Enabling Semantic Interoperability for Earth Science Data

*Final Report to NASA Earth Science Technology Office (ESTO)*

Rob Raskin

Jet Propulsion Laboratory

*Abstract-* **Data interoperability across heterogeneous systems can be
hampered by differences in terminology, particularly when multiple
scientific communities are involved. To reconcile differences in
semantics, a common semantic framework was created through the
development of Earth science ontologies. Such a shared understanding of
concepts enables ontology-aware software tools to understand the meaning
of terms in documents and web pages.**

This report updates last year\'s Semantic Web for Earth and
Environmental Terminology (SWEET) prototype. For the recent work, we
incorporated concepts of other funded initiatives such as ESML, ESMF,
grid computing, and OGC. We also created a system to update its
knowledge base as needed, from gazetteers and other on-line Web sources.
An accompanying search tool supports system-wide search and ultimately,
a wide range of semantically-based web services.

This report includes some background material that appeared in last
year's report that is repeated to convey a self-contained understanding
of the subject. This report concludes with road maps for various
technology initiatives.

> 1\. INTRODUCTION

Earth system science data originate from many disciplines, spanning
several community standards, terminologies, and data formats. Several
initiatives are underway to develop a common infrastructure to improve
data interoperability across the disciplines. Examples include the:
Earth Science Markup Language (ESML), Earth Science Modeling Framework
(ESMF), and the Open GIS consortium (OGC). Key to the success of these
initiatives is the development of a common semantic framework. Such a
framework enables dataset and science concepts to be understood by
software tools. The framework goes beyond *data* interoperability by
supporting *knowledge reuse*, or the exchange of *conceptual knowledge*
within and across these disciplines.

This framework can be achieved through the \"Semantic Web\" (Fensel, et
al., 2003), an ambitious extension to the existing WWW environment,
coordinated by the World Wide Consortium (W3C). The Semantic Web encodes
common sense knowledge directly into web pages themselves, using broadly
agreed upon namespaces and ontologies to define terms and their mutual
relationships.

The motivation of our task is to improve semantic understanding of web
resources by software tools, with specific application to discovery and
use of Earth science data. Semantic understanding of text by automated
tools is enabled through the combined use of *i)* ontologies and *ii)*
software tools that can interpret the ontologies. An ontology is a
formal representation of technical concepts and their interrelations in
a form that supports domain knowledge. Generally, an ontology is
hierarchical, with child concepts having explicit properties to
specialize their parent concept(s).

A Semantic Web emerges if terms on web pages are associated with
corresponding elements in ontologies. This is accomplished by placing an
XML tag around a term to identify its associated ontology namespace. A
search tool potentially can use these metadata tags to distinguish
different uses of the same term (e.g. "fall" as a season vs. "fall" as a
downward motion) to eliminate false hits. It also can locate resources
without having an exact keyword match, because terms such as "El Nino"
have an equivalent definition in terms of its defining scientific
components.

To support potential Semantic Web activities, we developed a collection
of ontologies for the Earth and environmental sciences and supporting
areas. We created a common sense knowledge base of the Earth sciences
using the Ontology Web Language (OWL) \[1\], a standard adopted by the
W3C. We use these ontologies in a prototype search tool that improves
performance by creating additional relevant search terms based on the
underlying semantics. We demonstrate how such a knowledge base can be
"virtual" by adding a wrapper around remote, dynamic data repositories.

1.1 SEMANTIC INTEROPERABILITY

In the early days of computing, an initial level of data
interoperability resulted when data structures (arrays) created on one
computer system were *readable* by another computer. Data formats such
as HDF emerged to extend this level of interoperability to more complex
data structures and across vendor platforms and enabled the preservation
of variable *names*. The Internet later brought on protocols such as
DODS, which supported modification of the data structure (subset
extraction) during the transfer. Exchanges of this type say nothing
about the scientific *interpretation* of the data on the receiving end.
A variable name is assigned to a data structure, but human intervention
is required to make sense of it.

