Domain Ontology to Support Open Data Analytics for Aquaculture

Pedro Oliveira, Ruben Costa, José Lima, João Sarraipa and Ricardo Jardim-Gonçalves

CTS, UNINOVA, Dep.º de Eng.ª Electrotécnica, Faculdade de Ciências e Tecnologia, FCT,

Universidade Nova de Lisboa, 2829-516 Caparica, Portugal

Keywords: Ontology Engineering, Knowledge Representation, Multilingual, Education and Training.

Abstract: The Aquaculture industry, which comprises mainly of SME companies, represents a significant source of

protein for people. From an IT perspective, aquaculture is characterized high volumes of heterogeneous data,

and also lack of interoperability intra and inter-organisations. Each organization uses different data represen-

tations, using its native languages and legacy classification systems to manage and organize information. The

lack of semantic interoperability that exists can be minimized, if innovative semantic techniques for repre-

senting, indexing and searching sources of non-structured information are applied. The work presented here,

describes the achievements under AQUASMART EU project, which aims to accelerate innovation in Eu-

rope’s aquaculture through technology transfer for the deployment of an open data solution through multilin-

gual data collection and analytics solutions and services, turning the large volumes of heterogeneous aqua-

culture data that is distributed across the value chain, into an open cloud of semantically interoperable data

assets and knowledge. Results achieved so far do not address the final conclusions of the project but form the

basis for the formalization of the AQUASMART semantic referential.

1 INTRODUCTION

The aquaculture industry, which comprises mainly of

SME’s companies, represents a significant source of

protein for people. Globally, nearly half the fish con-

sumed by humans is produced by fish farms. Aqua-

culture is now fully comparable to capture fisheries

when measured by volume of output on global scale.

The contribution from aquaculture to the world total

fish production of capture and aquaculture in 2012

reached 42.2 percent, up from 25.7 percent in 2000

(FAO Fisheries and Aquaculture Department, 2014).

Global production is forecasted to increase from 45

million tons in 2014 to 85 million by 2030, making

the aquaculture industry the fastest growing animal

food producing sector in the world. The European

Union needs an innovative aquaculture industry to

meet rising seafood demand and to enhance its com-

mercial stocks.

According to the Food and Agriculture Organiza-

tion of the United Nations (FAO), the volume and

value data in global aquaculture production are pri-

marily official statistics obtained directly from the na-

tions and mainly described in local language. Also,

there are available other relevant sources of data, like

academic reviews, consultant reports and other spe-

cialist literature.

The main problems of the Aquaculture sector are

related with the lack of global knowledge access, and

the inefficient data exchanges and data reuse between

aquaculture companies and its related stakeholders.

This is primarily due to incompatibility problems

among the several information representation struc-

tures used by the different software applications along

supply chains and business networks (Ray and Jones,

2006). Aquaculture companies have limited capabili-

ties to hire specialized technical resources out of their

core business. The main issue fish farmers’ face is

data understanding and identifying correlations be-

tween parameters that affect production, the lack of

skilled professionals and the right IT tools, prevents

fish farmers to get better insights of their own data

and also prevents them to share best practices with

other aquaculture stakeholders. For example, if one

could reach other growers data (e.g., growth rates,

FCR (Feed Conversion Ratio) – related to environ-

mental conditions of cause), than it would be able to

have a better and closer prediction plus better and

closer feeding according to the current biomass. So,

an important step is to be able to get actionable in-

sights in the data resulting in smarter decisions and

344

Oliveira, P., Costa, R., Lima, J., Sarraipa, J. and Jardim-Gonçalves, R..

Domain Ontology to Support Open Data Analytics for Aquaculture.

In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 344-351

ISBN: 978-989-758-158-8

better business outcomes, being able to look at past

performance and understand that performance by

mining the related data (production, environment,

etc.) to look for the reasons behind past success or

failure and take better decisions for the future.

Knowledge transfer goals are to take the state of

the art in multilingual data collection tools, analytics

solutions and services, semantic interoperability

methods and data mining procedures to implement a

global open aquaculture data information system,

able to respond to specific user needs and profiles.

