This document discusses type inference on noisy RDF data. It describes how RDF data like DBpedia contains many missing type statements that limit the ability to issue structured queries. A naive approach to add types via RDFS reasoning is shown to fail on noisy data. The document then presents the SDType approach which uses statistical type predictions based on domain/range declarations and property occurrences to infer missing types. Evaluation shows SDType achieves high precision and recall on DBpedia and OpenCyc data and was used to add over 3 million type statements to DBpedia.

1. Type Inference on Noisy RDF Data
10/31/13 Paulheim, Christian Bizer
Heiko Paulheim, Christian Bizer
Heiko
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2. The Problem
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One promise of the Semantic Web:
– You can issue structured queries
– e.g., „List all presidents that graduated from Harvard Law School“
– SELECT ?x WHERE {
?x a dbpedia-owl:President .
?x dbpedia-owl:almaMater
dbpedia:Harvard_Law_School }
10/31/13
Heiko Paulheim, Christian Bizer
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3. The Problem
•
SELECT ?x WHERE {
?x a dbpedia-owl:President .
?x dbpedia-owl:almaMater
dbpedia:Harvard_Law_School }
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...if we run this against DBpedia, we get one result
– i.e., Elwell Stephen Otis
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But...
10/31/13
Heiko Paulheim, Christian Bizer
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4. The Problem
10/31/13
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5. The Problem
•
So what is going wrong?
•
SELECT ?x WHERE {
?x a dbpedia-owl:President .
?x dbpedia-owl:almaMater
dbpedia:Harvard_Law_School }
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In DBpedia, Barack Obama is not of type President!
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How can we add missing types?
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Heiko Paulheim, Christian Bizer
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6. Is It a Big Problem?
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DBpedia has at least 2.7 million missing type statements
– w.r.t. the DBpedia ontology
– found using co-occurence analysis of matching classes
in YAGO and DBpedia
– a very optimistic lower bound
•
Highly incomplete classes:
– Species: >870,000 missing statements
– Person: >510,000 missing statements
– Event: >150,000 missing statements
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7. A Naive Approach
•
Idea: exploit properties with domain and range
•
Pseudo RDFS Reasoning:
– CONSTRUCT {?x a ?t}
WHERE { {?x ?r ?y . ?r rdfs:domain ?t}
UNION
{?y ?r ?x . ?r rdfs:range ?t} }
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8. A Naive Approach
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Experiment with Barack Obama
– Person, PersonFunction, Actor, Organization
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Experiment with Germany:
– Place, Award, Populated Place, City, SportsTeam, Mountain, Agent,
Organisation, Country, Stadium, RecordLabel, MilitaryUnit, Company,
EducationalInstitution, PersonFunction, EthnicGroup, Architect, WineRegion,
Language, MilitaryConflict, Settlement, RouteOfTransportation
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9. A Naive Approach
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What is going on here?
– DBpedia data is noisy
– One wrong statement is enough for a wrong conclusion
– e.g.: dbpedia:Kurt_H._Debus dbpedia-owl:award dbpedia:Germany
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Germany example: 69,000 statements
– 20 wrong types can come from 20 wrong statements
– i.e., an error rate of 0.03% is enough for a totally screwed result
– ...but that would be an excellent data quality for a LOD source!
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Heiko Paulheim, Christian Bizer
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10. SDType Approach
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Idea: outgoing/incoming properties are indicators
for a resource's type
– e.g.: starring → Movie
– e.g.: author-1 → Writer
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Basic compiled statistics
– P(C|p) := probability of class C in presence of property p
– e.g.: P(dbpedia:Film|starring) = 0.79
– e.g.: P(dbpedia:Writer|author-1) = 0.44
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11. SDType Approach
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Based on precompiled statistics
– Find types of instances
– Using voting
•
score(C) = avg(all properties p) P(C|p)
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Refinement:
– Weight for properties: discriminative power
– weight(p) = sum(all classes c) (p(c)-p(c|p))²
– i.e., how strongly this property's class distribution
deviates from the overall class distribution
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12. Evaluation
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Two fold evaluation
– On DBpedia and OpenCyc as „Silver Standard“
(automatic, 10,000 random instances)
– On untyped DBpedia resources (manual, 100 instances)
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Using only incoming properties
– Using outgoing properties is trivial!
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13. Evaluation Results
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On DBpedia
1
0.9
0.8
Precision
0.7
0.6
min. 1 link
min. 10 links
min. 25 links
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Recall
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14. Evaluation Results
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On OpenCyc
1
0.9
0.8
Precision
0.7
0.6
min. 1 link
min. 10 links
min. 25 links
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Recall
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15. Evaluation Results
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Evaluation on untyped resources
– Random sample of 100 untyped resources
– Manual checking of precision
1
12
0.9
10
0.8
0.7
Precision
0.6
0.5
6
0.4
4
0.3
0.2
# found types
8
# found
types
precision
2
0.1
0
0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Lower bound for threshold
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16. Evaluation Results
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DBpedia:
– works reasonably well (F-measure 0.89)
•
OpenCyc:
– harder because of deeper class hierarchy (F-measure 0.60)
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General:
– having more links increases precision
(in contrast to RDFS reasoning)
– more general types (e.g., Band) are easier than specific ones
(e.g., PunkRockBand)
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17. Deployment
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Heuristic types have been included in DBpedia 3.9
– for previously untyped instances
– 3.4 million type statements at precision ~0.95
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Includes also many resources without a Wikipedia page
– i.e., generated from a red link
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Runtime
– Complexity O(PT)
P: number of property assertions
T: number of type assertions
– ~24h for processing DBpedia
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18. Conclusion and Outlook
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SDType approach works at high quality
– outperforms most state of the art on DBpedia
– deployed for DBpedia 3.9
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Same approach can be used for
– validating links
– within dataset: deployed for DBpedia 3.9 (removed ~13,000 wrong statements)
– across datasets: to be done
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19. Type Inference on Noisy RDF Data
10/31/13 Paulheim, Christian Bizer
Heiko Paulheim, Christian Bizer
Heiko
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