A Probabilistic
Framework for
Information Integration and
Retrieval on the Semantic Web
by
Livia
Predoiu
,
Heiner
Stuckenschmidt
Institute of Computer Science,
University of Mannheim, Germany
presented by
Thomas Packer
Sources of Uncertainty in Automated
Processes in the Semantic Web
•
Uncertain Document Classification
•
Uncertain Ontology Learning from Text
•
Uncertain Ontology Matching
•
Leads to uncertain, unreliable or contradictory
information.
•
Traditional logic cannot handle inconsistency.
Motivational Example
•
Domain
: Bibliography
•
Use Case
: Find publications with keyword
“AI”.
•
Complication
: Second ontology does not
include the concept of “topic” or “keywords”.
•
Solution
: Use machine learning to categorize
documents from the second collection.
Motivational Example (Continued)
•
Domain
: Bibliography
•
Use Case
: Find publications with keyword
“AI”.
•
Complication
: “Report” concept in one
ontology kind of corresponds to “Publication”
in the other.
•
Solution
: Map concepts between
ontologies
.
Approach
•
Start with a more standard approach,
Description
Logic Programs
.
•
Extend them with probabilistic information.
•
Call the result
Bayesian Description Logic
Programs
(BDLPs).
•
It is a subset of
Bayesian Logic Programs
.
•
It also integrates logic programming and
description logics knowledge bases.
BDLP Pedigree
Description Logic
Programs (DLPs)
Bayesian
Description Logic
Programs (BDLPs)
Bayesian Logic
Programs (BLPs)
Description Logic
(DL)
Logic Programs
(LPs)
Bayesian
Networks (BNs)
Uses of
Bayesian Description Logic Programs
•
Framework for
–
information retrieval
–
information integration
–
across heterogeneous
ontologies
.
Description Logic Programs
(Background)
•
Intersection of:
–
Description Logics (knowledge representation)
–
Logic Programming (automated theorem proving)
•
DLP program contains:
–
Set of rules
–
Set of facts
•
Rules have the form:
–
Conjunction of predicates implies some other predicate.
–
H and B’s are atomic formulae.
–
Predicate argument are called terms.
–
Terms are constants or variables.
–
A ground atom’s terms are all constants.
Description Logic Programs
(Background)
Description Logic Programs
(Background)
•
Restricted expressivity
•
Many existing DL
ontologies
fit DLP
restrictions.
•
Reasoning in DLP is decidable.
•
Reasoning has much lower complexity than DL
reasoning in general (in theory and in
practice).
Bayesian Description Logic Programs
•
BDLP program contains:
–
Set of rules
–
Set of facts
•
Rules have the form:
–
Conjunction of predicates implies some other
predicate.
–
“” instead of “
” to imply conditional probability.
–
Each rule has a probability distribution specifying the
probability of each state of the head atom given the
states of the body atoms.
–
Each ground atom corresponds to a BN node.
Example BDLP
Example Bayesian Network
•
Blue
Ontology 2
•
Cyan
Learned from Ontology 2
•
Black & White
Ontology 1
•
Red arcs
Mappings
Where do Probabilities Come From?
•
Deterministic
ontologies
–
true = 1.0
–
false = 0.0
•
Probabilistic tools
–
Naïve
Bayes
document categorization
–
Probabilistic ontology mapping
•
Subjectively.
–
People argue that people are inconsistent in their
judgment of probabilities.
–
Using subjective probabilities is still more accurate
than forcing people to use Boolean judgments.
Example Query
•
Query for publications about AI.
•
Non

ground query.
•
Two valid groundings.
•
Query BN for probabilities (IR with ranking).
Conclusion
•
Strengths:
–
Actually explains how Bayesian Networks relate to predicates.
–
Handles integration (which others do not).
–
Handles IR.
•
Weaknesses
–
DLPs don’t allow for negation or equivalence.
–
No measured evaluation.
–
Size of model and therefore BN can be exponential in size of KB.
–
Intractable exact inference in BN’s with cycles.
•
Future work
–
Learn BLP programs from data.
–
Prune BN to portion relevant to query.
–
Approximate probabilistic inference.
–
Parallel/distributed programming.
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