Users in the Aquaculture industry can innovate

taking the novel capabilities for seamless and holistic

access of multilingual data products and services in

the Aquaculture value chain, bridging across borders,

languages, industries and sectors, removing barriers

both technical and organizational.

The access of this global knowledge creates sig-

nificant new and further commercial opportunities

and positive economic prospects.

In India several ICT initiatives are being tried for

disseminating aquaculture information to fish farm-

ers, shortening the digital gap and helping the farmer

in reaping a good harvest.

There is a demand for intelligent world-class so-

lutions capable of reinforcing partnerships and col-

laborations with an improved cross-cultural under-

standing. However due to the proliferation of termi-

nology, organizations from similar business environ-

ments have trouble cooperating, and are experiencing

difficulties exchanging electronically vital infor-

mation. To address these issues it is essential to de-

velop semantic tools that easy these integration pro-

cesses.

Some examples of closely related research

streams in recent years are: the extensive work on

knowledge models and knowledge management

tools, the rise of so-called knowledge engineering, the

myriad of projects around ‘controlled vocabularies’

(such as ontologies, taxonomies, dictionaries, and

thesauri), and the academic knowledge-centred

courses (graduation, master, and doctoral).

A controlled vocabulary is a list of terms that have

been enumerated explicitly. This list is controlled by

and is available from a controlled vocabulary regis-

tration authority. All terms in a controlled vocabulary

should have an unambiguous, non-redundant defini-

tion. This is a design goal that may not be true in prac-

tice. It depends on how strict the controlled vocabu-

lary registration authority is regarding registration of

terms into a controlled vocabulary. At a minimum,

the following two rules should be enforced (Lima et

al., 2007):

 If the same term is commonly used to mean dif-

ferent concepts in different contexts, then its name

is explicitly qualified to resolve this ambiguity.

 If multiple terms are used to mean the same thing,

one of the terms is identified as the preferred term

in the controlled vocabulary and the other terms

are listed as synonyms or aliases.

In line in the notion of controlled vocabularies, an on-

tology for the Aquaculture domain is being devel-

oped. The approach presented here will be beneficial

for aquaculture companies in several ways: (i) by en-

abling to benchmark similar companies with relation

to their production performance indicators; (ii)

providing access to consolidated information on best

practices and success stories; (iii) to be able to inter-

pret their production data using data mining tech-

niques and share that data through the

AQUASMART open cloud.

This paper is structured as follows: Section 2 pre-

sents the related semantic approaches. The overview

and architecture of the project that serves as valida-

tion for the ontology are explained in section 3. Sec-

tion 4 presents the AQUASMART semantic referen-

tial. Section 5 presents the IT semantic services de-

veloped, the tools used and the interdependencies be-

tween components that implement the

AQUASMART functionalities. Finally, section 6

presents our main conclusions and additional consid-

eration on future work.

2 RELATED WORK

Big Data analytics and knowledge extraction from the

aquaculture domain, is something relatively new.

This chapter address some relevant works that have

been done relatively to the technology and the do-

main.

2.1 ICT in Aquaculture

Recent ICT projects on the aquaculture domain, aim

to ease the access to information about scientific

farming practices and market prices through web por-

tals (De et al., 2008), while others propose innovative

and unique models of information exchange on farm

history, crop details, soil details, weather data, farmer

details, case sheets, photo bank and a library (Vimala,

2009). The multilingual issue is addressed by (Mon-

dal et al., 2011), where an online tool provides an-

swers to questions asked by farmers and agro-profes-

sionals over the Internet.

Also, a very important step to adopt ICTs in Aq-

uaculture environments is to reach the end-users in an

Domain Ontology to Support Open Data Analytics for Aquaculture

345

early-stage, even during the learning process. In

(Seixas et al., 2015) the authors review educational

means used in teaching and learning in the area of aq-

uaculture, fisheries and aquatic resources manage-

ment at European level, with specific consideration

on the use of ICT and e-learning tools. It concludes

that there is a real and urgent need to "train the train-

ers" to use ICT in their teaching environments. Addi-

tionally, from the students' end, there is a strong de-

sire to learn more about the application of e-learning

tools and to use them in their learning process.

