Guide to SWEET Ontologies

Rob Raskin

####### NASA/Jet Propulsion Lab

####### Pasadena, CA

####### raskin@seastar.jpl.nasa.gov

The ontologies within the Semantic Web for Earth and Environmental
Terminology (SWEET) provide an upper-level ontology for Earth system
science. The SWEET ontologies include several thousand terms, spanning a
broad extent of Earth system science and related concepts (such as data
characteristics) using the OWL language. The ontologies can be
downloaded from <http://sweet.jpl.nasa.gov/sweet>. To support such a
large collection and adhere to the guiding principles, the concepts are
divided, where possible, into orthogonal dimensions or *facets* in
support of reductionism. The primary ontologies are shown in Figure and
explained below. Each box represents a separate ontology, and a
connecting line indicates where major *properties* are used to define
concepts across ontology spaces.

![](media/image1.png){width="6.0in" height="4.5in"}

##### EarthRealm

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 are often 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. This ontology can
be considered a "state" of the planet that is extendable to past or
future time periods, (as well as to other planets).

##### Non-LivingSubstances

The non-living building blocks of nature include: particles,
electromagnetic radiation, and chemical compounds. These substances
constitute an ontology of physics and chemistry.

##### LivingSubstances

The living substances include plant and animal species. This ontology
was imported from the "biosphere" taxonomy of GCMD.

**PhysicalProcesses**

Physical processes include processes that affect living and non-living
substances, such as diffusion, evaporation, etc.

**PhysicalProperties**

A separate ontology was developed for physical properties, including
those observable or associated with other components.
*PhysicalProperties* include "temperature", "pressure", "height",
"2composition", etc., and could apply to *NonLivingSubstances,
LivingSubstances, PhysicalProcesses, etc.* These properties typically
are measured physical quantities (or qualities) with units.

##### 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.

**Time**

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, etc. Temporal relations include: after, before, etc.

**Space**

TheseT

Space is essentially a multidimensional 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, inlet, etc. Spatial relations include: above,
northOf, etc.

**Numerics**

Numerical extents include: interval, point, 0, **R**^2^, etc. Numerical
relations include: greaterThan, max. etc. We defined multidimensional
concepts, as these are not native to the OWL and XML environments.

##### PhysicalPhenomena

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 phenomena, spanning
approximately 50 events over the past two decades.

##### HumanActivities

This ontology is included for representing activities that humans engage
in, such as commerce, fisheries, etc. This ontology is included because
scientific processes and phenomena have human impacts, and there is a
need for representing such activities.

##### Data

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

These ontologies constitute a *concept* space for Earth system science.
The expression of a concept in *words* may differ from one person to
another depending upon their scientific community, cultural background,
language, and level of expertise. SWEET enables the same concept to be
represented using various phrases to satisfy these needs. Rather than
define a compound concept such as *air temperature*, we separated the
physical property (*temperature*) from the element that the property
applies to (*air*). This provides a more scalable solution to a growing
knowledge base. In this case, knowledge of the independent concepts of
the substance *"air"* and property "*temperature"* provide a complete
understanding of "*air temperature"* without a need to create an
explicit definition of the compound concept. Such a decomposition does
not preclude term recompositions, but the compound terms are designated
as synonymous with their integral parts. In other instances, the
compound concepts contain more meaning than their component parts (e.g.,
*static pressure*) and are explicitly included in the ontology.

##### 

A deficiency of RDF and OWL is that the languages contain no direct
support for numerical concepts, and must rely on a limited XSD (XML
Schema Definition) specification of datatypes. This spec defines number
types (e.g., floating point, unsigned integer) and methods to create
derivations of these types (e.g. the closed interval between 0 and 1),
but contains no operations or relations on these numbers. This is a
deficiency, because many scientific concepts are defined through numeric
concepts. For example, "brighter", "higher", "later", and "more
northerly" are special cases of the "greater than" relation, applied in
specific domains. In particular, spectral regions are defined in terms
of wavelength (e.g., visible light is between 0.4 and 0.7 micrometers),
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^. The *Numerics* ontology adds extensions
needed to define scientific concepts and is used to define concepts in
the spatial and temporal ontologies. Although other spatial and temporal
ontologies exist, none exploit the fact that space and time are
numerical scales. Without such a connecting thread, many numerical
concepts must be reinvented to create definitions of space and time.

The first several ontologies listed above represent orthogonal concepts
(or dimensions), often called *facets*. Traversing down a tree
associated with a facet follows the scientific path of reductionism by
adding additional details and specialization to more general concepts.
The unifying ontologies (phenomena and human activities) represent the
opposite approach, as these concepts are synergetic rather than mutually
orthogonal. These entries define holistic, synthesizing concepts using
elements from the faceted ontologies (e.g., a hurricane is associated
with particular coastal areas, and is characterized by high winds,
rainfall, flood impacts, etc.). Taken together, these two complementary
approaches mirror the scientist's dual methods of reductionism and
synthesis, and support a comprehensive framework for capturing
scientific knowledge and investigation.

Based on our experiences, we identified five design principles in
constructing scientific ontologies:

**Scalability:** An ontology should be easily extendable to enable
specialized domains to build upon more general ontologies already
generated.

**Application-independence:** The structure and contents of an ontology
should be based upon the inherent knowledge of the discipline, rather
than on how the domain knowledge is used.

**Natural language-independence:** The structure should provide a
representation of *concepts*, rather than of terms. The concepts remain
the same regardless of the inclusion of slang, technical jargon, foreign
languages, etc. Synonymous terms (e.g., marine, ocean, sea,
oceanography, ocean science) can be mapped separately to an ontology
element

**Orthogonality:** Compound concepts should be decomposed into their
component parts, to make it easy to recombine concepts in new ways.

**Community involvement:** Community input should guide the development
of any ontology.