SWEET Phase 1 Final Report

# Objective

The motivation for our task is improved semantic understanding of web
resources by software tools, with specific application to discovery and
use of Earth science data.

# TRL Assessment

The entry TRL is 2, based on existing experimental systems. The exit TRL
is 3, based on the search tool that we developed.

# Ontologies

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). Our primary task was ontology development specific to the
Earth and environmental sciences and supporting areas.

# Ontology Development

From our experience with ontology development, we concluded that the
following guiding principles are essential:

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

2.  **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.

3.  **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

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

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

Our starting point of reference was the collection of keywords in the
NASA Global Change Master Directory (GCMD)
<http://gcmd.nasa.gov/Resources/valids/index.html> . This collection
includes both controlled and uncontrolled keywords.

***Controlled keywords:*** GCMD includes approximately 1000 controlled
Earth science keywords, represented in a *taxonomy*. A taxonomy is a
subject classification, as used by libraries or clearinghouses to assign
a classification to a resource. In a taxonomy, properties are not passed
on from parent to child. This making this structure less suitable for
knowledge representation purposes. Several hundred additional controlled
keywords are defined for datasets and data services, including
instruments, data centers, missions, etc.

***Free-text terms:*** Over 20,000 uncontrolled keywords have been
submitted by data providers. These terms tend to be more general than or
synonymous with the controlled terms. Examples of frequently submitted
terms include: climatology, remote sensing, EOSDIS, statistics, marine,
geology, vegetation, etc.

Our original objective was to place a layer around the GCMD keywords, to
make it visible as an ontology. However, its taxonomy structure made it
difficult to map into an ontology. Instead, we used the GCMD keywords as
a guide in developing the following ontologies. Most notably, we
separated physical properties from the realms or elements that that the
properties apply to.

## 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, Hence, "troposphere" is the subclass of
"atmosphere" where elevation is between 0 and 15 km.

## Non-Living Element

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. It was imported from
the "biosphere" taxonomy of GCMD.

### 

### 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", "albedo", 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, ...

### 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, inlet, ...

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

## 

## Human Activities

This ontology is included for representing impacts of environmental
phenonema. It includes entries such as: commerce, fisheries, etc.

# Ontology Languages

An ontology is expressed using a *language* that is commonly a
specialization of XML. Resource Description Framework (RDF)
<http://www.w3.org/RDF/> is the simplest such ontology language. RDF
specializes XML by standardizing meanings for: class, subclass,
property, subproperty, domain, range, etc. The DARPA Markup Language
(DAML) and DAML+Ontology Inference Layer (DAML+OIL) are further
specializations of RDF. These languages add standard meaning for:
cardinality, inverse properties, synonyms, and many more concepts. DAML
is the most widely used ontology language and is being adopted by W3C as
its standard Ontology Web Language (OWL). We also adopted DAML for this
project, due to its widespread acceptance.

DAML has support for numbers only through a W3C specification
<http://www.w3.org/TR/xmlschema-2/> . 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 such as "brighter",
"higher", "later", or "more northerly" are special cases of the "greater
than" relation, when applied in specific domains. This specification
also has no notion of a multidimensional space **R**^n^.

The DAML web site [http://www.daml.org](http://www.daml.org/) maintains
libraries of ontologies developed by the community, to enable the work
of others to be extended. However, no libraries were there to support
numbers or to support spatial concepts. A few time ontologies exist, but
none took advantage of the fact that time is simply a numerical scale.
The number, space, time, and event ontologies that we developed are
being submitted to the DAML ontology library.

# Ontology Tools

General purpose DAML tools are available from the DAML web site, and
include JAVA and Perl parsers, editors, and visualization tools.
However, these products did not necessarily support all aspects of the
language. None of the editors supported numerical intervals (e.g. the
interval \[0,1\]). This is a deficiency for science applications, where
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.

# Storage of Ontology Elements

XML based languages (such as DAML) 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. There was no
DBMS API available for DAML, so we created two-way translators between
the internal DBMS representation and the usual XML representation of the
subclass and subproperty relations. By placing all term declarations in
the DBMS, any search for terms is very rapid..

