An Ontology to Support Knowledge Enabled Services on Earth Observation
R. A. Ribeiro, P. A. Sousa, N. Henriques, J. P. Pimentão, M. Pantoquilho, A. J. Falcão, R. Raminhos
UNINOVA- CRI/CA3
Caparica 2829-516, Portugal
rar@uninova.pt
Abstract
In this paper we present an ontology that is being developed in the frame of the European Space Agency
project, “Earth-Observation Knowledge Enabled Services” (EO-KES-B), reference number AO/1-4162/ISB. The main objective of this project module is to bridge the gap between understanding the ESA
products and their user communities. This effort requires the definition of an ontology and its semantic
description.
Most of today’s research and development on “information exploitation” is related to the development of
the Semantic Web [SemanticWeb]. This represents a reasonable paradigm where to mirror most of the
capabilities to be delivered. The Semantic Web is a future vision of the World Wide Web, originally
proposed by Tim Berners-Lee [Schawrtz], consisting of documents that are put together in such a way
that it facilitates automated information gathering and research in a far more meaningful way than can be
accomplished with current web search tools.
In this vision, the usability and usefulness of the Web and its interconnected resources can be enhanced
through:
SEMANTICS: Common metadata vocabularies (ontologies) and maps between vocabularies.
SYNTAXES: Documents ‘marked up’ with semantic information.
LOGIC, REASONING, OPERATION: Automated agents to perform tasks for users of the Semantic Web
using these metadata web-based services.
The EO-KES-B project aims at using these principles to support the search and processing of Earth
Observation (EO) data, specifically in the two domains selected for testing, “oil spills” and “harmful
algae bloom”, guiding the user as he translates his domain problem into the actual data supplied by ESA.
Knowledge representation is the central problem for the EO-KES-B project. The question is how to store
and manipulate knowledge in the EO-KES-B system in a formal way so that it may be used to extract
information out of data and to support users identifying the most appropriate products for their
applications in a kind of ‘intelligent’ information retrieval systems. For this purpose we use the following
semantic server architecture:
Knowledge Base
Database
Ontology
OWL DB
back-end
Protégé
HMI and Application service
Java API
EOKES-B
Application
XHTML
(Semantic Server)
OWL
File
Apache
(HTTP)
HTTP
Browser
Tomcat
(Servlet)
User
Lexical analysis
WordNet
WordNet
RDF/XML-ABBREV
File Format
UNINOVA
User
Knowledge
Server
Products
Server
GTD
The ontology is implemented in Protégé [Protégé], which is a generic inference engine that allows the
interaction and construction of knowledge bases. The codification of the knowledge is made through the
use of the OWL language [OWL]. To accommodate the expected system growth, a database will replace
the file system, but the OWL language will still be the used language to communicate with it. The
knowledge is stored in persistent formats using OWL encapsulated in XML and saved in the RDF/XML –
ABBREV files format. The Protégé tool has a built in Java API access, which will communicate with the
Semantic Server application built as a Servlet and served by means of an HTTP server with Java enabled
system (Apache + Tomcat). The service will be published as a Web Service following all the standards
and in synchronization with all the parts of EOKES-B system.
The ontology is composed of classes and subclasses organized around the key concept of “event” or
“feature”. The main concepts and relations are: An Event of a resource changes the state of the other
resource(s). Each event has a severity level and can be detected by a Meta Product through the analysis
of a set of properties that define it.
The event severity is a measure of the event’s criticality: it can be harmful, e.g. oil spill harmful, e.g.,
some algae blooms are toxic. An event can be a bloom of something (algae or any other resource) or a
spill of some resource, such as crude or refined oil, etc. The resources are organized in three major
categories: Organism, Artificial and Natural. The Organism is of a specific type (algae, animalia,
fungi or plantae) and belongs to a family, e.g., the algae can be of the rhodophyta family. The Natural
category of resources can include water, land and atmospheric resources, such as rivers, oceans or
forests. The Artificial category contains all other resources that are “man-tempered”, and that do not arise
from natural causes such as refined oil.
Each resource has a set of states that can be altered by the occurrence of an event. The identified states
are: appearance, e.g., the colour of the water, death, e.g., dead fish, injury, e.g., some algae may cause
injuries in the skin of some fish, and reproduction, e.g., bloom or fast increase of the reproduction rate of
a population. The Meta Product is a key concept in this system: it is either an operational product or a
transformed one. An operational product has a source that can be an instrument (from a satellite) or a
numerical model, e.g., ECMVF, Wavelength. Each operational product has a level that characterizes it,
e.g., L0, L1, and L2. A transformed product is an operational product transformed by an algorithm.
Every Meta Product has a particular data distributor and a set of properties to identify it.
In addition, to provide a more powerful consultation environment and to guide the user through the
concepts and related synonyms we incorporated the WordNet® dictionary [WordNet] on our ontology.
With this coupling, besides the concepts synonyms access, we can also perform a lexical analysis of a
composed sentence, as for instance “dead fish”.
Finally, to demonstrate the use of the semantic server knowledge base we will provide a user-friendly
interface, such as:
This interface is designed to facilitate the task of consulting and populating the knowledge base,
abstracting from the Protégé and Wordnet integration details. However, it is not yet completed and more
refinements are needed, as more sub-interfaces will be added to improve the user-friendliness and
capabilities of the semantic server knowledge base.
References:
[Schwartz]
Schwartz, D. G. “From Open IS Semantics to the Semantic Web: The Road Ahead” IEEE
Intelligent Systems, Vol 18, #3, May/June (2003).
[OWL]
http://www.w3.org/TR/owl-features/
[Protégé]
www.protege.com
[SemanticWeb] www.w3.org/2001/sw
[WordNet]
http://www.cogsci.princeton.edu/~wn/