This paper discusses the Ontology-driven Interactive Search Environment for Earth Sciences (ODISEES) project currently being developed to aid researchers attempting to find usable data among an overabundance of closely related data. ODISEES' ontological structure relies on a modular, adaptable concept modeling approach, which allows the domain to be modeled more or less as it is without worrying about terminology or external requirements. In the model, variables are individually assigned semantic content based on the characteristics of the measurements they represent, allowing intuitive discovery and comparison of data without requiring the user to sift through large numbers of data sets and variables to find the desired information

Huffer, Elisabeth B.

Kusterer, John M.

Quam, Brandi M.

Rutherford, Matthew T.

NASA Technical Reports Server

ODISEES: Ontology-driven Interactive Search Environment for
Earth Sciences
Matthew T. Rutherford1, Elisabeth B. Huffer2, John M. Kusterer3, Brandi M. Quam4
1,2 Lingua Logica LLC, NASA Langley, Atmospheric Science Data Center, Hampton, VA, United States
3,4 Science Directorate, NASA Langley, Atmospheric Science Data Center, Hampton, VA, United States
Abstract— This paper discusses the Ontology-driven Inter-
active Search Environment for Earth Sciences (ODISEES)
project currently being developed to aid researchers attempt-
ing to find usable data among an overabundance of closely
related data. ODISEES’ ontological structure relies on a
modular, adaptable concept modeling approach, which allows
the domain to be modeled more or less as it is without
worrying about terminology or external requirements. In
the model, variables are individually assigned semantic
content based on the characteristics of the measurements they
represent, allowing intuitive discovery and comparison of
data without requiring the user to sift through large numbers
of data sets and variables to find the desired information.
Keywords: ontology, semantics, data discovery, search
1. Introduction
Over the past few decades, researchers have often been
faced with a unique and very modern problem: the overabun-
dance of usable, relevant data. The exponentially shrinking
cost and size of computer hardware components has enabled
the storage of vast quantities of data, and the emergence of
the Internet has enabled near-instantaneous dissemination of
this data. As a result, finding precisely the right data can
be like searching for the proverbial needle in a haystack.
Oftentimes, researchers must sift through large volumes of
closely related information in order to uncover the desired
usable information buried there. This is, in many ways, the
opposite of what researchers have had to deal with in the
past where data was generally much less abundant and more
difficult to assess. As such, the scientific and information
technology communities face the ever-growing challenge of
storing, managing and distributing vast amounts of data and
providing user-friendly tools to the scientific communities
that hope to use the information therein. The Ontology-
Driven Interactive Search Environment for Earth Sciences
(ODISEES) project seeks to offer an alternative to traditional
methods of storage and organization.
There is a wealth of Earth science data—atmospheric,
geological, meteorological, hydrological, etc.—that has grown
rapdily over the past few decades. These data are produced
through a variety of collection methods and technologies
and interpreted by scientists and researchers from a wide,
heterogeneous set of disciplines for a similarly large and
varied set of purposes. Earth science researchers are often
faced with two significant, recurring challenges:
1) Finding data products that are immediately relevant to
their research
2) Quickly noticing and understanding the similarities
and differences among closely related products and
assessing their suitability for a particular purpose
As one of the Distributed Active Archive Centers (DAACs)
under the umbrella of NASA’s Earth Observing System Data
and Information System (EOSDIS), the Atmospheric Science
Data Center (ASDC) at the NASA Langley Research Center
is a steward of large amounts of Earth science data. The
ASDC’s purpose is to make its petabytes of data holdings
easily available to the public, serving a variety of users [1],
including scientists and researchers as well as the general
public. Given that scientists and laymen alike are actively
accessing and using its archives, the ASDC is met with the
unique challenge of catering to a user base with inconsistent
knowledge of its data holdings.
The use of ontologies and semantics has emerged as one of
many solutions to address these issues. Our implementation
of this solution, ODISEES, is being developed to provide
researchers with tools for discovering and assessing available
ASDC data products.
2. Ontology
There are many interpretations of the term “ontology”.
Long used by philosophers as a conceptual tool for studying
the conditions of existence and for classifying that which
exists, ontology has more recently been interpreted and im-
plemented by computer and information scientists—primarily
as a computer-readable artifact that can represent the entities
and relationships in a domain of interest.
Historically, ontology-based solutions have often started
out ambitiously. Researchers in Artificial Intelligence, for
instance, once looked to ontology and deductive reasoning
systems in an attempt to create truly intelligent systems that
could overcome the limitations of expert systems. The Cyc
project [2], [3] is one such project, designed to represent the
contextual common sense knowledge that humans take for
granted when they engage in deductive reasoning. However,
backlash over the years against AI in general, deductive
reasoning systems like Cyc in particular [4], [5], [6], and top
https://ntrs.nasa.gov/search.jsp?R=20160006496 2019-08-31T03:05:49+00:00Z
down ontologies, which try to impose context-independent
structure, resulted in less ambitious use cases for ontology.
At the same time, the Semantic Web was developing and
ontologies emerged as a means for controlling vocabularies