2.2 Ontology Development

The method proposed in our work, adopts an associ-

ation rules learning technique in order to discover rel-

evant relations among key terms in a document cor-

pus, and additional human input to perform the map-

pings between terms (frequent item sets) and ontolog-

ical concepts and the establishment of the final scores

on each relation. In simple words, frequent item sets

are groups of items that often appear together in the

data.

This will reuse the findings proposed by (Paiva et

al., 2013) and extend it to the aquaculture domain.

Figure 1 depicts the methodology to be addressed by

this work regarding thesaurus building.

Figure 1: Thesaurus building methodology (Sarraipa et al.,

2010).

One of the focus of the work addressed where, is

the development of a semantic referential for the aq-

uaculture domain. Regarding ontology development,

the scientific literature is vast and addresses several

approaches and tools for dealing with its specification

and management. Several efforts have focused on ex-

tracting structured ontologies from unstructured text.

2.3 Multilingual Data

The introduction of an innovative multilingual

knowledge base capacity suitable for the Aquaculture

sector, which would enable large volumes of data to

be accessible as semantic interoperable data and

knowledge will improve significantly the sector and

ultimately the EU’s competitiveness.

Three tools to support the multi-linguist require-

ments: 1) a tool for language independent document

representations (Canonical Correlation Analysis

(CCA) mappings) (Kuss and Graepel, 2003); 2) a tool

for multilingual semantic annotation (Enrycher)

(Jožef Stefan Institute, 2012); and 3) a statistical ma-

chine translation tool (MOSES) (Koehn et al., 2007).

They will be used to support the ontology mapping

processes which involve ontology concepts and anno-

tated documents in several languages.

Enrycher is a service-oriented system providing

shallow as well as deep multilingual text processing

functionality at the document level. It can be used to

perform multilingual semantic annotation on docu-

ments in common open knowledge bases such as

DBpedia (Lehmann et al., 2015), and can support the

local to reference ontology mapping processes.

The MOSES is an open source statistical machine

translation system to translate documents stored in lo-

cal databases across several language pairs in order to

share the knowledge between Aquaculture compa-

nies. The system will also be used to support the mul-

tilingual training and learning platform by translating

training materials such as best practices documents

and video lectures. Linguistic resources such as the

Fisheries Glossary can be incorporated in the process

(Countryside Council for Wales, 2001) by using it as

a parallel corpus. Having large collections of short

documents can be used to adapt the language model

for the target language.

3 AQUASMART CONCEPT

The data collected in the AQUASMART open data

cloud is suitable to be reused in other industrial do-

mains if needed, (e.g., environmental or transporta-

tion data), providing a cross-sectorial setting to the

provided solution. The cloud is enriched with a layer

of multilingual information (multilingual mappings

and multilingual linguistic information) and with a set

of services for creating, representing and accessing

that multilingual information.

The prime goal is to accelerate innovation in Eu-

rope’s Aquaculture through technology transfer for

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

346

the deployment of an open data solution through mul-

tilingual data collection and analytics solutions and

services, turning the large volumes of heterogeneous

aquaculture data that is distributed across the value

chain, into an open cloud of semantically interopera-

ble data assets and knowledge. Each such systems

usually uses different data representations, using its

native languages and knowledge organization tools

such as vocabularies and classification systems to

manage and organize information. Although the prac-

tice of using knowledge organization tools to support

document tagging (e.g. thesaurus-based indexing)

and information retrieval (e.g. thesaurus-based

search) improves the functions of a particular infor-

mation system, it is leading to the problem of integrat-

ing information from different sources due to lack of

semantic interoperability that exists among

knowledge organization tools used in different infor-

mation systems. The project technology transfer goals

are to take the state of the art in multilingual data col-

lection tools, analytics solutions and services, seman-

tic interoperability methods and data mining proce-

dures to implement a global open aquaculture data in-

formation system, able to respond to specific user

needs and profiles Figure 2.

