A KNOWLEDGE-CENTRIC E-RESEARCH PLATFORM FOR

MARINE LIFE AND OCEANOGRAPHIC RESEARCH

Ali Daniyal, Samina Abidi, Ashraf AbuSharekh, Mei Kuan Wong and S. S. R. Abidi

Department of Computer Science, Dalhousie University, 6050 Univeristy Ave, Halifax, Canada

Keywords: e-Research, Knowledge management, Web services, Marine life, Oceanography.

Abstract: In this paper we present a knowledge centric e-Research platform to support collaboration between two

diverse scientific communities—i.e. Oceanography and Marine Life domains. The Platform for Ocean

Knowledge Management (POKM) offers a services oriented framework to facilitate the sharing, discovery

and visualization of multi-modal data and scientific models. To establish interoperability between two

diverse domain, we have developed a common OWL-based domain ontology that captures and interrelates

concepts from the two different domain. POKM also provide semantic descriptions of the functionalities of

a range of e-research oriented web services through a OWL-S service ontology that supports dynamic

discovery and invocation of services. POKM has been deployed as a web-based prototype system that is

capable of fetching, sharing and visualizing marine animal detection and oceanographic data from multiple

global data sources.

1 INTRODUCTION

To comprehensively understand how changes to the

ecosystem impact the ocean’s physical and

biological parameters (Cummings, 2005)

oceanographers and marine biologists—termed as

the oceanographic research community—are seeking

more collaboration in terms of sharing domain-

specific data and knowledge (Bos, 2007).

To support the oceanographic research

community we have developed a collaborative e-

research platform to enable the timely sharing of (i)

multi-modal data collected from different

geographic sites, (ii) complex simulation models

developed by specialized research teams; (iii) high-

dimensional simulation results, generated by

specialized simulation models, reflecting the local

dynamics of specific geographic regions; and (iv)

textual knowledge resources—i.e. research articles,

reports, news and case studies.

We propose a knowledge management approach

to complement observation-based research programs

with high-level knowledge-based models to assist

researchers in establishing the causal, associative

and taxonomic relations between raw data, modelled

observations and published knowledge.

We present an e-research platform termed

Platform for Ocean Knowledge Management

(POKM)—that offers a suite of knowledge-centric

services for oceanographic researchers to (a) access,

share, integrate and operationalize the data, models

and knowledge resources available at multiple sites;

(b) collaborate in joint scientific research

experiments by sharing resources, results, expertise

and models; and (c) form a virtual community of

researchers, marine resource managers, policy

makers and climate change specialists. POKM is

supported by the CANARIE network (Canada’s high

bandwidth network) that enables the rapid collection

and integration of high-volume oceanographic data

and knowledge (in text format) from distributed

sites, and to broadcast the high-dimensional results

to users across the world (

Barjak, 2006).

The design approach for POKM is to integrate

Knowledge Management (KM) technologies with

Service Oriented Architectures (SOA). In order to

meet the abovementioned functional capabilities of

the e-research platform—i.e. POKM—we pursued a

high-level abstraction of ocean and marine science

domains to establish a high-level conceptual

interoperability between the two domains. This is

achieved by developing a rich domain ontology that

captures concepts from both domains and

interrelates them to establish conceptual,

terminological and data interoperability. To define

the functional aspects of the e-research services we

363

Daniyal A., Abidi S., AbuSharekh A., Kuan Wong M. and S. R. Abidi S..

A KNOWLEDGE-CENTRIC E-RESEARCH PLATFORM FOR MARINE LIFE AND OCEANOGRAPHIC RESEARCH.

DOI: 10.5220/0003101703630366

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2010), pages 363-366

ISBN: 978-989-8425-30-0

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

have developed a services ontology that provides a

semantic description of knowledge-centric e-

research services. These semantic descriptions of the

e-research services are used to both establish

correlations between domain and functional

concepts that are the basis for data and knowledge

sharing, cataloguing and visualization.

In this paper, we present the knowledge

management functionalities achieved through the

definition of the domain and service ontologies.

2 KNOWLEDGE RESOURCE

LAYER

The knowledge resource layer constitutes:

• Data repositories for ocean data, marine life data

and simulated data generated through various

simulations.

• Domain-specific knowledge represented as

research papers and technical reports.

