=Paper=
{{Paper
|id=None
|storemode=property
|title=Deep Semantics in the Geosciences: Semantic Building Blocks for a Complete Geoscience Infrastructure
|pdfUrl=https://ceur-ws.org/Vol-969/paper7.pdf
|volume=Vol-969
}}
==Deep Semantics in the Geosciences: Semantic Building Blocks for a Complete Geoscience Infrastructure==
https://ceur-ws.org/Vol-969/paper7.pdf
Short Paper:
Deep Semantics in the Geosciences: semantic building
blocks for a complete geoscience infrastructure
1,2 1
Brandon Whitehead, Mark Gahegan
1
Centre for eResearch
2
Institute of Earth Science and Engineering
The University of Auckland, Private Bag 92019, Auckland, New Zealand
{b.whitehead, m.gahegan}@auckland.ac.nz
Abstract. In the geosciences, the semantic models, or ontologies, available are
typically narrowly focused structures fit for single purpose use. In this paper
we discuss why this might be, with the conclusion that it is not sufficient to use
semantics simply to provide categorical labels for instances—because of the
interpretive and uncertain nature of geoscience, researchers need to understand
how a conclusion has been reached in order to have any confidence in adopting
it. Thus ontologies must address the epistemological questions of how (and
possibly why) something is ‘known’. We provide a longer justification for this
argument, make a case for capturing and representing these deep semantics,
provide examples in specific geoscience domains and briefly touch on a
visualisation program called Alfred that we have developed to allow
researchers to explore the different facets of ontology that can support them
applying value judgements to the interpretation of geological entities.
Keywords: geoscience, deep semantics, ontology-based information retrieval
1 Introduction
From deep drilling programs and large-scale seismic surveys to satellite imagery and
field excursions, geoscience observations have traditionally been expensive to
capture. As such, many disciplines related to the geosciences have relied heavily on
inferential methods, probability, and—most importantly—individual experience to
help construct a continuous (or, more complete) description of what lies between two
data values [1]. In recent years the technology behind environmental sensors and
other data collection methods and systems have enabled a boom of sorts in the
collection of raw, discrete and continuous geoscience data. As a consequence, the
operational paradigm of many conventional geoscience domains, once considered
data poor, now have more data than can be used efficiently, or even effectively. For
example, according to Crompton [2], Chevron Energy Technology Corporation had
over 6000 Terabytes of data, and derived products such as reports, and is rapidly
expanding. This data deluge [3], while significant in its affect on capturing
information related to complex earth science processes, has become a Pyrrhic victory
for geoscientists from a computational perspective.
The digital or electronic facilitation of science, also known as eScience [4] or
eResearch, coupled with the science of data [5] is fast becoming an indispensable
aspect of the process of Earth science [6–8]. There are exemplar projects such as
OneGelogy1, which translates (interoperates) regional geologic maps in an effort to
create a single map of the world at 1:1 million scale; as well as the Geosciences
Network2 (GEON) which houses a vast array of datasets, workflows, and tools for
shared or online data manipulation and characterisation. Further, the National
Science Foundation (in the U.S.A.) has funded EarthCube3 which seeks to meld the
perspectives of geoscientists and cyberscientists to create a framework for locating
and interoperating disparate, heterogeneous information about the entire Earth as a
comprehensive system. The major contributions that eScience can make is by
providing ways to communicate the semantics, context, capabilities and provenance
of the datasets, workflows, information and tools in order for researchers to have a
firm understanding of the artefacts they are using, and how they are using them.
In this paper, we illustrate how multiple, multi-faceted semantic models are
coordinated under the linked data paradigm to better reflect how geoscience
researchers situate concepts with their own knowledge structures in an effort to
contextualise observations, phenomena and processes. We look to expose which
semantic, or ontological, commitments are needed to glean how science artefacts
relate to researchers, methods and products (as data, or via theory) in order to transfer
what is known about a place, and how it is known, as a useful analog for geoscience
discovery. We use an interactive computational environment, known as Alfred, to
view disparate ontologies that carry pieces of this ‘knowledge soup’ [9] as facets, and
expose the relationships for discovery of new knowledge.
