=Paper=
{{Paper
|id=None
|storemode=property
|title=A Knowledge Base for Exploited Marine Ecosystems
|pdfUrl=https://ceur-ws.org/Vol-979/WS_s4biodiv2013_paper_10.pdf
|volume=Vol-979
}}
==A Knowledge Base for Exploited Marine Ecosystems==
https://ceur-ws.org/Vol-979/WS_s4biodiv2013_paper_10.pdf
A knowledge base for Exploited Marine
Ecosystems
Julien Barde1 , Pascal Cauquil1 , and Norbert Billet1
1
Institut de Recherche pour le Dévelopement, UMR EME 212, Sète, France
Abstract. In 2008, IRD started to work on setting up a knowledge
base (named Ecoscope) about Ecosystem Approach to Fisheries domain
(EAF) in the context of a marine ecology laboratory studying Exploited
Marine Ecosystems in different regions of the world. This application
was meant to fit the needs of researchers by improving knowledge and
related information resources management [14,12]. Among other goals,
researchers expected an information system enabling to provide an inven-
tory of available data sources (ecological observations, satellites images,
pictures, articles, reports..) and facilitating data rescue, data access, data
processes (indicators..) as well as the ability to summarize related knowl-
edge through fact sheets about domain entities (ecosystems, species..)
connected with hyperlinks (based on ecosystem relationships).
Beyond metadata, data management and related interoperability issues
(OGC, TDWG. . . ), this project was then a real opportunity to set up an
ontology for EAF domain in order to link existing information resources
with real-world entities (EAF domain concepts). To achieve these goals,
semantic Web standards and reference RDF schemas have been taken
into account (SKOS, Dublin Core, FOAF, OBOE, Darwin Core. . . ) and
a first version of RDF schema for EAF domain has been set up. These
ontologies have been instantiated to describe our information resources
and some knowledge about entities that researchers are studying.
A first Website has been set up on top of this knowledge Base. Related
Web pages consist mainly in fact sheets about domain entities (ecosys-
tems, top predators and related preys species, fishing vessels..persons)
where users can find related information resources (spatial layers, arti-
cles, pictures, indicators..). Knowledge can as well be summarized through
networks of entities like food webs with dedicated visualizations tools.
This is made possible by querying the knowledge base where Linked
metadata and data (in underlying databases) are tagged with related
species URIs. Proof has been done that Semantic Web languages can be
used to fit the needs of our colleagues. Moreover, in the context of iMa-
rine FP7 project, we started to deal with partners having similar projects
(FLOD from FAO, Worms, FORTH). We then set up a SPARQL end-
point and OpenSearch access to share the content our knowledge bases
with other applications (search engines, text mining applications..).
We will present our current application and related technical choices
as well as futur plans to connect additional data sources to enrich this
knowledge base and make it available for our partners. In particular we
will describe some use cases related to biodiversity management issues.
1 Ecosystem Approach to Fisheries
In this paper, we present our work on knowledge management applied to the
domain of Ecosystem Approach to Fisheries (EAF). According to FAO [5], EAF
is an approach that:
strives to balance diverse societal objectives, by taking into account the
knowledge and uncertainties about biotic, abiotic, and human compo-
nents of ecosystems and their interactions and applying an integrated
approach to fisheries within ecologically meaningful boundaries.
We used this definition to create a conceptual model as schown in the UML
diagram class of Figure 1.
Fig. 1. UML class diagram translating Ecosystem Approach to Fisheries definition [5]
This UML diagram has been used to create a first set of top-level classes
and properties for EAF domain (identifying real-world entities according to [2]):
ecosystems (areas), their components as well as their interactions. The Figure 2
shows some RDF triples for the class fish instantiated for the species exocoetus
volitans.
Similar objects have been created for hundreds of species, fishing vessels and
fishing gears. . . which are all part of different marine ecosystems.
EAF requires thus the management of knowledge related to marine ecosys-
tems and their abiotic, biotic and human components. However, this knowledge
comes from different scientific studies driven by researchers which generate var-
ious information resources. These entities have to be described as well.
Fig. 2. Examples of RDF triples summarizing our knowledge about species
2 Information resources related to EAF
Real-world entities of our domain (like species, fishing vessels, habitats. . . ) are re-
lated to different kinds of information resources (pictures, spreadsheets, databases,
satellites images, sensors data. . . )[15,16]. As illustrated in Figure 3, these entities
are related as well to some people (agents) who are driving the scientific studies
and generating the related information resources by running some processes.
Fig. 3. Information resources, processes and agents related to entities of the domain
As many other laboratories working on ecological studies, the inventory of
available data sources consists of various data types (which are subclasses of
”information resource” class in Figure 3):
– ecological observations from fieldwork (observers on-board fishing or sci-
entific vessels collecting samples for data analysis: size, stomach content,
isotopes, contaminants, fatty acids. . . ). These data are usually managed in
spatial databases (Postgres / Postgis) or spreadsheets.
