In the geosciences, the 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.

1. deep semantics in the geosciences:
semantic building blocks for a
complete geoscience infrastructure
Brandon Whitehead and Mark Gahegan
Centre for eResearch
The University of Auckland
8th Australasian Ontology Workshop
20120412

2. outline
1. geoscience background
2. what is deep semantics
3. ontologies created and the creation process
4. two examples of use
5. future work 2

4. representing observations as data

5. 5

6. 6

7. d
7

9. 9
P E/SH
Ruling
hypothesis
Mul0ple
sequen0al
hypotheses
Mul0ple
parallel
hypotheses
Composite
hypotheses
P
P
H1 R
H2
H3
H4
R
R
E/S
H1
H2
H3
H4
R
R
R
E/S
P
H2
H3
H4
E/S
H1 R
H2
+
H3
+
H4
paths
to
explana7on
(Schumm,
1991)

10. a case for deep semantics
In the geosciences, it is not only important to
capture and formalise what is known, but how it is
known.
deep- as in deeply examining the conceptual
structure and complexities of the domain in order
to provide enough specificity in the concepts and
relations that they are useful terms to differentiate
complex but real situations (as they are found in
research artefacts).
10

11. workshop | Subject Matter Expert (SME)

12. An initial set of terms collected from source materials were used to
spark discussion throughout the knowledge acquisition exercise

13. Experts work together to disambiguate conceptual semantics

14. Basin Ontology (v1)

16. possible states of a basin

17. Concepts and relationships are codified using the Web Ontology Language (OWL) which
allows for a semantic relationship to other concept spaces and description frameworks used in
the domain

18. other semantic facets
1. Dublin Core Metadata Initiative (DCMI) schema
18

21. other semantic facets
1. Dublin Core Metadata Initiative (DCMI) schema
2. task ontology
-includes observations, methods and processes like
data collection, data manipulation, statistical methods, etc.
3. World Oil and Gas ontology
-from World Oil and Gas Atlas (Guoyo, 2010)
4. Oilfield glossary
-Schlumberger online glossary of terms
21

22. We now have a codified, formal representation of
concepts relevant facets related to basin
characterisation and reservoir analysis.
…now what do we do with it?

23. The ontology is used to identify semantic connections in
research artefacts (i.e., document collections)

24. Conceptual neighbourhood of an artefact
ConceptVista, Gahegan et al.

25. Geographical neighbourhood of an artefact, or set of
artefacts
Looking for a location
mentioned in a document?
And the resources near it?

26. The geographic extent of an artefact coordinated with the
highlighting of recognised concepts

27. two quick examples
-Alfred as a visual environment
-completely transparent and customisable
27

28. Adam Jones

29. Adam Jones

30. Danny Carey

31. 31

32. 32

33. 33

34. Future work
• Can geologic analogs be mined from artefact
collections?
• How can large collections be tagged effectively?
• What concepts are needed to extend the
spectrum from very high level ontological
components (DOLCE) and concepts to those
found in published materials?
• Interface (complexity, fluidity, etc.)

35. Fín