OMEN: A Probabilistic Ontology Mapping Tool

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OMEN A Probabilistic Ontology Mapping Tool

• Mitra et al.

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The Problem

• We need to map databases or ontologies

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The Problem

• Mapping is difficult
• Most mapping tools are imprecise
• Even experts could be uncertain
• We deal with probabilistic mappings

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The Solution

• Infer mappings based on previous ones
• We use Bayesian Nets for inference
• We use other tools for initial distributions
• Preliminary results are encouraging

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Basic Concepts

• Bayesian network
• Probabilistic graphical model that represents
  Random variables

• Evidence nodes The value is given

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Bayesian Network

• Conditional Probability tables (CPT)

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Bayesian Nets in our approach

• How do we build the Bayesian Net
• Nodes are property or class matches
• Classes are concepts
• Properties are attributes of classes

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Building Bayesian Nets
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Our Bayesian Nets

• All combinations of nodes is too many
• We generate only useful nodes
• The cutoff is k from evidence nodes
• Up to 10 parents per node
• Cycles are avoided (confidence .5)

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Our Bayesian Nets

• We need evidence nodes and CPTs
• Evidence nodes come from initialization
• CPTs come from Meta-rules

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Meta-rules

• Describes how other rules should be used
• Basic Meta-rule

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Other Meta-rules

• Range Restriction of property values
• Mappings between properties and ranges of
  properties
• Single range
• Specialization

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Other Meta-rules

• Mappings between super classes
• Children matching depends on parents matching
• Fixed Influence Method (FI) P.9
• Initial Probability Method (AP) P yc
• Parent Probability Method (PP) P xc

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Probability Distribution
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Combining Influences

• We assume that the parents are conditionally
  independent
• PCA,B PCA x PCB
• Fix of this for future work

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Results

• 2 Sets of 11 and 19 nodes
• Predicate matching was manual
• Thresholds were .85 and .15

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Results
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Strengths

• Innovative research
• Published at ISWC
• Mathematically oriented

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Weaknesses

• Lots of typos
• No comparison with current methods
• Little literature research
• Could use better explanation of basic concepts

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Future Work

• Handling conditionally dependency of parent nodes
• Handling of matching predicates
• Automatic pruning and building of the network

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