Mapping the Central LOD
Ontologies to PROTON Upper-
Level Ontology
Mariana Damova, Atanas Kiryakov, Kiril
Simov, Svetoslav...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
Linking Open Data (LOD)
FactForge (http://factforge.net)
• a reason-able view of the web of data
• the biggest and most he...
Access to FactForge
Forest Interface
- keyword search (for molecules in the RDF graph)
- auto-suggestion of URI
- SPARQL q...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
Current State
Target State
SELECT * WHERE {
?Person dbp-ont:birthPlace ?BirthPlace ;
rdf:type opencyc:Entertainer ;
?Birth...
Benefit
easier and simpler access to the wealth of data
(one needs to know a single ontology instead of learning the
vocab...
Applications
semantic search and annotation using
the entities from FactForge
semantic browsing and navigationsemantic bro...
PROTON - a modular, lightweight, upper-level ontology defining
about 300 classes and 100 properties
Solution
PROTON
DBPedi...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
Approaches to Matching Ontologies
• syntactic vs. semantic mapping
• harmonizing semantics (interlingua ontology)
• bidire...
Ontological Heterogeneity
• Semantic matching approaches cannot cope with
ontological heterogeneity
• The classes and the ...
Our Approach
unidirectional semantic manual alignment between
PROTONPROTON
and
the schema ontologies of the selected datas...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
Mapping Methods
• A series of iterations of enrichments at conceptual and
at data levels
(b) Extending PROTON with classes...
Mapping rules
(a) dbp:Place
owl:subClassOf proton:Location .
(b) dbp-prop:location
rdfs:subPropertyOf proton:locatedIn .
(...
Conceptual mismatches
dbp:Architect
rdfs:subClassOf
[ rdf:type owl:Restriction ;
owl:onProperty
proton:hasProfession ;
owl...
Multiple Matching
DBPedia predicate
DBPedia ontology
predicate
PROTON predicate
dbp:place
rdfs:subPropertyOf proton:locate...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
PROTON Extension
• Preserve the OntoClean approach in PROTON design
• Obtain coverage of the rich data in FactForge
• Keep...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
Statistics
• PROTON Extension
– 141 new Classes
– 3 new Datatype properties
– 12 new Object properties
• Mapping PROTON to...
Outline
• Introduction
• Problem and Conceptual Solution
• Approaches to Matching Ontologies
• Mapping Methods• Mapping Me...
Experiment
Data Loading
BigOWLIM – the most scalable OWL Engine
http://www.ontotext.com/owlim/
FactForge Standard
June 201...
FactForge Extension Statistics
FactForge FactForge with
Inference Rules
FactForge with IR
and PROTON
FactForge with IR,
PR...
FactForge Extension Statistics
Difference
between FactForge and Each Adding Iteration
FactForgeFactForge and
FactForge wit...
FactForge Extension Statistics
Difference
between Each Adding Iteration
FactForge FactForge and
FactForge with
Inference R...
Experimentation: PROTON query
US non-profit organizations founded after 1950
PREFIX p-ext: <http://proton.semanticweb.org/...
Query: Prime Ministers born in the United Kingdom
PREFIX dbp-ont: <http://dbpedia.org/ontology/>
PREFIX dbpedia: <http://d...
Query: Cities around the world which have Modigliani art work
PREFIX fb: <http://rdf.freebase.com/ns/>
PREFIX dbpedia: <ht...
Query: Cities around the world which have Modigliani art work
PREFIX dbpedia: <http://dbpedia.org/resource/>
PREFIX rdf: <...
Outline
• Introduction
• Problem
• Conceptual Solution
• Approaches to Matching Ontologies• Approaches to Matching Ontolog...
Future Work
• Two-level intermediary layer to access FactForge
mapping PROTON to UMBEL
Dataset1 Dataset2 Dataset3 Dataset4...
Future Work
• Official FactForge release with the presented
mapping
• Publish Proton and mapping as LOD
• Cover more datas...
http://factforge.net
(a version with Proton mapping is currently available at
http://ldsr4.ontotext.com)
Service available...