The HDF-EOS format remedied the semantic interoperability problem for
*independent* variables by standardizing the naming convention of
spatial and temporal parameters. The Open GIS Consortium (OGC) provides
a similar level of spatial/temporal interoperability problem in its Web
Mapping Service (WMS) and Web Coverage Service (WCS) protocols. The
HDF-EOS and OGC solutions enable a data seeker to query and access data
by spatial/temporal parameters rather than by array row/columns (which
would require human intervention). Thus a software tool understanding
these conventions can access *any* HDF-EOS or OGC-compliant dataset and
be guaranteed that the spatial-temporal interpretation is known.

Semantic interoperability for dependent variables has generally meant
the use of controlled keywords. For instance, the NASA GCMD defines
approximately 1000 controlled keywords, each with a dictionary
definition. Such a representation does not support computer reasoning
that would be required to respond to general queries or chain services
together. It does not support inheritance of concepts for knowledge
reuse, does not provide a rich expression of the relationship between
the keywords and is not directly extendable by the user. This project
addresses a more scalable solution to semantic interoperability in the
context of the Earth sciences.

2\. ONTOLOGY DEVELOPMENT

An ontology is a formal representation of technical concepts and their
interrelations in a form that captures domain knowledge. Generally, an
ontology is hierarchical, with child concepts having explicit properties
to specialize their parent concept(s). Thus, "hydrosphere" is the parent
concept of "surface water", which is a parent of "river", which is a
parent of "Mississippi River", etc. In this paper, we describe our
experiences with the development of Earth and environmental science
ontologies.

In the initial year of ESTO funding, we created the Semantic Web for
Earth and Environmental Terminology (SWEET) \[2\] to prototype how a
Semantic Web can be implemented in the Earth sciences. We used the terms
in the Global Change Master Directory (GCMD) \[3\] as a starting point
in manually populating the ontologies, but reorganized and expanded the
concepts to form a scalable framework. Later, we incorporated an
analogous keyword list used in the Earth Science Modeling Framework
(ESMF) \[4\].

## Earth Realm

The "spheres" of the Earth constitute an *EarthRealm* ontology, based
upon the physical properties of the planet. Elements of this ontology
include "atmosphere", "ocean", and "solid earth", and associated
subrealms (such as "ocean floor" and "atmospheric boundary layer"). The
subrealms generally are distinguished from their parent classes, based
on the property of altitude, e.g., "troposphere" is the subclass of
"atmosphere" where elevation is between 0 and 15 km.

## 

## Non-Living Element (Substance)

This ontology includes the non-living building blocks on nature, such
as: particles, electromagnetic radiation, and chemical compounds.

*Living Element*

This ontology includes plant and animal species, imported from the GCMD
"biosphere" taxonomy.

### 

### Physical Property

A separate ontology was developed for physical properties that might be
associated with any component of *EarthRealm, NonLivingElements, or
LivingElements. PhysicalProperties* include "temperature", "pressure",
"height", "a3lbedo", etc.

### 

## Units

##  Units are defined using Unidata's UDUnits. The resulting ontology includes conversion factors between various units. Prefixed units such as km are defined as a special case of m with appropriate conversion factor.

## 

### Numerical Entity

Numerical extents include: interval, point, 0, **R**^2^, ... Numerical
relations include: greaterThan, max, ... We defined multidimensional
concepts such as coordinate systems, mathematical operators and
functions.

### Temporal Entity

Time is essentially a numerical scale with terminology specific to the
temporal domain. We developed a time ontology in which the temporal
extents and relations are special cases of numeric extents and
relations, respectively. Temporal extents include: duration, season,
century, 1996, ... Temporal relations include: after, before, ...

### 

### Spatial Entity

TheseT

Space is essentially a 3-D numerical scale with terminology specific to
the spatial domain. We developed a space ontology in which the spatial
extents and relations are special cases of numeric extents and
relations, respectively. Spatial extents include: country, Antarctica,
equator, ... Spatial relations include: above, northOf, ...

