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

1. PraSem
A Pragmatic Semantics
for the Web of Data
Stefan Schlobach
Wouter Beek
(www.wouterbeek.com)

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. Illustrative example

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. 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. 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. 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. Pragmatic entailment

9. Swarm intelligence

10. Implementations
Ant calculus:
• Identify popular resources by
random-walks, simulating
PageRank.
Bee calculus:
• Dataset enrichment