A semantic data model for mineral and energy resource exploration
Ontology: An ontology is the definition and
classification of concepts and entities and the relationships
between them. Semantic data: Semantic data is data that is
organised so it can be understood by machines. Exploration:The process by which geological
information is collected and analysed to identify mineral or energy
resources as well as determining the economic feasibility of their
An entity or location within, or encompassing, a feature that acts as a proxy to represent a
complete (ultimate) feature.
A Feature of Interest is proximate when it represents a larger feature, as opposed to being a
discrete component of a larger feature. e.g. an outcrop can be examined as a representative of a
formation, whereas a formation does not represent a whole basin but is a component of it.
Where a sampling is undertaken, but the sampling geometry and site geometry do not necessarily
have to be equivalent.
The sample is a representative part of a feature of interest.
Samples may be original samples, subsamples where a new sample
is split into smaller samples, processed samples where a sample content is
retained but is processed to have altered properties, or duplicates - identical
A sample may be surveyed to produce a new sample or sub-sample.
Examples: drill core, drill cuttings, soil sample, hand specimens, water, photograph, LAS
An act of carrying out an observation using a procedure to measure, estimate, calculate
a value of, or describe a feature, site or sample.
Observations differ from sampling in that sampling yields an artefact, whereas an observation
yields a qualitative or quantitative result.
Observations may be the observation of the physical limits of an interval.
The result of the observation performed on a sample, stored as a description or as a value and
unit of measure.
Physical properties, e.g. concentration, mass, temperature
Geophysical measurements e.g. gravity, magnetic field strength
Petrophysical log measurements e.g. gamma, density, resistivity.
What are the business advantages of an ontology?
Ontologies, along with taxonomies and vocabularies, provide these business advantages:
The ontology provides the business with a shared understanding of business concepts, entities,
People across business divisions, as well as customers and suppliers, can use the ontology as the basis for
seamless information exchange.
Ontologies and their vocabularies allow multi-language taxonomies, and synonyms, regional, historical and colloquial
terms for concepts. This enables semantic master data management.
Teaching the ontology to new recruits during induction will help them to understand the
How does a computer understand the ontology?
The ontology is made available to the computer in the Web Ontology Language (OWL) as an RDF (Resource
Description Framework) file.
The computer can understand this semantic description of the data entities, their attributes, and
We use RDF-based controlled vocabularies to feed the computer the taxonomy - a list of words related
to each other. The computer uses SPARQL query language to query the vocabulary API to understand the
words and their meaning.
A borehole example of computer reasoning
This example demonstrates the use of semantic data techniques that enable the computer to
reason (understand) without needing a human to tell it what to do.
We feed the computer data for borehole CARINYA SOUTH 3 using the Persistent
Identifier (PID) BH063772.
The computer can reason that a borehole is a type of geological
site by querying the Geological Properties Ontology.
The computer can reason that a Borehole has alternative labels of Core Hole,
Corehole, Drillhole, and Well by querying the borehole concept in the geological sites vocabulary.
The computer can reason from the borehole purpose that it will process this borehole as a
Coal Seam Gas Well.
Sure, we could have told the computer straight up that this was a Coal Seam Gas Well.
However, hopefully you can see the power of enabling the computer to reason across Features, Sites,
Surveys, Samples, Observations, and Results, and all of their attributes, relationships, ontologies
What about the existing data models for exploration and appraisal data?
Standards such as
(Geoscience Markup Language) for minerals and
for petroleum and gas are detailed data models that fit within the
ontology model and inform it.
Many of the geoscience data models exist as relational data models,
and lack the semantic information required by computers for activities such as machine learning.
This ontology is not about replacing, but instead complements, extends and integrates these data
The ontology enables integration of data across these different models. For example, if we integrate GeoSciML
mineral data with PPDM petroleum and gas data, the computer will know that a borehole and a well are
Where does my database fit in?
This ontology can be represented in a relational database. However, if you're starting afresh, you
should consider databases that support key-value data or document (JSON-based) data structures.
Graph databases are perfect for OWL and RDF data.
Here are some things you can do to make your existing relational database more semantic:
Map your existing reference data tables to ontologies and vocabularies.
Create and store persistent identifiers for both data and metadata.
Create semantic data views so machine consumers can query these views.
We hope you enjoyed this introduction to the Geological Properties Ontology. For further information please contact us at email@example.com.
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