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PraSem
A Pragmatic Semantics
for the Web of Data
Stefan Schlobach
Wouter Beek
(www.wouterbeek.com)
Problem statement
• The Web of Data (WoD) is complex, inherently messy, contextualised,
and opinionated.
• Today the WoD i...
Illustrative example
Existing solutions for semantics
• Context-dependence (contexts)
• Complexity (small dataset + rich semantics, big dataset...
Alternative solution: Pragmatic Semantics
Theory:
• A collection of truth orderings, each representing a particular ‘world...
Examples of truth orderings
• Model-theoretic notions of truth
• (Classical) truth value
• Ratio of maximally consistent s...
Example
At the VU university:
• Computer Scientists talk about ‘ontologies’
• Philosophers talk about ‘ontology’
Suppose s...
Pragmatic entailment
Swarm intelligence
Implementations
Ant calculus:
• Identify popular resources by
random-walks, simulating
PageRank.
Bee calculus:
• Dataset e...
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Pragmatic Semantics for the Web of Data

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Presented at the AAAI Fall Symposium for Big Data on 2013-11-15.

Published in: Education, Technology
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Transcript of "Pragmatic Semantics for the Web of Data"

  1. 1. PraSem A Pragmatic Semantics for the Web of Data Stefan Schlobach Wouter Beek (www.wouterbeek.com)
  2. 2. Problem statement • The Web of Data (WoD) is complex, inherently messy, contextualised, and opinionated. • Today the WoD is constructed and used as a database. • Tomorrow the WoD should be constructed and used as a marketplace of ideas / a ‘knowledge economy’.
  3. 3. Illustrative example
  4. 4. Existing solutions for semantics • Context-dependence (contexts) • Complexity (small dataset + rich semantics, big datasets + less rich semantics) • Dynamicity (irregular snapshots) • In- or para-consistency (maximally consistent subset, reasoning light) • Objectivity (contexts, provenance) • Vagueness (Fuzzy logic)
  5. 5. Alternative solution: Pragmatic Semantics Theory: • A collection of truth orderings, each representing a particular ‘worldview’. • A framework for optimisation over those truth-orderings. Implementation: • Distributed and nature-based algorithms.
  6. 6. Examples of truth orderings • Model-theoretic notions of truth • (Classical) truth value • Ratio of maximally consistent subsets • Number of justifications • Structural aspects of the graph • Shortest path ordering (e.g. using random-walk distance) • Edge-weights • Node-ranks (e.g. PageRank) • Meta-data: • Popularity / abnormality / scarcity • Background knowledge from other sources: • Google count • Similarity / relevance
  7. 7. Example At the VU university: • Computer Scientists talk about ‘ontologies’ • Philosophers talk about ‘ontology’ Suppose someone (foolishly?) asserted that a CS ontology is a Phil. ontology… • The deductive closure may contain falsities (e.g. “there is exactly one CS ontology”). But Computer Scientists are more connected with other Computer Science researchers than with Philosophers. When deduction is constrained by a structural metric, false assertions are less likely to arise.
  8. 8. Pragmatic entailment
  9. 9. Swarm intelligence
  10. 10. Implementations Ant calculus: • Identify popular resources by random-walks, simulating PageRank. Bee calculus: • Dataset enrichment
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