Mapping Lo Dto Proton Revised [Compatibility Mode]
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Mapping Lo Dto Proton Revised [Compatibility Mode]

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Presentation at the Ontology Matching Workshop held at ISWC\'2010

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Transcript of "Mapping Lo Dto Proton Revised [Compatibility Mode]"

  1. 1. Mapping the Central LOD Ontologies to PROTON Upper- Level Ontology Mariana Damova, Atanas Kiryakov, Kiril Simov, Svetoslav Petrov ISWC’2010
  2. 2. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  3. 3. Linking Open Data (LOD) FactForge (http://factforge.net) • a reason-able view of the web of data • the biggest and most heterogeneous body of factual knowledge on which inference is performed • 8 datasets from the LOD cloud (DBPedia, Freebase, UMBEL, CIA World Factbook, MusicBrainz, Wordnet, Lingvoj, Geonames) • an overall of 1,4 billion loaded statements • 2,2 billion stored statements (indexed) • 9,8 billion distinct retrievable statements
  4. 4. Access to FactForge Forest Interface - keyword search (for molecules in the RDF graph) - auto-suggestion of URI - SPARQL queries using facts from different datasets (formulating SPARQL queries requires in depth knowledge about the datasets and the schemata in FactForge) - exploration
  5. 5. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  6. 6. Current State Target State SELECT * WHERE { ?Person dbp-ont:birthPlace ?BirthPlace ; rdf:type opencyc:Entertainer ; ?BirthPlace geo-ont:parentFeature dbpedia:Germany . } Target State SELECT * WHERE { ?Person prot:birthPlace ?BirthPlace ; rdf:type prot:Entertainer ; ?BirthPlace prot:subRegionOf dbpedia:Germany . }
  7. 7. Benefit easier and simpler access to the wealth of data (one needs to know a single ontology instead of learning the vocabularies of multiple datasets) higher degree of interoperability offering only one single schema better integration of the datasets in FactForge (so the schemata of the different datasets are mapped through a single ontology) information from many datasets via a unified vocabulary (a constraint which uses a single predicate from the ontology can match data from different datasets)
  8. 8. Applications semantic search and annotation using the entities from FactForge semantic browsing and navigationsemantic browsing and navigation querying FactForge in natural language many others
  9. 9. PROTON - a modular, lightweight, upper-level ontology defining about 300 classes and 100 properties Solution PROTON DBPedia Freebase Geonames about 300 classes and 100 properties DBPedia - the RDFized version of Wikipedia, data-driven ontology, an overall of 592 classes and 720 properties, and about 1 478 000 instances Freebase - a social database, no ontology, hidden class hierarchy in structured predicate names, 19632 predicates Geonames – a geographical database that covers 6 million geographical features with ontology of 5 classes, and 26 properties, and about 645 location denotators
  10. 10. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  11. 11. Approaches to Matching Ontologies • syntactic vs. semantic mapping • harmonizing semantics (interlingua ontology) • bidirectional vs. unidirectional • automated vs. manual• automated vs. manual • OAEI - ontology matching competitions – benchmark with best F-measure result of 80% 2009
  12. 12. Ontological Heterogeneity • Semantic matching approaches cannot cope with ontological heterogeneity • The classes and the properties may be described in different unrelated ontologies • The algorithms cannot discover hidden relationships• The algorithms cannot discover hidden relationships that hold between unrelated entities. FactForge presents exactly such a reality of heterogeneous facts. This makes their automated processing inconvenient.
  13. 13. Our Approach unidirectional semantic manual alignment between PROTONPROTON and the schema ontologies of the selected datasets of FactForge
  14. 14. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  15. 15. Mapping Methods • A series of iterations of enrichments at conceptual and at data levels (b) Extending PROTON with classes and properties (a) Subsumption relations for classes and properties FactForge entity PROTON entity subClassOf (c) Using OWL class and property construction capabilities to represent classes and properties from FactForge and map them to PROTON classes Class restricted from FactForge predicates PROTON entity subClassOf (d) Extending FactForge with instances to account for the conceptual representation of the matching FactForge Instance 1 FactForge Instance 2 (new) FactForge Instance 3 (new)
  16. 16. Mapping rules (a) dbp:Place owl:subClassOf proton:Location . (b) dbp-prop:location rdfs:subPropertyOf proton:locatedIn . (c) pfb:Location(c) pfb:Location rdf:type owl:Restriction ; owl:onProperty <http://rdf.freebase.com/ns/type.object.type> ; owl:hasValue <http://rdf.freebase.com/ns/location.location> ; rdfs:subClassOf proton:Location . (d) p <rdf:type> <dbp-ont:PrimeMinister> ---------------------------------------------------- p <proton:hasPosition> j j <proton:hasTitle> <proton:PrimeMinister>
  17. 17. Conceptual mismatches dbp:Architect rdfs:subClassOf [ rdf:type owl:Restriction ; owl:onProperty proton:hasProfession ; owl:hasValue proton:Architect ] . Expression matching
  18. 18. Multiple Matching DBPedia predicate DBPedia ontology predicate PROTON predicate dbp:place rdfs:subPropertyOf proton:locatedIn . dbp-prop:location rdfs:subPropertyOf proton:locatedIn . <http://rdf.freebase.com/ns/time.event.locations> rdfs:subPropertyOf proton:locatedIn . Freebase predicate
  19. 19. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  20. 20. PROTON Extension • Preserve the OntoClean approach in PROTON design • Obtain coverage of the rich data in FactForge • Keep an optimal degree of granularity of the concept hierarchy Proton was split into modules : - 19 modules reflecting the conceptual divisions which surfaced during the analysis of the data, e.g. proton event, proton social abstraction, proton location, proton biological substance, etc.