• Domain-specific information represented as

images, movies, audio and posters.

• Simulation models shared by the researchers.

2.1 Ontological Modeling of the

Domain

The domain ontology in POKM serves as the formal

semantic description of the concepts and

relationships pertaining to the Marine Biology and

the Oceanography domain. POKM provides a core

ontology that contains concepts necessary for

modelling Marine Animal Detection Data (MADD),

Oceanography Data, data transformations and

interfaces of the web services in POKM.

The taxonomic hierarchy of the domain ontology

constitutes 20 highest level classes.; 15 of these

classes are further decomposed into sub-classes at

the lower levels of hierarchy.

2.2 Modelling Marine Sciences

There are six upper level classes related to marine

sciences—i.e. M

ARINEORGANISM, ANIMALDETAIL,

AXONOMY, TAXONID, MARINELIFEDATA,

ARINELIFEDATACOLLECTION, DATASOURCE,

ATAFORMAT.

ARINEORGANISM represents all marine

animals, plants and plankton via classes

ARINEANIMAL, MARINEPLANT and PLANKTON

respectively. There are four main subclasses of

ARINEANIMAL: FISH, MARINEMAMMAL, REPTILE

and S

EABIRD. MARINEPLANT has two main sub-

classes: A

LGAE and SEAGRASSES. Plankton has three

sub-classes representing three functional groups of

planktons: B

ACTERIOPLANKTON, PHYTOPLANKTON

and Z

OOPLANKTON.

NIMALDETAIL represents all the necessary

information to build a marine animal profile. It has

five main sub-classes: A

GE, LIFESTAGE,

OVEMENTBEHAVIOR, SEX, TAGID. AGE represents

the age of the animal. L

IFESTAGE represents the

current stage of the animal in life, e.g. adult,

juvenile, sub-adult etc. M

OVEMENTBEHAVIOR

represents various movement behaviors of marine

animal that are captured by its sub-classes:

EHAVIORALSWITCHING, DISPERSAL, DIVING,

RIFT, FORAGING, MIGRATING and

OVEMENTPATTERN.

AXONOMY represents nine main taxonomic

ranks used to categorize marine organisms as

follows: C

LASS, FAMILY, GENUS, KINGDOM, ORDER,

PHYLUM, SCIENTIFICNAME,

SCIENTIFICNAMEAUTHOR and SPECIES.

AXONID describes an organism in terms of the

above mentioned nine taxonomic ranks.

ARINELIFEDATA represents various aspects of

the data about the marine organisms. These include

temporal data represented by sub-classes:

AYCOLLECTED, MONTHCOLLECTED,

YEARCOLLECTED, DATELASTMODIFIED and

IMESTAMPCOLLECTED, which has two sub-classes

of its own: E

NDTIMESTAMPCOLLECTED and

TARTTIMESTAMPCOLLECTED. The class

ARINELIFEDATA is also used to represent concepts

related to the cache of the marine data that is

represented using sub-classes such as C

ACHEID,

RECORDLASTCACHED, BASISOFRECORD and

ESOURCEID.

ARINELIFEDATACOLLECTION is a class the

properties of which are used to capture all the data

represented by class M

ARINELIFEDATA.

2.3 Modeling Ocean Sciences

The classes to model ocean sciences include:

CEANREGION, OCEANPARAMETER,

SATELLITEINFORMATION, INSTRUMENT, MEASURE,

MOVEMENTMODEL, MODELATTRIBUTE, FILETYPE,

CEANREGION represents all ocean regions

categorized by five main sub-classes:

RCTICOCEAN, ATLANTICOCEAN, INDIANOCEAN,

PACIFICOCEAN and SOUTHERNOCEAN. Each of these

classes are further sub-divided into sub-classes

representing sub regions of the each ocean region.