2 Geoscience Background
The geosciences are far from exact; the earth as a living laboratory provides plenty of
challenges, not least to the task of representing and communicating semantics. While
geoscientists are remarkable in their ability to utilise disparate knowledge in
mathematics, physics, chemistry and biology to create meaning from observed
phenomena, their theories are bound by the inherent problems associated with scale
and place, cause and process, and system response [10]. The Earth’s phenomena are
complex, they often exhibit statistically unique signatures with several stable states
while mechanical, chemical and biologic processes work in tandem, or
asynchronously. Due to these often contradictory complications it has also been
suggested that the Earth sciences exemplify a "case study to understand the nature and
limits of human reasoning, scientific or otherwise" [11]. Adding to the complexity,
“Geologists reason via all manner of maps, outcrop interpretation, stratigraphic
1
http://www.onegeology.org/
2
http://www.geongrid.org
3
http://earthcube.ning.com/
relationships, and hypothetical inferences as to causation” [12] and they do this
simultaneously across geographic and temporal scales.
In order to discern the categories and components of the Earth as a system, the
geoscientist requires a trained eye, what anthropologists call “professional vision”
[13], which often necessitates years of experience and mentoring. This contextualised
view of the world uses a long view of time, and becomes adept at distinguishing
infrequent catastrophic events from those more frequent via the feedback loops
between processes and components [13]. However, these feedback loops are often not
well understood due to the fragmented nature of geoscience observation and data.
This has required the geoscience community of practice to develop the means by
which their observations are understood. Most notably, instead of constructing a
specific research question and testing it, geoscientists often use the method of
‘multiple working hypotheses’ [14] and work toward reducing what is not known,
instead of working towards some axiomatic truth. Indeed, the ability to abstract earth
processes to a rational metaphoric justification could be considered an art form.
As such, geology is often referred to as an interpretive science [15]; where empirical
evidence is not possible, a story often emerges. Interpreting meaning in the
geosciences revolves heavily around the inherent allusion in hypothesis, methods,
models, motivations, and often more importantly, experience. Understanding the
knowledge any researcher creates requires understanding that person’s research
methods and the rationale behind their decision processes, which requires the ability
for knowledge components to change roles as one tries to demystify the scale in
context and perceptions from which they are constrained. Often, what is determined
to be a result is steeped in probability as a function of a desired resource. To date, the
research and research tools used throughout geoscience domains are largely
situational; capturing tightly coupled observations and computations which become
disjointed when the view, filter, or purpose is altered, even slightly, to that which is
more representative of an earth system science.
3 Semantic Modelling in the Geosciences
As the previous section suggests, the semantic nature of geoscientific ideas, concepts,
models, and knowledge is steeped in experiential subjectivity and often characterised
by what can or cannot be directly observed, directly or indirectly inferred, and, in
many cases, the goals of the research. As the Semantic Web [16] has gained traction
and support, a subset of Earth science researchers have been intrigued by the
possibility of standards, formal structure, and, ultimately, ontologies in geoscience
domains, mainly because, as Sinha et al., have stated, “From a scientific perspective,
making knowledge explicit and computable should sharpen scientific arguments and
reveal gaps and weaknesses in logic, as well as to serve as a computable reflection of
the state of current shared understanding” [17].
As evidenced by the dearth of semantic models, or ontologies, in the earth sciences
[18], the often-conflicting ideals and knowledge schemas are proving to be significant
hurdles for ontological engineers. Most of the semantic models in Earth science
communities would be considered weak [19], lightweight (sometimes referred to as
‘informal’) [20, 21] or implicit [22]. These include taxonomies, or controlled
vocabularies—like the American Geophysical Union’s (AGU) index of terms,4
glossaries [23], thesauri [24], or a typical data base schema. Conversely, semantic
models created with the aspiration of eventuating to strong, heavyweight or formal
ontologies are limited. In cases where published formal domain ontologies do exist
[25], they are often not openly available within the community.
One openly available ontology of note is the upper-level ontology SWEET: Semantic
Web for Earth and Environmental Terminology [26]. This formal ontology was
created to tag the huge repositories of satellite imagery created and housed by NASA.
As a result, the concepts used in SWEET are very high level and the granularity of the
ontology is, in most cases, not detailed enough to differentiate between the thousands
of resources an active research geoscientist might find useful,
There is a middle ground between the two ends of the semantic spectrum, in the form
of Mark-up Languages, which is quite promising. In the geosciences, two exemplar
Mark-up Languages do exist; the Geography Mark-up Language (GML) [27], and
Geoscience Mark-up Language (GeoSciML) [28]. The two languages were created to
serve mainly as a translation schema for data sharing and interoperability, and do
provide a level of formalisation and weakly typed relationship structure.