Figure 2: AQUASMART Concept.

Actually, aquaculture companies have never tried

to transform data that are captured into knowledge,

and share this knowledge to improve efficiency, in-

crease profitability and do business in a sustainable,

environmentally friendly way. In other words, data

are just captured but not exploited. AQUASMART

represents a big innovation in this direction, it adds

the dimension of global open data. The improvement

and the innovation become even bigger because the

quality of the analysis that will be performed by the

companies will be dramatically improved. Hence,

even the exchange of information itself is a huge busi-

ness innovation. It is the first time that companies will

be able compare their results to the ones of other com-

panies and benchmark their performance. This is go-

ing to create a multiplier effect and boost competition

for improvement in the sector. This will be further fa-

cilitated and enhanced by the integration with the Eu-

ropean Union Open Data Portal and the exchange of

information in the social media.

AQUASMART vision relates to implementing a

state of art multilingual open data framework that

companies can use to seamlessly access global data

and take more knowledgeable decisions using multi-

lingual information. H2020’s vision suggests that en-

terprises must move away from silo solutions, used

behind the closed doors of company, to a more open

data technological solutions built for the industrial

sector to enhance their operations. However, the ac-

tual state of practice is that knowledge is transferrable

and sharable but with significant barriers for semantic

compatibility.

The main mission is driven by the business need

of the European aquaculture companies, when com-

panies have business objectives that they cannot

achieve due to lack of instruments that would enable

them to manage and access to global knowledge and

big data, in a multi-lingual, multi-sector and cross-

border setting.

4 SEMANTIC REFERNCIAL

To improve the ability of aquaculture companies to

innovate across their value chain, there is a need to

provide multi-lingual data, which must be interoper-

able through various products or services. To achieve

this objective, the proposed approach integrates a ref-

erence ontology that supports the integration of het-

erogeneous data (including multilingual) that con-

cerns the aquaculture environment.

The proposed semantic referential provides the

distributed composition, formality, richness and qual-

ity of information required among the aquaculture

sector to ensure that all the actors within the produc-

tion process “speak the same language”.

The creation of the semantic referential followed

a method for designing and developing a domain on-

tology with inputs from knowledge experts, provid-

ing the necessary insights towards the improvement

of the efficiency of the aquaculture production pro-

cesses. Such experts, contributed with their

knowledge about the aquaculture production, the ac-

tors involved, and the data generated during the pro-

duction process (Oliveira et al., 2015).

Domain Ontology to Support Open Data Analytics for Aquaculture

347

Figure 3: AQUASMART Semantic Referential.

This semantic referential, as seen in Figure 3, is

composed by four main areas, (i) the AQUASMART

production domain; (ii) FAO aquaculture glossary;

(iii) AQUASMART training modules and; (iv) the

AQUASMART IT framework. In the following sec-

tions, the authors address each of these modules,

which comprises the overall AQUASMART seman-

tic referential. Each of the modules are being inte-

grated under the same umbrella (AQUASMART se-

mantic referential) and formalized in OWL language.

4.1 AQUASMART Production Domain

In the AQUASMART context, knowledge experts are

the end users (mainly fish farmers). The purpose of

involving such experts in the process, is not only to

provide input to the semantic referential, but also to

perform a quality review of the AQUASMART train-

ing courses. With the help of these experts, the main

structure of the AQUASMART ontology was devel-

oped to accommodate all the important and necessary

information that will support all the project services

and functionalities.

The ontology is mainly separated in two concepts,

the “Aquaculture Production Entities” and the “Grow

Out Data Analysis”. The first contains the all the aq-

uaculture related entities, while the second one con-

tains the key performance, the process and production

related data.

The “Aquaculture Production Entities”, are the

main components of the production operation, includ-

ing actors involved in the process, species being pro-

duced (e.g. seabream and seabass), location of the fish

farm, and the type of cages used to store the fish. The

“Grow Out Data Analysis”, is focusing on process

steps, production data and indictors. A sample of rel-

evant production parameters is shown on Table 1.