CEANPARAMETER represents all the

geophysical parameters used to describe an ocean

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environment. These are modeled as sub-classes of

this class and include parameters about:

• Air, such as: A

IRTEMPERATURE,

• Wind, such as: W

INDGUST, WINDSPEED,

EASTWARDWIND, NORTHWARDWIND,

UPWARDWIND and WINDFROMDIRECTION

• Water, such as: W

ATERDEPTH,

WATERTEMPERATURE, SALINITY and DENSITY,

• Current, such as: C

URRENTTODIRECTION,

EASTWORDCURRENT, NORTHWARDCURRENT,

FLOWVELOCITY and UPWARDCURRENT

• Sea Layers, such as: S

EASURFACEELEVATION,

SEASURFACETEMPERATURE and THERMOCLINE

ATELLITEINFORMATION represents the satellite

used to monitor the oceans, represented in terms of

nine sub-classes: S

ATELLITEID, ALTITUDE,

BESTSIGNALSTRENGTH,

FREQUENCYOFTRANSMISSION, ELAPSEDTIME,

NUMOFMESSAGESRECIEVED,

NUMOFSUCCESSFULPLAUSIBLECHECKS,

QUALITYINDICATOR and SENSORCHANNEL.

NSTRUMENT represents all the instruments used

for the observation of oceans and to measure various

parameters, such as: temperature, salinity and

density of the ocean water, ocean currents, depth,

pressure, etc. These instruments are represented as

the following sub-classes: ADCP,

ARGOS,

ARGOFLOAT, CTD, ELECTRONICTAG, GLIDER,

GLOBALPOSITIONINGSYSTEM, SATELLITE and

UBMERSIBLERADIOMETER.

EASURE represents all the spatial and temporal

measures of the regions used in the domain of Ocean

Sciences, and are modelled as two main sub-classes

PATIALMEASURE and TEMPORALMEASURE

respectively. The sub-class S

PATIALMEASURE has

further sub-classes: H

EIGHT, LATITUDE, LONGITUDE

AND

SPATIALRESOLUTION representing the

respective spatial measures of the relevant ocean

region. T

EMPORALMEASURE has two sub-classes:

IMEINTERVAL and TIMERESOLUTION, representing

the respective temporal measures.

OVEMENTMODEL represents various models

used to estimate the migrating and foraging

behaviors of marine organisms and their movement

parameters such as determining the next positioning

estimate of an animal after a period of missing data.

These models are represented as sub-classes:

IRSTPASSAGETIME, FRACTALANALYSIS,

GEOLOCATIONMODEL, KERNELANALYSIS,

STATESPACEMODEL.

NIT represents all the units used to measure

geophysical parameters describing an ocean. It has

nine sub-classes: D

ENSITYUNIT, DEPTHUNIT,

LIGHTLEVELUNIT, SALINITYUNIT,

SPATIALRESOLUTIONUNIT, SPATIALUNIT,

TEMPERATUREUNIT, TIMEUNIT, VELOCITYUNIT.

2.4 Relationships Between Classes

In addition to providing semantics for modelling

different resources on the POKM system, the

purpose of the domain ontology is to inter-relate the

domains of Marine Sciences and Ocean Sciences.

There are seventy seven object properties and six

datatype properties. We describe only the salient

properties are described in this section.

The class M

ARINEANIMAL (sub-class of

ARINEORGANISM) is related to respective sub-

classes of the class M

ARINELIFEDATA through

properties has_age, has_sex, has_life_stage,

has_movement_behavior and has_TagID. In

addition it is also related to class O

CEANREGION

through property has_geographic_area. Thus, this

property relate the domains of marine sciences and

ocean sciences.

The class O

CEANPARAMETER is related to class

Unit through property has_unit. This property is

given hasValue restriction, to restrict the filler of the

property to a specific instance of the class U

NIT. For

example AirTemperature, which is an

OceanParameter has_unit Degree Celsius, which is

an instance to class U

NIT.

The class M

ARINELIFEDATACOLLECTION is

related to respective sub-classes of class

ARINELIFEDATA through properties

has_basis_of_record, has_cache_ID, has_date

_last_modified, has_day_collected, has_depth,

has_depth_precision, has_latitude, has_longitude,

has_month_collected, has_record_last_cached,

has_record_ID, has_taxon_ID, has_temperature,

has_time_of_display_collected,

has_time_zone_collected and has_year_collected.

Each one of these properties is a functional property.

The class M

OVEMENTMODEL is related to

respective sub-classes of class M

ODELATTRIBUTE

through properties: has_hierarchical,

has_input_data, has_linearity,

has_observation_error, has_output, has_statistical_

estimation_method, has_statistical_framework,

has_stochasticity and has_time_value.