and bolstering the exchange of information on the Internet [7].
The Resource Description Framework (RDF) [7], [8], which
effectively merged ontology and XML, was developed. Since
then, the word “ontology” has largely been used to refer to
controlled vocabularies that are used to provide semantic
content for marking up data objects for the Internet. In this
context, the reasoning and inference capabilities that were
critical to AI applications became less important.
While less ambitious uses of ontology have become
more widely accepted, the term “ontology” continues to
be subject to multiple interpretations. One of the more
popular definitions of ontology in an information and
computer science context comes from Tom Gruber [9]: “...an
ontology defines a set of representational primitives with
which to model a domain of knowledge or discourse. The
representational primitives are typically classes (or sets),
attributes (or properties), and relationships (or relations
among class members).” Some liberal interpretations can
include taxonomies, relational models, and XML, but these do
not provide any semantics. At the other end of this interpretive
spectrum are full-fledged logical theories of domain that
rely on first-order, second-order, and even modal logic. In
developing ODISEES, we were interested in the latter, more
robust interpretation and application of ontology: artifacts that
model a domain with a good deal of precision and leverage
the inferential powers of first and second-order logic.
3. Methodology
ODISEES relies on the ontological classification of mea-
sured phenomena represented in ASDC data products, using
the resulting characterization of the data to enable effective,
expedited discovery and comparison of said data. It was
designed to perform three primary tasks in service to data
search and discovery:
1) Model the set of objects and concepts that make up
the Earth science domain and the relationships among
them in order to provide a common domain model
to impart meaning to the terms used to describe the
domain
2) Identify and define the terms used by specialized
user groups within the Earth science community,
mapping these terms to the common domain model and
creating computer-readable definitions of community-
based terms
3) Be usable by humans, databases, and applications that
need to interpret model to impart meaning to the terms
used to describe the domain
The ODISEES search system is comprised of open-source,
commercial-off-the-shelf (COTS), and custom software com-
ponents that interact with an Earth science ontology and
Fig. 1: Variables’ semantic content. Variable names are on
the left; semantic content is on the right. These attributes
were ascertained by examining actual variables and data sets
instead of requirements.
metadata repository to offer long-term solutions to the
problem of data discovery in the era of Big Data. The
ODISEES ontology and metadata repository provides an
ontological and lexical framework for describing ASDC data
holdings, as well as support for deductive reasoning and
querying capabilities. The aforementioned RDF format was
chosen to represent this information. An intuitive web-based
user interface was developed, allowing users to quickly sort
through and analyze relevant portions of the ontology in
order to locate the desired data.
3.1 Concept Modeling
ODISEES was, in large part, developed around the idea that
recognition is generally faster, easier, and simpler than recall.
As such, the model itself is not overly concerned with termi-
nology or nomenclature, and instead puts greater emphasis
on the semantics and relationships of concepts. The ontology
was structured with the intention of merging a controlled
vocabulary with useful deductive reasoning systems that can
support semi-intelligent applications. Controlled vocabularies
can, however, be overly restrictive and cumbersome for some
types of applications, such as text-based searches, insofar as
they often require significant effort on the user’s part to either
memorize a lot of terms or spend a lot of time looking up the
correct term. Instead of using this kind of terminology-centric
approach, the ODISEES ontology focuses on modeling the
domain concepts that give meaning to terms, and treats these
terms as first-class objects that refer to the concepts in the
domain.
For example, a data variable is described in the ontology
as a set of relationships between it and other domain objects.
A Radiative Flux variable will have a relationship to a certain
Fig. 2: Data set and variable class models. The data set model is at the bottom right, and the variable model at the top left.
Note the line connecting the two models. This indicates that the variable class is an attribute of the data set class.
wavelength or spectral range. The relationship is represented
as a binary function that takes a particular atmospheric
radiation variable, such as “CERES SW TOA Flux Upwards”,
and a particular spectral range, such as “shortwave”, as
arguments. Each variable’s set of attributes is determined
by analyzing the variable itself, the data sets in which it
is included, metadata about the data set, and reviewing
product documentation as well as consulting with domain
experts. Taken together, the set of relationships describe
the variable in a machine-readable format with sufficient
detail to allow a scientist to assess, with reasonable precision,
the essential characteristics of the observation or model
output represented. This representation provides maximum
flexibility and extensibility in describing Earth Science data
and model outputs because, at any point, new domain objects
can be introduced, new relations can be defined, and new
relationships among variables and other domain objects can
be asserted, without requiring any changes to the underlying
data model. As a result, variables, or indeed any object
in the model, can be identified and evaluated strictly in
terms of their defining attributes and without regard for the
naming convention that may have been followed in labeling
it. Figure 1 provides an illustration of how this semantic
content is attached to variable names.