Table 1: Production Parameters.

Geographical Region Species

Hatchery Broodstock origin

Hatchery Quality (text) Stocking Month

Average Weight Mortality

Avg. Temperature Avg. SFR

Avg. Fish Density Oxygen

This ontology will be integrated with an aquacul-

ture glossary that contains the aquaculture related

terms and their definitions. To make available fully

interoperable multi-lingual data products and services

in the Aquaculture, AQUASMART makes use of the

FAO glossary for aquaculture (FAO, 2015).

4.2 FAO Aquaculture Glossary

From the AQUASMART perspective, the multilin-

gual data generated within the aquaculture domain,

can be exploited as a layer of services and resources

by seamlessly adding (i) linguistic information for

data and vocabularies in different languages, (ii) map-

pings between data with labels in different languages,

and (iii) services to dynamically access and traverse

linked data across different languages.

We envisage a multilingual aquaculture where an

end-user would query the “Aquaculture Open Data

Cloud” in his/her own language, and would get the

relevant data in that language.

The primary objectives of the AQUASMART

glossary is: (i) to serve as a reference to fish farmers,

consultants, administrators, policy makers, develop-

ers, engineers, agriculturists, economists, environ-

mentalists and any other actor interested in aquacul-

ture; (ii) and to facilitate communication among ex-

perts and scientists involved in aquaculture research

and development.

Figure 4: Glossary Term Example.

The glossary supports a multi-lingual approach

that includes, in an initial phase, English, French,

KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development

348

Greek, Hebraic, Spanish and Portuguese terms. Each

term has properties which define it (Name, definition,

related term, synonyms, subject area, translation, and

image). This kind of information is what defines and

gives semantic meaning to terms. Figure 4 shows an

example of a glossary term.

4.3 Ontology Training

The training development methodology translates de-

sign specifications into training materials. The meth-

odology presented by Sarraipa et al., (2013) starts by

identifying the training objectives and the target au-

dience including desired roles & competences. Then

it uses an appropriate instructional approach to per-

form the training courses’ materials development,

complemented with a set of different quality reviews.

The overall process of developing training fol-

lows a specific process, composed by three different

task tracks (training development, overall training

validation and training execution) that complement

each other. The training development track starts by

defining the course’s synopsis according to the direc-

tives obtained from a training overall objective. Thus,

it was identified the need of defining all these objec-

tives in a courses synopsis. A course synopsis is an

official description of the course as stated in the insti-

tution's catalogue of courses. It should indicate the

overall goal of the course, briefly characterize the

main topics covered, point out why the course is im-

portant to students, identifying any special instruc-

tional methods to be used, and comment on what

background students should have in order to best ap-

preciate the course content. The courses synopses

also act as guidelines to the training course’s authors.

Figure 5: Training Ontology.

The AQUASMART ontology will also be used, to

represent the training KB (Knowledge Base), facili-

tating the categorization of its elements and subse-

quently reasoning over it. It should contribute to the

skills and competencies development of the trainees

as required for specific understanding and exploita-

tion. This reflects the need to develop, organise and

run courses, for example, to train “future users”, in

how to use a specific software or extracting relevant

patterns from aquaculture production data. Figure 5

presents the relations between training concepts built

in Protégé (Stanford Center for Biomedical Informat-

ics Research, 2012).

In this model each learning Module has several

concepts associated, the Sources concept contains in-

formation about the sources referred in the Module,

Contact includes the contact information of the author

of a Module or Course and Keywords that contain a

list of all relevant keywords needed for describing the

contents of the Module. A Course, other than Con-

tacts and Modules that contain the course also in-

cludes Keywords (that include Keywords inherited

from its Modules) and belongs to a Curriculum Main

Area that is divided by Content Areas and Learning

Levels. Each Module and Course has a Target Audi-

ence Group and a Target Audience Industry, to be

recommended accordingly to the profile of the

learner. Finally, a pre-defined Programme is defined

for a specific Target Audience Industry and Target

Audience Group.