4 A Case for Deep Semantics
In this research we use the relative lack of formalised structures in the geosciences as
an opportunity to start from scratch and take a slightly different approach to
ontological engineering in the domain. We try get away from the highly restrictive,
monolithic, overarching structures and focus on a more complete picture of the
relational patterns of geoscientific artefacts. To summarise Gahegan et al., [29]: we
are looking to expose the ‘web of multi-faceted interactions’ between observations,
theory, data, motivations, methods, tools, places and people. To focus the modelling
effort, we asked the following questions: How is something known? Which entities
support a research artefact? Who has been publishing about a topic, concept, place,
research method or data product? What was the inference path a geoscientist traveled
from a piece of evidence to an interpretation?
As these questions might suggest, a deep semantic support structure should provide a
conceptual richness that permeates the depth of a specialised set of concepts and
provide a mechanism for defining how an artefact came to be represented. A deep
semantic structure should provide enough specificity in the concepts and relations that
4
http://www.agu.org/pubs/authors/manuscript_tools/journals/index_terms/
the terms can be used to differentiate complex but real situations via the written
materials describing these situations, not simply how it was labelled, or tagged, by the
creator, librarian or data curator. Exposing this story behind the data, or, more
formally, the epistemological connections, deep semantics works towards
constraining the conceptual uncertainty of the procedural knowledge by explicitly
representing and exposing the semantics of research artefacts as the scientist has
orientated them for his/her evidence, and thus, the resultant interpretation. This
effectively frees the research scientist to focus on the declarative knowledge
supporting the probabilities in the numerical components.
The ability to locate resources has become increasingly important as data storage
continues to increase. What carries a heavier weight is the ability to locate a data
product at the time it is most useful, by being able to distinguish a resource’s when
and where relatively rapidly. With a deep semantic view, we are able to begin
pursuing the why and how of the conceptual structures that support geoscientific
knowledge and discovery. In the information sciences this is often referred to as
precision and recall. Deep semantics adds epistemological underpinnings and a level
of context to precision and recall while adding facets to the constraint and delineation
mechanisms.
5 Ontology Inception and Use
Building the relationship structures, as described in this section, of the disparate parts
of geoscience research artefacts creates a contextualised, and in this case visual,
representation of the network of ontological components that support a concept. We
treat every ontological component as linked data supported by domain specific
terminological ontologies [30]. We use the full version of the Web Ontology
Language (OWL Full) to promote emergent constraints via relationships when
possible. OWL Full was chosen due to its compatibility with other modeling
approaches, most notably Resource Description Framework (RDF), as well as
reducing the restrictions on class definitions. The latter is necessary in the Earth
sciences as it is quite common to find a concept, or identifier, that is a class name as
well as an instance. In addition, given the nature of geoscience knowledge, it should
not be logically impossible to arrive at a conclusion that is not yet known to the
system through the ontological framework. We felt this fits more in line with the
process of Earth science, which relies quite heavily on reducing what is not known
rather than enforced, top-down, logical constraints depicting what is known
axiomatically. It is this connected interworking of heterogeneous semantic models
ranging from weak to strong, lightweight to heavyweight, and informal to formal
which join together as linked data, that we have come to refer to as deep semantics.
The remainder of this section describes how each individual ontology was
constructed.
5.1 Basin and Reservoir ontology
We endeavoured to create a framework for formal geoscience knowledge as it applies
to sedimentary basins and reservoirs in the energy and petroleum industry under the
aegis of recognised industry Subject Matter Experts (SMEs). The SMEs participated
in knowledge acquisition exercises [31] orchestrated to discuss fundamental concepts
and their meanings as interpreted and explained through their formal and experiential
mastery. As concepts emerged, they were explicitly described, often through
diagrams and examples, to the satisfaction of the other participants. Prior to each
workshop, a set of concepts had been extracted from a survey of applicable literature
in the domain to serve as exemplars for the types of concepts found in research
artefacts that differentiate and describe specific geoscience situations and models.
These concepts were periodically re-introduced to the SMEs to ensure structure that
was being created had semantically tenable end-points. This process allowed
interrelationships between fundamental and domain-level concepts to be exposed and
characterised. As the exercise progressed, clusters of concept and relationship types
became apparent. The open nature of the knowledge acquisition exercise allowed the
participants to navigate through the conceptual neighbourhood that they had created.
As such, there are areas of the concept space that are defined more rigorously than
others.
The workshops culminated two ontological frameworks: a Basin ontology [32, 33]
and a Reservoir ontology. The Basin ontology focuses on concepts corresponding to
basin characterisation. The core concepts are related to properties and other classes
through select earth processes (e.g., the Basin class is related to the Strata class via a
tectonic processes, such as subsidence). The Reservoir ontology was created quite
similarly as the Basin ontology, with the exception being that the contributing SMEs
were well versed in petroleum reservoir characterisation and modeling instead of
basin characterisation.