# 

# Ontology-Aided Search

A search tool that is aided by an ontology can potentially locate
resources without having an exact keyword match. To verify this claim,
we created a search tool that consults the SWEET ontology to find
synonymous and more specific terms 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. During the
next stage of this project, we will develop metrics to quantify the
increased number of hits.

As a further enhancement to ontology-aided search, we explored methods
of automatically discovering associations between terms. For example,
the terms "carbon dioxide" and "global warming" would likely be high
associated, in the sense that when one term appears, the other is
relatively likely. We used the GCMD DIF summaries as the text, from
which we created an association matrix. We applied latent semantic
analysis (LSA), a method that uses empirical orthogonal functions to
find additional hidden associations between terms. We included these
association scores in the search tool that we developed. The use of DIF
summaries probably limits the value of this approach (as the summaries
are inconsistent in their content). Nevertheless, this exercise showed
that additional relevant associated terms could be automatically
extracted. In the next stage of this project, we will examine
alternatives to LSA that reduce the large computational requirements of
processing large matrices.

# Relationship to XML Data Description Languages

ESML and XDF are other specializations of XML that could be used to
define (and potentially discover) Earth Science datasets. ESML is
structured as a taxonomy rather than an ontology - it repeatedly
redefines existing concepts with new names, rather than reusing its
known concepts. This makes it difficult to list ESML terms in an
ontology; nevertheless, we include some of the major ESML terms within
SWEET. Through ongoing collaborations with UAH, we hope that in the
future, ESML terms will be defined either in SWEET or in an ESML
ontology. XDF is a closer match to an ontology representation, although
it lacks the Earth science extensions already in ESML.

# 

# Applications

Our primary application was data discovery, although numerous other
applications are likely to benefit from a greater semantic understanding
by information retrieval tools. The ESIP Federation Products and
Services Standing Committee plans to help build ontology-based metadata
based on each of the EarthRealms. In the process, we expect to give
special attention to the different needs of the ESIP Categories across
the spectrum of data gathering and distribution, science interpretation
and application building, and delivery and usage for public benefit and
decision support.

# Agents

# **We experimented with a network of agents to aid in solving specific types of queries that a user might submit. We assigned an agent to each of four possible query types, that might be representative of user requests. Each agent is a Perl script that tries to capture what types of problems it can solve directly, what types of problems it can guess a proper response, and what problems it must query the user for further information. The four query types are:** 

1\. Researcher, data specific ("Give me ASTER data for Ecuador, March
1997")

2\. Researcher, non-specific ("Give me near-infrared data for the
Amazon, 1996")

3\. Educated public ("Give me information related to the 1998 El Nino")

4\. General ("Show me my house")

# 

Request 1 maps well to the existing ontologies and can be easily
satisfied. Request 2 requires prompting user for specific data product.
Request 3 requires some assumptions to be made to satisfy request.
Request 4 requires significant assumptions to be made to satisfy. In
each case, the request is "passed up" the chain until it can be
satisfied. In simple cases, we found this approach to be promising.
However, much additional agent "learning" must be included for a robust
system to be successful on general requests.

# 

# Indexing Issues

The vision of the semantic web is for data providers to enter tags on
their web pages that provide an ontology meaning (or namespace) for
technical terms. These namespace tags could be used by search engines to
properly classify documents that it indexes. It is unclear whether web
page developers will mark up their pages in such a manner, as this
requires i) knowledge of the relevant ontologies and ii) time to create
the tags. We explored alternate approaches to automatic generation of
these tags during the indexing process itself. The results of processing
by semantic analysis tools could be used potentially to insert these
tags during the index process.

# Future Research

Much future research is needed to enable the semantic web vision to
become a reality. Of particular interest are automation of tasks now
being performed manually, including: automatic semantic acquisition,
automatic ontology population, and automatic query classification.
Accompanying these efforts should be a method of benchmarking, which is
necessary to compare our approach with others in the field. There also
is a need for better tools for manipulating ontologies. All of these
areas are likely to be addressed by the general ontology community, as
they are not specific to the Earth sciences.

# Phase 2

The next phase of SWEET is its integration into the Federation
Interactive Network for Discovery (FIND). This work will further develop
the semantic analysis tools and the agent network and will expand the
ontology itself. Completion of the follow-on task should advance the
effort an additional one or two TRLs..