5 AQUASMART SERVICES

The AQUASMART IT framework is not yet devel-

oped at the current stage of the project. It will be in

this framework that the AQUASMART services will

persist.

One of the services is the ability to search for Aq-

uaculture partners through the ontology. The ontol-

ogy will support a search feature that allow users to

find suitable companies in Aquaculture domain to

partner. This feature allow to search companies by

different criteria like water temperature, type of fish

produced, size of production, country, this are some

examples supported.

Providing the achieved results to other users is a

service that is being worked on. The main idea of this

service is to provide the results obtained during the

project. This can be in text, formulas or other type of

format that can be understand by users external to the

project.

5.1 Knowledge Search Engine

The other service developed is the training search en-

gine to support the Aquaculture domain. Training ma-

terials are a necessary part of any program or activity

that involves knowledge acquisition and retention

(Wikihow, 2015). For this, authors found appropri-

ated to define “oLEARCH - Ontologies LEArn by

Domain Ontology to Support Open Data Analytics for Aquaculture

349

seaRCHing”, as a new concept related to ontologies

able to change/adapt their knowledge (to learn)

through their users’ patterns of searching/reasoning.

The concept was inspired from the concept LEARCH

defined by (Ratliff et al., 2009) that means “LEArn-

ing to seaRCH” and was defined to represent algo-

rithms for imitation learning in robotics with the main

purpose to search something.

oLEARCH is a training materials search engine

application available to users by Internet. This system

learns from user’s searched training materials con-

cepts improving the KB.

The oLEARCH function uses an algorithm sup-

ported in an instance-based learning approach based

on user interactions. In instance-based learning, train-

ing examples are stored verbatim, and a distance

function is used to determine which member of the

training set is closest to an unknown test instance

(Witten and Frank, 2005). In oLEARCH, such dis-

tance function is represented by the semantic dis-

tance, which is the inverse of the semantic relatedness

between the users introduced concepts and the train-

ing materials classified in the reference ontology.

Thus, oLEARCH provides to the users a set of train-

ing materials that are close to their introduced con-

cepts in terms of semantic relatedness. Then, users are

able to select the most appropriated training materi-

als from this set of possible choices. These last users’

selections are also used, as a last feedback, to increase

the semantic relatedness weight of the selected train-

ing materials associated concepts.

6 CONCLUSIONS

The work described within this paper relates to the

development of a domain ontology, able to support

and describe the analysis of aquaculture production

data, but also, the training and IT services which com-

poses the AQUASMART platform. Although final

conclusions are not yet validated, preliminary analy-

sis led us to conclude that the Aquaculture domain is

lacking for semantic approaches which enable data

understanding intra and inter-organizations. The for-

malization and validation of a common semantic ref-

erence model, which is able to drive new and dynamic

collaborations between aquaculture companies and

consequently generate new business opportunities to

them, can be seen as first step towards semantic in-

teroperability. From an application scenario perspec-

tive, the objective of the AQUASMART semantic

referential will enable the understanding of data ana-

lytics resulting from the production data. The idea is

to semantically annotate the results of the correlations

found in batches of production data, with ontology

concepts in order to give meaning to data analytics

results. Multilingual is another important feature due

to the fact that knowledge transfer is one of the main

challenges to be addressed here.

With the proposed approach presented here, there

will be opportunity for innovation in the aquaculture

industry such as transforming data into global

knowledge, and use this knowledge to improve effi-

ciency, increase profitability and do business in a sus-

tainable, environmentally friendly way; Better and

perfect view of the life to date fish behaviour and the

living inventory (biomass) that exist in a farm, based

on the analysing of all environmental and biological

data that will exist in the local system and at global

level. By knowing the global parameters that affect

the production, the companies will be able to make

accurate estimations of the growth of the fish and the

result of the production every day.

ACKNOWLEDGEMENTS

The authors acknowledge the European Commission

for its support and partial funding and the partners of

the research project: H2020-644715 AQUASMART.

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