leveraging data and structure in ontology integration
DESCRIPTION
Leveraging Data and Structure in Ontology Integration. Octavian Udrea 1 Lise Getoor 1 Renée J. Miller 2 1 University of Maryland College Park 2 University of Toronto. Contents. Motivation and goals Short overview of OWL Lite The ILIADS method Experimental evaluation. ILIADS. Goal: - PowerPoint PPT PresentationTRANSCRIPT
Leveraging Data and Structure in Ontology Integration
Octavian Udrea1
Lise Getoor1
Renée J. Miller2
1University of Maryland College Park2University of Toronto
Contents
Motivation and goals Short overview of OWL Lite The ILIADS method Experimental evaluation
ILIADS Goal:
Produce high-quality integration via a flexible method able to adapt to a wide variety of ontology sizes and structures
Method: Combining statistical and logical inference Use schema (structure) and data (instances)
effectively Solution:
Integrated Learning In Alignment of Data and Schema (ILIADS)
Contributions Show how to combine statistical and logical
inference effectively
Show that a small amount of inference yields high qualitative gain
Show that parameters needed to perform inference over data and structure are robust
Provide a thorough evaluation on 30 pairs of real-world ontologies (with ground truth)
Contents
Motivation and goals Short overview of OWL Lite The ILIADS method Experimental evaluation
Example OWL Lite ontologies
(discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty)(discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty)(resultsF rom, rdfs:subPropertyOf, associatedWith)
Example OWL Lite ontologies
An entity can be a:• Class
(discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty)(discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty)(resultsF rom, rdfs:subPropertyOf, associatedWith)
Example OWL Lite ontologies
An entity can be a:• Class• Instance
(discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty)(discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty)(resultsF rom, rdfs:subPropertyOf, associatedWith)
Example OWL Lite ontologies
An entity can be a:• Class• Instance• Property
(discoveredBy, owl:inverseOf, discoverer); (discoveredBy, owl:type, owl:FunctionalProperty)(discoveredBy, owl:inverseOf, discoverer); (associatedWith, owl:type, owl:TransitiveProperty)(resultsF rom, rdfs:subPropertyOf, associatedWith)
Example OWL Lite ontologies
(discoveredBy, owl:inverseOf, discoverer)(discoveredBy, owl:type, owl:FunctionalProperty)(discoveredBy, owl:inverseOf, discoverer)(associatedWith, owl:type, owl:TransitiveProperty)(resultsF rom, rdfs:subPropertyOf, associatedWith)
Inference in OWL Lite
Inference in OWL Lite
Inference in OWL Lite
The integration problem
The integration problem
The integration problem
The integration problem
Contents
Motivation and goals Short overview of OWL Lite The ILIADS method Experimental evaluation
State of the art Robust statistical methods
Well-known similarity measures Used for matching data (entities) and schema May use graph structure of schema
Logical inference Not combined with statistical inference Basis for most schema mapping and ontology
integration methods Approaches integrate schema, but not data
Issues How to combine statistical inference with
logical inference Takes into account data, structure, etc. so it’s no
longer obvious In particular, how to quantify the results of logical
inference into a similarity-like form? How to do logical inference in a tractable
manner For OWL-Lite, EXPTIME-complete for the worst
case for the entire ontology
The ILIADS algorithm
repeat until no more candidates
1. Compute local similarities
2. Select promising candidates
3. For each candidatea. Select relationship
b. Perform logical inference
c. Update score with the inference similarity
4. Select the candidate with the best score
end
The ILIADS algorithm
repeat until no more candidates
1. Compute local similarities
2. Select promising candidates
3. For each candidatea. Select relationship
b. Perform logical inference
c. Update score with the inference similarity
4. Select the candidate with the best score
end
Computing local similarities
simlexical: Jaro-Winkler and Wordnet
simstructural: Jaccard for neighborhoods
simextensional: Jaccard on extensions
parameters: λx, λs, λe
different for classes, instances and properties
)e(e,sim
)e(e,sim
)e(e,sim )esim(e,
extensione
structures
lexicalx
The ILIADS algorithm
repeat until no more candidates
1. Compute local similarities
2. Select promising candidates
3. For each candidatea. Select relationship
b. Perform logical inference
c. Update score with the inference similarity
4. Select the candidate with the best score
end