## 

## Phenomena

A phenomena ontology is used to define transient events. A phenomenon
crosses bounds of other ontology elements. Examples include: hurricane,
earthquake, El Nino, volcano, terrorist event, and each has associated
*Time*, *Space*, *EarthRealm*s, *NonLivingElements*, *LivingElements*,
etc. We also include specific instances of recent phenomena.

#### Human Activities

This ontology is included for representing impacts of environmental
phenomena such as commerce, fisheries, etc.

#### Data

The data ontology provides support for dataset concepts, including
representation, storage, modeling, format, resources, grid computing,
and distribution.

2.1. ONTOLOGIES AS A UNIFYING KNOWLEDGE FRAMEWORK

The first several ontologies listed above represent orthogonal concepts
(or dimensions), often called *facets*. Traversing down the tree
associated with a facet follows the scientific path of reductionism by
adding additional details to more abstract concepts.

A completely different type of ontology is encountered in "phenomena",
as this category is synergetic rather than orthogonal to the others. The
phenomena entries describe synthesizing concepts that utilize elements
from the other ontologies (e.g., a hurricane is associated with
particular coastal areas, and is characterized by high winds, rainfall,
flood impacts, etc.). Thus, phenomena are defined in terms of
combinations of elements from the faceted concepts. The "Human
activities" ontology also is a unifying, rather than reductionist
collection.

Taken together, these two complementary approaches mirror the
scientist's dual processes of reductionism and synthesis. This structure
provides a relatively complete framework for capturing scientific
knowledge. Using OWL, we relate concepts in these two approaches.
Generally, unifying concepts are built up and defined in terms of
individual facets. Alternatively, facets can be defined through
projection operations on unifying concepts.

2.2 ONTOLOGY LANGUAGES

An ontology is expressed using a *language* that is typically a
specialization of XML. XML is widely supported by existing software
tools and is platform-independent. The World Wide Consortium (W3C) has
adopted two XML languages as its standard method of representing
ontologies: the Resource Description Framework (RDF) and the Ontology
Web Language (OWL). Each of these languages is rich enough to express
the hierarchical structures inherent in knowledge representation. RDF
specializes XML by standardizing meanings for: class, subclass,
property, subproperty, domain, range, etc. OWL is a further
specialization of RDF; it adds standard meaning for: cardinality,
inverse properties, synonyms, and many more concepts in three versions:
OWL Lite, Owl DL, and OWL Full. The four languages (RDF, Owl Lite, OWL
DL, OWL Full) offer a nested set of language capabilities. We adopted
OWL Full due to its anticipated widespread acceptance over the coming
years. Our ontologies initially were written in the DARPA Markup
Language (DAML), a predecessor to OWL, and converted these ontologies to
OWL Full.

OWL has support for numbers only through a W3C specification \[5\]. This
spec defines number types (e.g., real numbers, unsigned integer) and
some abilities to create derivations of these types (e.g. the closed
interval between 0 and 1). It contains no operations or relations on
these numbers. This is a deficiency, because basic scientific concepts
are defined in terms of numeric concepts. For example, "brighter",
"higher", "later", and "more northerly" are special cases of the
"greater than" relation, when applied in specific domains. In
particular, spectral regions are defined in terms of wavelength (e.g.
visible light is between 0.3 and 0.7 nanometers), atmospheric layers are
defined by altitude (e.g. troposphere is between 0 and 15 km), etc. This
specification also has no notion of a multidimensional space **R**^n^.

Repositories of OWL ontologies exist to enable the work of others to be
extended. However, at present there are no ontologies supporting numeric
operations (e.g. "greater than", "max"). Several spatial and temporal
ontologies exist, but these ontologies do not exploit the fact that
space and time are numerical scales. Therefore, the numerical, space,
time, and event ontologies that we developed for SWEET will be submitted
to a general OWL ontology library.

