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Modeling vs Encoding for the Semantic Web
Werner Kuhn University of Münster
Institute for Geoinformatics Münster Semantic Interoperability Lab (MUSIL)
Kuhn, W. (2010). Modeling vs encoding for the Semantic Web. Semantic Web - Interoperability, Usability, Applicability, 1(1), 11–15
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Integrating spatial information across vocabularies
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Claims in the semantic web
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Stated
“... ontologies are expected to be used to provide structured vocabularies that explicate the relationships between different terms, allowing intelligent agents (and humans) to interpret their meaning flexibly yet unambiguously...” [Horrocks et al., JWS, 2003]
”Technically, Linked Data refers to data published on the Web in such a way that it is machine-readable, its meaning is explicitly defined, ...” [Bizer et al., IJSWIS, 2009]
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Implicit
• description logic statements are necessary and sufficient to capture what people mean when they use vocabularies
• ontology engineers can say something useful about the semantics of vocabularies by expressing themselves in an encoding language for machine reasoning
• decidability matters in designing semantic models.
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How we are “living up” to these claims
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the
sem
antic
web
bot
tlene
ckHow we are “living up” to these claims
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the
sem
antic
web
bot
tlene
ckHow we are “living up” to these claims
meaningful communication
concepts
words
predicates
sortals
sets
⊓⊔⊑
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the
sem
antic
web
bot
tlene
ck
thro
win
g se
man
tics
out
of t
he w
indo
w
How we are “living up” to these claims
meaningful communication
concepts
words
predicates
sortals
sets
⊓⊔⊑
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the
sem
antic
web
bot
tlene
ck
thro
win
g se
man
tics
out
of t
he w
indo
w
How we are “living up” to these claims
context
pragmatics
structure
processes
prototypes
similarity
meaningful communication
concepts
words
predicates
sortals
sets
⊓⊔⊑
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Acknowledgments to Ross Purves
schema.org
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What I mean by “meaning”
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conceptualize
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conceptualize
express
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refer to
conceptualize
express
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The Ogden and Richards (1923) semiotic triangle
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Meaning as Process
• meaning is a process, not an object [Putnam 1975]
• “words don’t mean, people do”
• information results from referring to things through symbols
• information users interpret such references
• information providers can constrain such interpretationsmeadow := extensively used grassland [CORINE land cover class 231]
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How can the semantic web
constrain interpretations ?
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The Standard ApproachIt is useful to ...
• ... equate terms with classes (which are sets)class meadow
• ... assign properties (sets, again) to classesmeadow hasUse extensive
• ... declare sub-class (sub-set) relationships meadow is-a grassland
The arguments supporting this approach are based on formalization (rather than modeling) requirements.
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But ...It may be harmful to ...
• ... equate terms and classes, because terms are used in some contexts (but not in others)”is this a meadow or a field?”
• ... assign properties to classes, because “some do, some don’t”myMeadow instanceOf meadowsmyMeadow hasUse intensive
• ... declare sub-class (sub-set) relationships, because compositionality rarely holdsgrassland hasUse extensive
≠ extensive ⊓ grassland
Resulting discussions about “correct terminology” are a waste of time.
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Alternative ApproachIt is useful to ...
• ... document actual uses of vocabularies as triplestriples showing who calls what a “meadow”
• ... treat these as inconsequential type declarationssame piece of land could be typed “field”
• ... define type classes for shared behavior as ontology patternsclasses ARABLE, SELLABLE, BUILDABLE, ...
• ... inherit behavior to types playing rolestype meadow instantiates ARABLE, SELLABLE
• ... reason with type classesfield sameAs meadow, if same behavior
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A Modeling Language: Haskell
class (LINK link from to, SUPPORT from for, SUPPORT to for, CONTAINMENT medium link) => PATH for link from to medium where
move :: for -> link -> from -> to -> medium -> for
instance PATH Car Link Node Node Air
The standard modern functional language• clean, higher order type system• executable algebraic specifications• multi-parameter type classes
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What kinds ofontology patterns
are useful?
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Example: PATH
One source: Image Schemata
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Example: PATH
One source: Image Schemata
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Example: PATH
One source: Image Schemata
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Example: PATH
One source: Image Schemata
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• structured, invariant, compositional, physically grounded ...
• (re)capture some context, pragmatics, processes, prototypes
• more examples: link, containment, support, center-periphery, part-whole, ...
Example: PATH
One source: Image Schemata
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OWL:Classpath
OWL:Class:medium
OWL:Class:surface
OWL:Class:name
has part
part of
has
OWL:Class:endObjectOWL:Class:
startObject
OWL:Class:description
inon
has
has
has
Cardinality of all relations [0..m]
http://vocamp.org/wiki/GeoVoCampSB2012
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OWL:Class motion
OWL:Classpath
OWL:ClassstartEvent
OWL:Class movingObject
OWL:ClassmovedObject
OWL:ClassendEvent
OWL:Classname
OWL:Classdescription
OWL:ClassreferenceFrame
has part
part of
has
has
hashas
hashas
has
has
Cardinality of all relations [0..m]
http://vocamp.org/wiki/GeoVoCampSB2012
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Conclusions
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Conclusions
1. The semantic web emphasizes encoding for automated reasoning over modeling meaning.
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Conclusions
1. The semantic web emphasizes encoding for automated reasoning over modeling meaning.
2. As a consequence, there is an over-emphasis on set-based types, at the expense of higher level structure.
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Conclusions
1. The semantic web emphasizes encoding for automated reasoning over modeling meaning.
2. As a consequence, there is an over-emphasis on set-based types, at the expense of higher level structure.
3. I propose to constrain interpretations through second order type classes (as ontology patterns).
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Conclusions
1. The semantic web emphasizes encoding for automated reasoning over modeling meaning.
2. As a consequence, there is an over-emphasis on set-based types, at the expense of higher level structure.
3. I propose to constrain interpretations through second order type classes (as ontology patterns).
4. These provide small theories, easily combinable, for big data.
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Conclusions
1. The semantic web emphasizes encoding for automated reasoning over modeling meaning.
2. As a consequence, there is an over-emphasis on set-based types, at the expense of higher level structure.
3. I propose to constrain interpretations through second order type classes (as ontology patterns).
4. These provide small theories, easily combinable, for big data.
5. Specifications have started at the GeoVoCamp series (Santa Barbara, Dayton, ...): http://vocamp.org/wiki/GeoVoCampDayton2012.
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But, will this solve the fax-number-of-mountains
problem?
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But, will this solve the fax-number-of-mountains
problem?
Yes! mountains areendObjects of PATHs for hiking (not faxing)
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But, will this solve the fax-number-of-mountains
problem?
Yes! mountains areendObjects of PATHs for hiking (not faxing)
Thank You!Thursday, September 6, 12