– satellites images to characterize environmental parameters of species habi-
tats. These data are usually managed with binary formats (e.g. netCDF files
for series of images).
– pictures that are collected by on-board observers or scientists,
– articles, reports. . . published by researchers,
– processes to run data analysis with different programming languages (R,
IDL, Matlab. . . ).
All these resources are usually described and managed with specific (meta-
)data formats that impedes basic tasks like data discovery and retrieval. In addi-
tion to knowledge management, RDF is expected to facilitate data management
(seamless access to metadata catalogues, codelist mapping. . . ) by complying
with widely used schemas.
3 Underlying standards
The description and the management of the information resources has to comply
with well known standards to ensure that these resources will be made available
for various communities of users. In particular, we target communities related
to spatial, biodiversity and statistical information. In addition to information
resources management, we selected as well existing standards to manage in-
formation about agents and domain entities (species. . . ). An effort has been
required to transform these (meta)data in RDF with EAF domain URIs.
3.1 Schemas for information resources and related agents
Many standards enable the description of information resources. Most of them
consist in XML schema where keywords and mother metadata elements are de-
scribed with literals (for species, characteristics, fishing vessels, agents. . . that
are observed). This is the case with OGC metadata standards for spatial infor-
mation (19115, 19119, 19110 [13]. . . ), with metadata standards for biodiversity
data (Ecological Metadata language / EML, TDWG standards like Darwin Core
[18]. . . ), with SDMX for statistical datasets [17]. . .
In order to describe and retrieve our information resources by using common
metadata elements and URIs we decided to comply with following RDF schemas:
– DCMI [4] as metadata standard which can be used to describe any kind of
information resource,
– dclite4g [19] Information model for metadata about geospatial data. ISO
19139 or EML metadata can be converted into dclite4g RDF metadata,
– SKOS [11] for description of terms and definitions related to information
resources and concepts,
– FOAF [3] for description of agents (persons, institutes, projects) and their
relationships,
– BIBO [7] for bibliographic references.
We aim to describe and make some of our data available as Linked Open Data
by taking into account the 5 star development scheme for Open Data [2,1].
However, being able to describe information resources, processes and related
agents requires URIs for ecological entities described in Figure 1.
3.2 Standards for domain entities
Among existing RDF schema relevant for our domain, we can mention:
– Previous work on ontologies for ecoinformatics [21].
– OBOE for modeling and representing scientific observations [9,10],
– Darwin Core [20] for sharing of information about biological diversity,
– FLOD [6] for fisheries domain.
These ontologies have been taken into account to map our ontology classes
and properties as well as for raw data triplication.
3.3 RDF generation
We have two kinds of RDF triples which are generated from our information
resources:
– statements instantiating our ontology with real-world entities (ecosystems
and related components): species, fishing vessel, ecosystems. . . Each data
source provides a set of entities (species, environmental parameters. . . ) which
are translated into instances of our ontology,
– RDF description of information resources, including related agents. More
than basic descriptions, our goal here is to tag metadata with URIs of domain
entities (previous item).
For now, we have been using an ”ad hoc” approach to get RDF triples from
each type of information resource as illustrated in Figure 4. Moreover in some
cases, previous efforts can be reused:
– OGC metadata (ISO 19139) can be transformed in GENESI-DEC RDF
metadata by reusing an existing XSL file from GENESI-DEC project,
– EML metadata can be transformed in OGC 19139 metadata with a GBIF
XSL file,
– bibliographic references metadata can be exported in RDF (BIBO compli-
ant) from Zotero (as well as references of pictures, videos if managed with
Zotero).
In this case, the real challenge consists in adding some context to these RDF
metadata by relating them to URIs of domain entities. This can be achieved by
entity mining approach.
Fig. 4. Data sources to be triplified
4 RDF storage and server
Once classes and properties for EAF entities have been created and related
objects (including information resources and agents) instantiated, these ob-
jects have been loaded with JENA in a triple store (Jena with TDB, preferred
to Postgres for persistent storage and access) and have been made accessible
through a SPARQL endpoint (the ontology is available as well at this URL:
http://www.ecoscope.org/ontologies/main).
Another access has been set up to deliver search results through OpenSearch
protocol with different data formats: HTML, RSS and RDF (Semantic extension).
That was needed to enable a set of use cases and make the content our
knowledge base available online.
5 Related applications and products
In this section, we describe some use cases exploiting the content of the Ecoscope
knowledge base. Our first use case was the setting up of a Web portal built on
top of the ontology / knowledge base (through Jena for storage and access and
Struts2 to set up Web pages on top of Jena). The goal was to satisfy basic
use cases like data discovery and retrieval, knowledge summary about domain
entities (species, fishing vessels. . . ).
