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Geoscientific knowledge: The limits of ontology and other ways of knowing

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Steve Weaver. San Diego Supercomputer Center (GEON) Kai Lin. Chaitan Baru ... 'You can know the name of a bird in all the languages of the world, but when ... – PowerPoint PPT presentation

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Title: Geoscientific knowledge: The limits of ontology and other ways of knowing


1
Geoscientific knowledge The limits of ontology
and other ways of knowing
  • Mark Gahegan
  • GeoVISTA Center, Department of Geography
  • The Pennsylvania State University, USA

2
Credits
  • GeoVISTA Center, Penn State (GEON, HERO,
    Dialog-Plus)
  • Junyan Luo,
  • Bill Pike,
  • Tawan Banchuen
  • Anuj AJ
  • Steve Weaver
  • San Diego Supercomputer Center (GEON)
  • Kai Lin
  • Chaitan Baru

3
CyberInfrastructure The GEON GRID
4
Some problems with current approaches
  • Human knowledge that creates meaning out of
    analyses is often unrecorded
  • for lack of a model of the scientific process
    that can capture knowledge as it is created and
    used.
  • We argue for an approach to representing
    scientific concepts that reflects
  • the situated processes of science work,
  • the social construction of knowledge, and
  • the emergence and evolution of understanding over
    time.
  • In this model, knowledge is the result of
    investigation, negotiation, and collaboration by
    teams of researchers.

5
Representing living knowledge
  • Knowledge keeps no better than fish
  • -- Alfred North Whitehead
  • You cannot put your foot in the same stream
    twice
  • -- Heraclitus
  • You can know the name of a bird in all the
    languages of the world, but when you're finished,
    you'll know absolutely nothing whatever about the
    bird... So let's look at the bird and see what
    it's doing -- that's what counts.
  • -- Richard Feynman

6
Where does meaning come from?
  • Domain understanding / theory (ontology)
  • The way things are done (epistemology)
  • How are resources created and used (work
    practices / situations)?
  • Negotiation among the community of users (social
    network, group cognition)
  • We know things in many ways
  • Theoretical, Experiential, Procedural
  • i.e. the interplay of top-down and bottom-up
    knowledge played out in private and social
    situations

7
Knowledge Goals of Cyber-Infrastructure
  • Help communities of researchers and educators to
    do better science by sharing their resources
    computing power, data, tools, models, protocols,
    results
  • BUTMaking resources available is not the same as
    making them useful to others
  • Can we also share meaning?
  • Litmus tests
  • Can we remember what we did?
  • Will future generations of scientists be able to
    follow our work?

8
Knowledge soup Sowa, 2002
Little round planet in a big universe, Sometimes
it looks blessed, sometimes it looks cursed. It
depends what you look at obviously But even
more, it depends on the way that you see. (Bruce
Cockburn Child of the Wind, 1994)
9
Whats in the soup? A nexus of knowledge
structures (Whitehead, 1923)
10
Why ontologies? (Noy and McGuinness)
  • To share common understanding of the structure of
    information among people or software agents
  • To enable reuse of domain knowledge
  • To make domain assumptions explicit
  • To automatically integrate disparate databases

11
GEON Multiple, different geological ontologies
Genesis
Fabric
Kai Lin, SDSC Boyan Brodaric, GSC
Texture
12
Geologic Map Integration in the Portal
  • After registering datasets, and their ontologies,
    mappings can be constructed between the datasets
    via the ontologiessemantic mediation

Kai Lin, SDSC
13
Rock Taxonomy(ontologically based)
Geological taxonomy converted to an
ontology Gathered from experts during a specially
convened workshop Formalizes relationships
between concepts
Randy Keller (UTEP), Bertram Ludaescher, Kai
Lin, Dogan Seber (SDSC), et al
14
An alternative rock taxonomy!
Rock music taxonomy converted to a concept
map Gathered automatically from consumer
purchasing logs Assumes relationships between
concepts
15
A continuum of knowledge
  • We know things in many ways
  • Theoretical, Experiential, Procedural
  • Top-down, structured knowledge (concept maps,
    ontologies)
  • Formal knowledge structures (taxonomies,
    hierarchies, rules)
  • Bottom-up, informal knowledge (social networks,
    use-cases)
  • Situates e-resources and knowledge in Whiteheads
    nexus

16
Why Not Ontologies!
  • Top down knowledge (ontology) only gets you so
    far
  • Experiences, use-cases (situations surrounding
    the use of resources), Social networks.
  • What happens to all the millions of geo-
    resources that predate ontologies?
  • The cost of retro-fitting ontologies can be
    prohibitive.
  • Creating useful domain ontologies is very
    expensive and problematic
  • Can they be encouraged to emerge?
  • Most current ontologies are static resources
  • Our understanding is dynamic continually
    evolving
  • C.S. Peirce

17
Learning from situations of use
  • Who created that resource?
  • When was it created?
  • How often has it been used?
  • Has it been modified recently?
  • Who has used it?
  • What has it been used with?
  • Such questions add a rich context by capturing
    situations surrounding resource usage

18
Remembering situations of use
19
Situations
Creation Application Represented by
Who did it? Who should use it? Collections of people
Where was it made? Where does it apply? Collections of sites / scales
When was it made? When does it apply? Collections of temporal intervals
How was it made? How should it be used? Collections of methods and data
Why was it made? Why should it be used? Collections of research questions, motivations, theories
20
Whats in the soup? A nexus of knowledge
structures (Whitehead, 1923)
21
Situating e-resources in the knowledge nexus
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Perspectives as filters
Perspectives filter an information space
according to particular situations. Perspectives
A and B preferentially select different types of
resources and relations the ability to view
perspectives can show how someone else made sense
of a given set of resources.
25
Four perspectives on a seismic velocity concept
(red node). a) Intensional concept structure.
b) A task that describes how seismic velocity can
be measured. c) A social network built around
users of the concept. d) Data resources that
have been used to describe seismic velocity.
26
Concept use and evolution
Evolution of Depositional environment concept
through use by different researchers over time,
progressing from upper left to lower right.
27
What is wrong with this approach?
  • Does not represent the importance of ontology as
    a formal, fixed, sharable, community resource
  • Can we still have ontology, but with
    perspectives?
  • Knowledge horizons an idea from Hermeneutics
  • Creating flexible horizons
  • Relations become properties (internalized),
    properties become relations (externalized)
  • Perspectives can be applied locally or globally

28
ConceptVista What to represent?
  • Styling
  • Perspectives
  • Situations
  • Connections to web resources
  • Basic types
  • Geon Themes
  • Resources
  • Methods
  • Personnel
  • Institutions
  • Articles

29
Perspectives for GEON
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32
Navigating through conceptual universes
33
Combining perspectives e.g. GEON institutions,
publications and personnel
34
Navigation strategies
Styling independently serializable (OGCs SLD)
Expand/collapse remove or expand detail
Locality limit the depth of expansion
Perspectives visualized using SLD
Query linking to other resources Using a variety of nym options
35
Summary
  • Rich, Living Knowledge
  • Knowledge keeps no better than fish
  • -- Alfred North Whitehead
  • You cannot put your foot in the same stream
    twice
  • -- Heraclitus
  • So let's look at the bird and see what it's
    doing -- that's what counts. -- Richard
    Feynman
  • Perspectives allow scientists to describe what
    they know onto shared ontological resources.
  • Irony of Ontology is that ontologically-based
    languages can be used to represent its
    obverseEpistemology.

36
Current work integrating data analysis and
concepts in a single system
37
End
  • Questions?

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