Each attribute of a given object in the ODISEES model is
itself an object and has a label or name. Variable objects are
themselves attributes of the data set objects. Figure 2 shows
the concept models for both the variable class and the data
set class.
A primary strength of this kind of model is that even if
the application requirements change, the model can be easily
adapted to accommodate these changes. For the ODISEES
search application, we want some of the attributes of a data set
to be inherited by the variables within that data set. Similarly,
if characteristics of the remote sensing instrument used to
create the data set have implications for the data it produces,
we want the data set or the variables within it to inherit
whatever attributes are implied. For example, if a sensor is
calibrated to measure radiation in a particular spectral range,
the spectral range associated with a data variable produced
by that instrument can be automatically inferred from the
fact that it was produced by that instrument. The inferential
capabilities of our logic-based model were used to materialize
many of the assertions we wanted to drive the application.
3.2 Variable Discovery and Comparison
As mentioned in the beginning of Section 3, the core
purpose of ODISEES is not just to represent or conceptualize
information, but rather to make information more discoverable
and thereby usable. More specifically, ODISEES is designed
to leverage the concept modeling system described in
Section 3.1 to enable users to more easily and effectively
discover and compare variables from many distinct data sets.
To this end, ODISEES uses semantic content attached to
Fig. 3: Attribute filter selection with the ODISEES user
interface.
Fig. 4: Variable comparison with the ODISEES user interface.
individual variables. This content is encoded as a set of
RDF assertions that uniquely describes each variable. We
say “uniquely” because, even though variables may have
many attributes in common, the complete set of assertions
that describes any given individual variable is unique to that
variable.
The set of RDF assertions that describe the data variables
are used as filters that let users specify criteria and thereby
narrow their search to a set of results that satisfy all and
only those criteria. Figure 3 shows a set of filters and the
variables which satisfy the selected options as they appear
in the ODISEES user interface.
Once the desired variables are selected, users can generate
a comparison table, which displays each variable’s respective
semantic content items and highlights differences between
them to aid and inform the comparison process. Figure 4
shows this comparison feature in the ODISES user interface.
3.3 Results
The ODISEES beta version is currently deployed and
maintained at the ASDC, and is accessible to the public at
odisees.larc.nasa.gov. We are actively developing improve-
ments to the search capabilities and increasing the amount
of searchable data in the ODISEES repository. There are
currently 91 data sets represented in the ontology, and new
ones are being added regularly. The ontology is still relatively
small—400,931 RDF triples—but it’s expected to grow
significantly over the next two years. Initial user feedback
has been generally positive, with many users attesting to the
present and future usefulness of the tool. The test user group
is composed of developers and researchers from NASA,
NOAA, the EPA, multiple universities, and several other
organizations.
4. Discussion
The ODISEES project discussed in this paper presents an
adaptable, modular solution to some challenges posed by the
extreme abundance of closely related data. Furthermore, it
demonstrates the potential usefulness of ontology-based appli-
cations in solving issues posed by the growing overabundance
of data.
Several features, including text search, simple data subset-
ting with the Open-source Project for a Network Data Access
Protocol (OPeNDAP), and various improvements to the
web interface, are planned for future releases. Additionally,
development has begun on the Ontology-based Metadata
Portal for Unified Semantics (OlyMPUS) [10], a metadata
ingest system which leverages the same ontological structure
as ODISEES. The ODISEES-OlyMPUS end-to-end system
will support both data consumers and data providers, enabling
the latter to register their data sets and provision them with
the semantically rich metadata that drives ODISEES’ data
discovery and access service for data consumers.
5. Acknowledgments
The authors would like to thank the Atmospheric Data
Center at NASA’s Langley Research Center for their ongoing
support.
References
[1] ASDC, “About the Atmospheric Science Data Center,” 2015. [Online].
Available: https://eosweb.larc.nasa.gov/more-about-asdc.
[2] Cyc, “Cyc Homepage,” 2015. [Online]. Available: http://www.cyc.com/.
[3] D. Lenat, S. Laningham, “Doug Lenat on Cyc, a Truly Seman-
tic Web and Artificial Intelligence AI,” 2008. [Online]. Available:
http://www.ibm.com/developerworks/podcast/dwi/cm-int091608txt.
html.
[4] J. Friedman, “The sole contender for ai” in Harvard Science Review,
2003.
[5] D. Lenat, G. Miller, and T. Yokoi, “Cyc, wondernet, and edr: critiques
and responses” in Communications of the ACM, vol. 38, no. 11, 2003.
[6] J. Taubarer, “AI Founder Blasts Modern Research,” 2003. [Online].
Available: http://archive.wired.com/science/discoveries/news/2003/05/
58714?currentPage=all.
[7] D. Allemang, J. Hendler, Semantic Web for the Working Ontologist.
Elsevier, 2011.
[8] J. Taubarer, “What is RDF?,” 2006. [Online]. Available:
http://www.xml.com/pub/a/2001/01/24/rdf.html.
[9] T. Gruber, “Ontology” in Encyclopedia of Database Systems, 2009.
[10] J. Gleason, E. Huffer, A. Ross, P. McInerey, P. Mehrotra, P. Rinsland,
“Ontology-based metadata portal for unified semantics (olympus)”,
Proposal submitted in response to National Aeronautics and Space
Administration (NASA) Research Announcement (NRA) Advanced
Information Systems Technology Program, 2014.


ODISEES: Ontology-Driven Interactive Search Environment for Earth Sciences

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server