time meteorological and hydrological

observation network, the expectation is that a greater

number of people and applications will benefit with

ease and independence that the internet provides.

The integration of the ontology to automatically

capture the instances from the Oracle database is

still a research project to be carried out.

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