The Basin and Reservoir ontologies have been created to interoperate with each other
to coordinate the delineation of scale dependent ambiguities in research artefacts. To
further promote semantic interoperability, both of these ontologies have natural
contact points for semantic correlation with upper-level earth science ontologies in
the public-domain, such as SWEET, as well as with other domain-specific ontologies
from hydrocarbon exploration and production, to hydrologic and paleoclimate
modeling, should they become available.
5.2 Agent and Resource ontology
Two of the more important facets of this research are the actual research artefacts and
the researchers, or creators, of those artefacts. Fortunately, librarians have already
spent a significant amount of time developing a standard for metadata that captures
the types of information that we wanted to capture from resources. We used the
Agent profile from the Dublin Core Metadata Initiative5 (DCMI) to describe authors,
contributors, software, companies, and research groups. There are other types of
agents, of course, and DCMI is set up to handle these distinctions, but for our
5
http://dublincore.org/
purposes a subset was all that was required. The Resource profile from DCMI is used
to describe any artefact produced by research. This can include publications,
abstracts, presentations, and data products. Again, the schema for the DCMI
framework allows for a plethora of types, but a subset was all that was required here.
5.3 Task ontology
The Task ontology was constructed to provide a framework for actions that are
completed during research. These include observations, methods and processes like
data collection, data manipulation, statistical methods, etc. Items in the Task
ontology link to Resources as outputs and inputs, and to Agents as creators,
contributors, reviewers, etc. The concepts in this specification are often chained
together to create large structures, and are helpful in delimiting clusters of
information. The Task ontology was created by, first, describing a small set of
exemplar concepts that related strongly to key components of the semantic models
described in the previous two sections. Once the initial concepts were introduced, the
structure was extended by defining known superclasses and subclasses, and then
supplanting those core concepts with text mining utilising basic natural language
processing principles.
5.4 Oilfield Glossary and World Oil and Gas Atlas ontology
The Schlumberger Oilfield Glossary6 is a fantastic on-line resource covering an
expansive number of topics. The Oilfield Glossary ontology was constructed from
harvesting the information hosted on this web site. Due to research limitations, it was
more beneficial to create a local copy of this data and convert that to a series of triples
than to develop a script to query the site interactively. During the data conversion, all
partial relationships within the structure, and the links to the corresponding web page
were preserved.
The World Oil and Gas ontology was created by manually entering information
provided in the summaries, graphs, and tabular data, as depicted in the World Oil and
Gas Atlas [34], into an electronic format. Once in an electronic format, a script was
generated to alter the format, along with a little manual editing, to OWL.
6 Early Results: Powder River Basin Use Case
This use case illustrates how research artefacts associated with the Powder River
Basin, located in the central part of the U.S.A., can be visualised and navigated via a
knowledge computation platform referred to here as Alfred. This platform allows for
navigating and manipulating disparate multifaceted structures in one graph space.
The user loads an ontology into the system, which is then represented as a facet. Any
facet can be docked to any length of the graph border. Through docking a facet (up to
four), Alfred provides a space for the user to follow their interests in their linked data
exploration.
6
http://www.glossary.oilfield.slb.com/
We proceed from the perspective of a research scientist with an interest in the Powder
River Basin. The user enters “Powder River” into Alfred’s the search field. The
resulting graph, shown in Figure 1, shows two concepts matching the search term
within a neighbourhood of related concepts. One of the Powder River concepts
(highlighted with a yellow outer ring) is symbolised using a gold circle coupled with a
black ‘basin’ object from a scalable vector graphic (SVG). The edge pointing to the
yellow circle labeled Basin, denotes it is an instance of the Basin class (yellow disc)
from the Basin ontology. The other symbol labeled Powder River is a grey triangle
which signifies it as a member of the World Oil and Gas ontology (in the structure,
these two concepts are in fact connected via an owl:sameAs relation, but this type of
relation has been suppressed in the current view for readability). Three conference
abstracts, symbolised by a red circle, with an SVG in the shape of a book, relate via a
references edge to the Powder River concepts, as well as a few concepts symbolised
by black diamonds, which are delineating concepts from the Oilfield Glossary
ontology.
Fig. 1. Local graph neighbourhood of the concept representing the Powder River Basin. The
current view shows three published artefacts, how the concept Powder River is linked in the
hierarchy (it is an instance of Basin) as well as a few terms from the Oilfield Glossary.