XML-based languages such as OWL are well suited to data and model
exchange, but are less practical for storage and query of large
ontologies. Existing database management systems provide the needed
functionality in storage and indexing of robust ontologies, including
support for data integrity, concurrency control, etc. Consequently, we
adopted the Postgres object-oriented DBMS to store the names and
parent-child relations of our ontology elements. We created two-way
translators between the internal DBMS representation and the standard
XML representation of OWL properties. By placing all term declarations
in the DBMS, any search for terms is very rapid.

For representation of spatial concepts, we used bounding polygons to
describe regions, where possible. Polygons are a native datatype in
PostGRES.

3\. SEMANTIC INTEROPERABILITY WITH OTHER INITIATIVES

The Earth Science Markup Language (ESML) combines an XML-based language
for describing datasets with an API read library. Its XML tags are of
two types: syntactic (for reading data) and semantic (for interpreting
data). ESML no longer maintains semantic tags within its libraries and
relies instead on external ontologies to provide that functionality.
Thus, SWEET tags may be used to provide the semantic content of any ESML
file. Examples include: science subject, geographic coordinate system,
scaling factors & offsets, etc.

ESMF is an effort to make large Earth System models interoperable. Model
interoperability involves knowing input/outut compatibility and
parameter tables. We defined within SWEET the model parameters required
to ascertain model interoperability. ESMF also uses the list of 350
variable names, defined under the CF/Standard name conventions. Most of
these terms are concatenations of several terms (e.g.
temperature_at_top_of_boundary_layer). We mapped the terms to the SWEET
ontology, so that this list of terms could grow more naturally. We are
working with Cecelia DeLuca, Project Associate at UCAR, to ensure
compatibility between ESMF and SWEET, though there is no formal
commitment from the ESMF project to use our ontology at this time.

The Earth System Grid (ESG) \[6\] is a DOE-funded project to use grid
computing in support of Earth system modeling. We included the grid
concepts into the SWEET data ontology and are working with Line
Pouchard, ESG Project Associate at ORNL, to achieve consistency between
ESG and SWEET.

The Open GIS Consortium uses standard representations for coordinate
systems and geometric objects. While it was not practical to include all
of these entities in the SWEET spatial ontology, we included the more
widely used ones.

4\. DYNAMIC ACCESS TO ONTOLOGY ELEMENTS

Many Earth science facts reside in large external databases. We created
OWL wrappers to enable several of these database contents to be
accessible as if they were local ontology elements. The databases
include three gazetteers: CIA World Map \[7\], Getty Thesaurus \[8\],
and the Calle Global Gazetteer \[9\]. Gazetteers translate vernacular
names to and from geographic coordinates. We added polygon boundaries to
many gazetteer entries that otherwise contained only rectangular
bounding boxes. Also included are the USGS real-time list of earthquakes
\[10\] and the Heavens Above real-time list of satellite locations
\[11\]. A Web Map Server (WMS) \[12\] import capability was added to
acquire images and maps accessible through WMS-compliant servers. A
map-based interface demonstrates all of these capabilities by querying
the external sources in response to user requests.

The gazetteer entries generally include fields for bounding rectangle
but not bounding polygons. The polygon information is available
separately from other sources for state and international boundaries. We
inserted the bounding polygon data into the internal SWEET database. In
many cases, the size of the polygon exceeded what could be stored
natively in PostGRES, and we reduced the spatial resolution.

#  

5\. INTELLIGENT SEARCH ENGINE

A search tool that is aided by an ontology can locate resources without
having an exact keyword match. To demonstrate this capability, we
created a search tool that consults the SWEET ontology to find related
terms. These terms may be synonymous (same as), more specific (child
of), or less specific (parent of) than those requested. The tool then
submits the union of these terms to the GCMD search tool and presents
the results. The results verified that additional relevant terms were
found from the search, relative to the exact keyword search. The search
tool is implemented as a web service using the RQDL (RDF Query
Language). Once the synonyms and parent-child relationships have been
discovered, the augmented query returns resulting GCMD DIF summaries. An
extension of this search tool will be incorporated into the Earth
Science Information Partner (ESIP) Federation Interactive Network for
Discovery (FIND).