Selecting promising candidates
1. Select candidates with sim(e,e’) > λt
2. Use a policy based on entity type to order, e.g.:
Class alignments first Instance alignments firstAlternate between classes and instances
The ILIADS algorithm
repeat until no more candidates
1. Compute local similarities
2. Select promising candidates
3. For each candidatea. Select relationship
b. Perform logical inference
c. Update score with the inference similarity
4. Select the candidate with the best score
end
Selecting relationship Must decide on relation type
subClassOf vs. equivalentClass subPropertyOf vs. equivalentProperty
Determination is difficult, especially under the OWL open-world semantics
Use a simple extension based technique based on a threshold λr
Selecting relationship
Selecting relationship
The ILIADS algorithm
repeat until no more candidates
1. Compute local similarities
2. Select promising candidates
3. For each candidatea. Represent candidate relationship
b. Perform logical inference
c. Update score with the inference similarity
4. Select the candidate with the best score
end
Performing logical inference
For the candidate pair (e,e’): Select an axiom to apply The logical consequences are the pairs of
entities (e(i), e(j)) that have just become equivalent
Repeat a small number of times (5) to maintain tractability
Performing logical inference
Performing logical inference
Performing logical inference
Performing logical inference
(TheodorEscherich, owl:sameAs, T.S. Escherich) is a logical consequence of the candidate (E-ColiPoisoning, owl:sameAs, E-Coli)
The ILIADS algorithm
repeat until no more candidates
1. Compute local similarities
2. Select promising candidates
3. For each candidatea. Represent candidate relationship
b. Perform logical inference
c. Update score with the inference similarity
4. Select the candidate with the best score
end
Updating score
For the candidate pair (e,e’): Initial local similarity sim(e,e’) Inference similarity over all consequences:
Updated similarity:
e,esim-1
e,esim
(j)(i) e,e(j)(i)
(j)(i)
P
Pss *e'e,ime'e,imupdated
Updating score
Updating score
Consistency The constructed alignment is not guaranteed
to be consistent ILIADS can only detect inconsistencies that
appear in the few logical inference steps Pellet used to check consistency after ILIADS
Experimentally, inconsistent ontologies in less than .5% of runs
Contents
Motivation and goals Short overview of OWL Lite The ILIADS method Experimental evaluation
Experimental framework 30 pairs of real-world ontologies
From 194 to over 20,000 triples From a variety of domains: medical, geographical,
economical, biological
Ground truth provided by human reviewers Multiple iterations to ensure the best human-
provided alignment
Datasets available: http://www.cs.umd.edu/linqs/projects/iliads
Experimental framework Evaluation: precision, recall and F1 quality
F1 = 2 * Precision * Recall / (Precision + Recall) 7 independent runs
ILIADS Variations: ILIADS-tailored uses the best set of parameters
for each pair of ontologies ILIADS-fixed uses one set of parameters for all
pairs of ontologies Used to evaluate robustness of the parameters
Experimental framework ILIADS compared to two leading tools:
FCA-merge [Stumme and Maedche, IJCAI 2001] uses formal concept analysis and an external
document corpus
COMA++ [Aumueller et al., SIGMOD 2005] implements multiple match strategies,
including fragment and reuse-based matching
Precision/recall
Precision/recall
Precision/recall
Precision/recall
Precision/recall comparison
Precision/recall for ontologies with substantial instance data
Number of inference steps
ILIADS parameters
ILIADS-fixed
.2 .4 .1 .5 .6 .4 .3 .5 .7 .2
Min ILIADS-tailored
.15 .4 0 .3 .45 .35 .2 .35 .65 .2
Max IILIADS-tailored
.25 .45 .1 .65 .7 .5 .35 .65 .7 .2
cx i
x px c
sis
ps
ce
pe t r
Lexical parameters
Structuralparameters
Extensionalparameters
Choosing ILIADS parameters Despite the number of parameters, method is
quite robust Parameters are stable around the ILIADS-fixed
values if the two ontologies in a pair are not very different
Strong correlations between Structural similarity coefficients and the average
node degree Extensional coefficients and the ratio of instances
to classes
False negative analysis
Concluding remarks New ontology integration algorithm
First to combine statistical and logical inference
Evaluated feasibility of combined inference Small number of logical inference steps are sufficient
for integration decisions Inference is stable to parameter settings Parameters permit principled tuning based on
ontology characteristics
Dataset and code available at:http://www.cs.umd.edu/linqs/projects/iliads