5.1 Metadata catalogue
One of the goal of using RDF metadata with URIs was to enable seamless
access to different metadata catalogues without having to deal with specific
standards. In particular OGC metadata (19115/39 used by INSPIRE), EML
metadata (GBIF), Bibliographic references, Dublin Core metadata have been
transformed to comply with a common set of metadata elements (cf section
3.1). Moreover these metadata are all anotated (and thus linked) with URIs of
domain entities (cf section 3.2). This approach enables to query resources related
to domain entities (e.g. yellowfin tuna) without having to restrict the search to
specific standards, languages or terms. The search engine suggests all the results
related to this entity and cluster the results according to their types Results
can be related entities (e.g. preys of yellowfin tuna) or information resources like
pictures, articles, databases, people. . . (see online application).
Fig. 5. Search engine for the Ecoscope knowledge base
More than inventorying existing information resources, our prior goal was to
summarize available knowledge by themes / domain entities. This has been done
by setting up fact sheets.
5.2 Fact sheets
The main purpose of our knowledge base was to feed the content of a Web
portal by providing the knowledge about entities of interest for our laboratory
(ecosystems, species, fishing vessels. . . ). The main goal was the creation of fact
sheets about these entities. To achieve this goal, A SPARQL query harvest all
the triples related to a given entities and Jena objects are used by Struts2 to
build some HTML views. Figure 6 gives an example of Web page for yellowfin
tuna.
The fact sheets cluster related entities by type of relationships (is predator
of, is prey of, is exploited by) and cluster related information resources by data
types (pictures, spatial data and related processes / indicators).
Other visualization interfaces are available to present RDF triples as taxon-
omy or network.
Fig. 6. Fact sheet about Yellowfin tuna
5.3 Visualization of a food web
This use case illustrates what can be achieved with previous ontologies when
applied to management of biodiversity data. The example of a food web is very
relevant as it shows relationships between entities (species) that are either preda-
tors, preys or both. In Figure 7, we filtered RDF triples of the knowledge base
to represent the food web related to tropical tuna trophic data (using prefuse
API [8] for visualization of data). This application is interfaced with relational
databases in order to enable users to spatially query the food web.
Fig. 7. Representation of a food web from RDF triples
The Figure 6 gives an example of Web page which is available online with
other visualization application (e.g. Taxonomy).
5.4 Matching service
Another use case consists in using the Ecoscope knowledge base for the mapping
of codification systems (code lists). For example, in EAF domain, species are
often identified either by FAO codes in fisheries datasets or, most of the time,
by Worms codification systems in ecological observations. This is an issue when
researchers need to run some processes which require cross analyses between
fisheries and ecological datasets. Indeed, in this case, there is a need to enable
mapping between codes to merge datasets. A first application has been developed
to enable such mapping at data export. We aim to deliver similar services in
a generic way (in a programmatic way or through GUIs) to enable mapping
between code lists of different schemas.
6 Outlooks
For now, RDF triples to link our data with entities and agents of the domain
have been created in various ways. To make the knowledge base sustainable in
the long term, we can’t afford to feed it with ad hoc approaches. There is a
need to harvest information from dedicated endpoints to facilitate updates by a
workflow:
– databases and netCDF files will be turn in RDF through a single data server,
– pictures that are collected by on-board observers or scientists,
– articles, reports. . . published by researchers will be exported in RDF from
Zotero Server,
– processes to run data analysis with different programming languages (R,
IDL, Matlab. . . ) will be described in RDF from OGC WPS metadata.
Another important improvement is related to the introduction of logical rules
to infer some knowledge. A simple example consists in infering competition re-
lationship between species from predation relationship. Indeed, two species are
competing when they are predators of the same species (preys). This first use
case is going to be implemented in the framework of iMarine FP7 program.
7 Conclusion
Our first application has demonstrated the interest of using ontologies and knowl-
edge bases to satisfy different needs of researchers in our marine ecology labo-
ratory: data discovery and retrieval, knowledge management and visualization
(fact sheets, food web. . . ). Such an application is worth but our current ”ad
hoc” approach still requires a lot of work to fill and update the database. To fix
this issue, we aim to enable a RDF export from the Web portal which is used to
access raw data (relationnal databases and netCDF files). We aim now to set up
a workflow to facilitate RDF generation directly from the relational databases
where ecological observations are stored and used by researchers to run scientific
analysis. As a second step, we want ecological observations (raw data) to be
made available as well in RDF and to be linked with existing RDF triples (sum-
marizing underlying knowledge: for example a RDF triple stating a predation
relationship between two species should be related to hundreds of observations /
facts proving it). The next goal consists in enabling RDF export from our main
data sources, as already done with other standards (OGC, TDWG, SDMX).
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