6\. ROADMAPS

The following mini-strategies describe opportunities for exploiting
semantic interoperability in future NASA and ESTO work. These
recommendations may be difficult to implement immediately, due to the
tendency of scientists to retain their narrow disciplinary perspectives.
But emerging demands for cross-disciplinary science and automated data
services will rely heavily on semantic interoperability.

###### Data Mining

The target of a data mining algorithm generally is a phenomena of
interest, as defined within the mining algorithm. The definition of the
phenomena often is hidden or incomplete, as there is no standard
language for its expression. A complete description (including
spatial/temporal resolution, model assumptions, etc.) is required to
enable community comparisons and review. By defining a target concept in
terms of ontology concepts, such a representation can be articulated.
This functionality is particularly important in on-board processing
systems, where knowledge must be reused to identify targets of interest
for enhanced data collection. It is recommended that data mining
activities be required to use the full expressive capabilities of an
ontology. Without a formal requirement, it is unlikely that algorithm
developers will voluntarily contribute this information.

###### Web Services

Based on current business application trends, it is likely that a wide
range of Web services will be established in the Earth sciences to
locate, acquire and use data. WSDL and UDDI are currently used to
describe and advertise services, respectively. WSDL and UDDI address the
semantics of requests, only to the extent that ontologies are referenced
in the service descriptions. It is recommended that future data-oriented
Web service descriptions be required to use the full expressive
capabilities of an ontology. This suggestion is especially pertinent in
a grid computing environment, where ontologies can describe what
services may be chained together and how this choreography is
implemented.

*Science Domain Specialist Involvement*

Obtaining review of existing SWEET ontologies has been very difficult.
This situation is due in part to the limited tools available for
ontology visualization, as the dimensionality of the semantic space is
very large. Dedicated workshops focusing on 3 --D walkthroughs of the
semantic space might inspire much greater community involvement in the
review process. Making this happen will require investments in the
relevant 3-D visualization technologies and support of workshops for
domain specialists.

###### Standards

Currently, NASA-funded Earth science data products must be classified
using the GCMD Science Keywords. It is recommended that this requirement
be relaxed to allow an alternative classification, such as
representation in a SWEET ontology. This requirement is of secondary
importance because we provide a transformation table on our Web site
between GCMD and SWEET representations.

REFERENCES

Fensel, D., J. Hendler, H. Lieberman, W. Wahlster (Eds.), 2003, Spinning
the Semantic Web, MIT Press, Cambridge, 479 pp.

INTERNET REFERENCES

\[1\] OWL. <http://www.w3.org/TR/owl-ref>

\[2\] SWEET. [http://sweet.jpl.nasa.gov](http://sweet.jpl.nasa.gov/)

\[3\] GCMD Science Keywords and Directory Keywords.
<http://gcmd.nasa.gov/Resources/valids>

\[4\] CF Standard name table.
<http://www.cgd.ucar.edu/cms/eaton/cf-metadata/standard_name.html>

\[5\] XML Schema Part 2: Datatypes. <http://www.w3.org/TR/xmlschema-2>.

\[6\] Earth System Grid.
[http://earthsystemgrid.org](http://earthsystemgrid.org/)

\[7\] CIA World Factbook.

<http://www.cia.gov/cia/publications/factbook/>

\[8\] Getty Thesaurus of Place Names.
<http://www.getty.edu/research/conducting_research/vocabularies/tgn/>

\[9\] Calle Global Gazetteer. <http://www.calle.com/world>

\[10\] Earthquake List for World.

<http://earthquake.usgs.gov/recenteqsww/Quakes/quakes_all.html>

\[11\] Heavens Above.
[http://www.heavens-above.com](http://www.heavens-above.com/)

\[12\] Web Map Server. [http://opengis.org](http://opengis.org/)