Each O

CEANREGION is related to various

CEANPARAMETERS through properties:

has_density, has_flow_velocity, has_salinity,

has_sea_surface_elevation, has_water_depth,

has_water_mass and water_temperature. Class

CEANREGION is also related to respective sub-

classes of class M

ARINELIFE through sub-classes

has_marine_animal, has_marine_plant and

has_plankton. Note that these three sub-classes

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relate the ocean sciences domain with marine

sciences domain.

The class T

AXONID is related with respective

sub-classes of class T

AXONOMY, in order to capture

the identification features of each of the marine

species. These properties are: has_class, has_family,

has_genus, has_kingdom, has_order, has_phylum,

has_sceintific_name, had_scientific_name_author

and has_species. Each one of these properties is a

functional property.

The class S

ATELLITE is related to respective sub-

classes of S

ATELLITEINFORMATION through

properties: has_altitude, has_best_signal_strength,

has_elapsed_time, has_frequency_of_transmission,

has_num_of_messages_received,

has_num_of_successful_plausible_check,

has_quality_indicator, has_satellite_ID and

has_sensor_chanel.

3 MANAGING DATA-SOURCES

POKM provides users the ability to procure Marine

Animal Detection Data (MADD) and oceanographic

data from multiple sources. Oceanographic data is

stored in netCDF format and is accessible by means

of standardized methods. However MADD is

normally stored in a relational database with no

standard relational structure. The relational structure

used to store MADD varies from one data source to

another. To support this functionality, we have

modelled MADD using the domain ontology. Our

approach for modelling MADD renders it possible

for users to (a) integrate additional MADD sources;

and (b) develop a high-level querying mechanism to

enable data access from heterogeneous MADD

sources.

In order to retrieve MADD from different sources,

POKM needs to access different relational

structures. We employ the domain ontology to

provide that common vocabulary. We define each

MADD source as a D

ATASOURCE in the domain

ontology, whereby each D

ATASOURCE has a number

of tables captured by instances of the T

ABLE class.

Each instance of the class T

ABLE has a number of

OLUMNs related to it, which in turn is mapped to a

concept in the domain ontology.

In addition to facilitating end-users in writing

detailed queries to obtain data from MADD sources.

POKM also requires some high level querying

mechanisms to enable certain functionalities on the

portal. For example to be able to query MADD

using just the temporal coverage and bounding

boxes (spatial coverage) requires a query specific to

the relational structure of the MADD source.

4 CONCLUSIONS

POKM presents a highly distributed e-research and

e-science environment that enables researchers

distributed across multiple locations are able to

collaborate through a suite of services

The oceanographic research community

generally uses a wide variety of internet resources

(projects, frameworks, systems) in their research.

The usual approach, particularly for researchers, has

been to download public oceanographic data onto

their workstations for integration with privately held

biological data, and then to conduct the required

analyses. There are vast volumes of oceanographic

data repositories, specialized models and data

analysis tools that can be leveraged to pursue more

comprehensive and complex studies. The efficacy of

POKM has been noted by researchers in terms of (a)

access to global animal tracking observations,

including the animal tracking data provided by

OTN; (b) access to a range of models that can be

used to interpret, interpolate and extrapolate the

animal tracking data; (c) interpretations of animal

movement and its causes through evaluation of

model uncertainty through a multi-model approach;

and (d) most importantly interoperability of data and

terminology between the ocean and marine life

science communities so that they are now able to

collaborate more effectively. POKM users are now

able to interconnect distributed raw data, simulation

models and knowledge resources into an integrated

‘knowledge asset’ to advance scientific exploration

programs.

ACKNOWLEDGEMENTS

This project is supported by a grant from CANARIE

(Canada). The authors acknowledge the support of

members of the entire POKM team.

REFERENCES

Cummings, J. N., Kiesler, S., 2005. Collaborative research

across disciplinary and organizational boundaries. In

Social Studies of Science, vol. 35(5), pp. 703-722.

Bos, N., Zimmerman, A., Olson, J., Yew, J., Yerkie, J., E.

Dahl, 2007. From shared databases to communities of

practice: A taxonomy of collaboratories. In Journal of

Computer-Mediated Communication, vol. 12(2), pp.

652-672.

Barjak, F., 2006. Research productivity in the internet era.

In Scientometrics, vol. 68(3), pp. 343-360.

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