At this stage, the user has a few options. The user can select something from the
graph, adjust the filter settings to increase the type and/or level of information
displayed in the graph, or start a new search. To continue with the example in the use
case, we assume the users interest has been piqued by one of the research artefacts
sharing a relationship with the Powder River Basin concept. If the user were
interested in seismic reflection data, they might select the artefact purporting to deal
with complex seismic reflection attributes (red disc with book SVG, lower left) via a
double click.
Upon this click action, the graph re-centres itself using the user selected node as the
central concept, as depicted in Figure 2. This selection reveals a deeper structure
associated with that particular artefact. In this view, the creator of the artefact,
symbolised by a dark green circle surrounding a SVG of a person, has emerged along
with several concepts found in the Oilfield Glossary. The relationship to the Powder
River and Basin concepts have persisted to the new concept layout (lower middle of
Fig. 2). Several concepts from the Task ontology (blue circles) have emerged,
potentially signifying relationships to the data (seismic data) as well as concepts
related to analysis mechanisms (phase coherence) associated with this particular
research artefact. This view also provides the user with contextually similar research
artefacts by displaying the research outputs that share a relation with other concepts in
the known ontologies.
Fig. 2. Local graph neighborhood of a research artefact related to the Powder River Basin.
The current view shows the artefacts creator, as well as other concepts from the semantic
structures known to the system.
In the example depicted in Figure 2, three research artefacts appear to share several
relationships (mostly a reference edge) with concepts found in the Oilfield Glossary,
as well as the Tasks ontology. This clustering suggests there are other research
products that have utilised the same, or similar, methods and data that were used in
the research artefact of interest. This is worth mentioning here as the related data,
tasks, and concepts allow the user to explore and glean the concepts and structures
that support a research artefact. The ability to navigate, what has become, the
epistemological lineage of a research artefact cultivates a formal representation of the
symbiotic components of geoscience research products and geoscience knowledge.
Fig. 3. A local graph neighbourhood shown with a web page that is marked up with red text
using the concepts from the ontologies loaded into the system. The user can go back and forth
between graph space and the web page in order to better refine the context and scope of
research artefacts and web enabled content.
At this final stage of the use case, we illustrate how the conceptual neighbourhood of
the graph can synchronise with other web enable components, in this case browser
content. As portrayed in Figure 3, a user can open a web page and search for known
semantic components on that page. When the search has completed, all known
concepts are now displayed with red text within the browser. Further, by hovering
over any syntactic component on the corresponding web page (in this instance, it is
the mobile version of the Wikipedia page for the Powder River Basin) the user is
presented with a pop-up dialogue populated by the referring ontology. If a term is
situated in multiple ontologies, each one will be listed in the pop up, with the text
providing a live link back to the graph space. In this way, a user could bring their
conceptual neighbourhood with them as they peruse web content and use the
highlighted text references to help filter for relevance. This has proved to be
particularly helpful with the increasing number of peer reviewed publications
available in a web friendly format.
The Powder River Basin use case illustrates how deep semantics can benefit
geoscientists by providing a mechanism to visualise and explore the components that
comprise a knowledge construct. When a geologist purports to know how to
characterise a particular basin, other geologists and engineers naturally want to know
what data and analysis methods were used to support that interpretation. How was
the stratigraphy interpreted? What was the timing of the tectonic events? What is the
burial history? Deep semantics allows other geoscientists to explore these supporting
entities and the decisions that were made along the path to any particular explication.
7 Concluding Remarks
Geoscience ontologies are typically quite lightweight, or implicit, and are engineered
for one specific purpose. As such, the semantic structures in the geosciences fail to
capture the complexities and intricacies inherent in the domain knowledge.
Ontologies like SWEET are a great start at a general upper level structure for
geoscience domains, but other than providing a label for instances, these structures
are far removed from capturing the level of detail necessary to empower domain
scientists, or knowledge engineers, with useful components for day-to-day
meaningful research activities. In this paper we illustrate how a deep semantic
structure serves to differentiate research products by capturing epistemological
commitments of geoscience research artefacts using ontologies throughout the
spectrum of formalisation. This deep semantic structure provides the conceptual
backbone for geoscientific search, discovery and enquiry.
Acknowledgments. The authors would like to thank Dr. Will Smart and Sina
Masoud-Ansari, at the University of Auckland’s Centre for eResearch, for their
contributions to the Alfred framework used to illustrate the use case in this paper.
The authors would also like to acknowledge the generous support of the New Zealand
International Doctoral Research Scholarship (NZIDRS), which helped make this
research possible.
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