  21. 21. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  22. 22. Statistics • PROTON Extension – 141 new Classes – 3 new Datatype properties – 12 new Object properties • Mapping PROTON to FactForge – 536 subClassOf relations – 36 subPropertyOf relations
  23. 23. Outline • Introduction • Problem and Conceptual Solution • Approaches to Matching Ontologies • Mapping Methods• Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  24. 24. Experiment Data Loading BigOWLIM – the most scalable OWL Engine http://www.ontotext.com/owlim/ FactForge Standard June 2010 FactForge with mappings June 2010 NumberOfStatements 1,782,541,506 2,630,453,334 NumberOfExplicitStatements 1,143,317,531 1,942,349,578 NumberOfEntities 354,635,159 404,798,593
  25. 25. FactForge Extension Statistics FactForge FactForge with Inference Rules FactForge with IR and PROTON FactForge with IR, PROTON and DBP mappings FactForge with IR, PROTON, DBP mappings and Freebase mappings NumberOfStatements October 2010 2,237,550,385 2,237,550,617 2,237,578,643 2,255,543,166 2,375,287,183 NumberOfExplicitStatements 1,357,013,227 2,027,992,627 2,027,995,363 2,027,995,651 2,027,995,750 NumberOfEntities 524,120,454 524,120,465 524,121,955 524,121,996 524,122,009
  26. 26. FactForge Extension Statistics Difference between FactForge and Each Adding Iteration FactForgeFactForge and FactForge with FactForge and FactForge with factForge and FactForge with factForge and FactForge with October 2010 FactForge with Inference Rules FactForge with IR and PROTON FactForge with IR, PROTON and DBP mappings FactForge with IR, PROTON, DBP mappings and Freebase mappings NumberOfStatements 0 232 28,258 17,992,781 137,736,798 NumberOfExplicitStatem ents 0 670,979,400 670,982,136 670,982,424 670,982,523 NumberOfEntities 0 11 1,501 1,542 1,555
  27. 27. FactForge Extension Statistics Difference between Each Adding Iteration FactForge FactForge and FactForge with Inference Rules FactForge with Inference Rules and FactForge with IR and FactForge with IR and PROTON and FactForge with IR, PROTON and FactForge with IR, PROTON and DBP mappings and FactForge October 2010 with IR and PROTON IR, PROTON and DBP mappings and FactForge with IR, PROTON, DBP mappings and Freebase mappings NumberOfStatements 0 232 28,026 17,964,523 119,744,017 NumberOfExplicitStatements 0 670,979,400 2,736 288 99 NumberOfEntities 0 11 1490 41 13
  28. 28. Experimentation: PROTON query US non-profit organizations founded after 1950 PREFIX p-ext: <http://proton.semanticweb.org/protonue#> PREFIX ptop: <http://proton.semanticweb.org/protont#> PREFIX dbpedia: <http://dbpedia.org/resource/> SELECT distinct ?s ?date ?l WHERE { ?s a p-ext:Non-ProfitOrganisation . ?s ptop:establishmentDate ?date . ?s ptop:locatedIn ?l . ?l ptop:subRegionOf dbpedia:United_States . FILTER (?date > "1950") }
  29. 29. Query: Prime Ministers born in the United Kingdom PREFIX dbp-ont: <http://dbpedia.org/ontology/> PREFIX dbpedia: <http://dbpedia.org/resource/> SELECT ?PrimeMinister WHERE { ?PrimeMinister rdf:type dbp-ont:PrimeMinister . ?PrimeMinister dbp-ont:birthPlace dbpedia:United_Kingdom . } PREFIX pupp: <http://proton.semanticweb.org/protonu#> PREFIX p-ext: <http://proton.semanticweb.org/protonue#> PREFIX dbpedia: <http://dbpedia.org/resource/> PREFIX ptop: <http://proton.semanticweb.org/protont#> SELECT ?PrimeMinister WHERE { ?PrimeMinister ptop:hasPosition ?pos . ?pos pupp:hasTitle dbpedia:British_prime_minister . ?PrimeMinister p-ext:birthPlace dbpedia:United_Kingdom . }
  30. 30. Query: Cities around the world which have Modigliani art work PREFIX fb: <http://rdf.freebase.com/ns/> PREFIX dbpedia: <http://dbpedia.org/resource/> PREFIX dbp-prop: <http://dbpedia.org/property/> PREFIX dbp-ont: <http://dbpedia.org/ontology/> PREFIX umbel-sc: <http://umbel.org/umbel/sc/> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX ot: <http://www.ontotext.com/> SELECT DISTINCT ?painting_l ?owner_l ?city_fb_con ?city_db_loc ?city_db_cit WHERE {WHERE { ?p fb:visual_art.artwork.artist dbpedia:Amedeo_Modigliani ; fb:visual_art.artwork.owners [ fb:visual_art.artwork_owner_relationship.owner ?ow ] ; ot:preferredLabel ?painting_l. ?ow ot:preferredLabel ?owner_l . OPTIONAL { ?ow fb:location.location.containedby [ ot:preferredLabel ?city_fb_con ] } OPTIONAL { ?ow dbp-prop:location ?loc. ?loc rdf:type umbel-sc:City ; ot:preferredLabel ?city_db_loc } OPTIONAL { ?ow dbp-ont:city [ ot:preferredLabel ?city_db_cit ] } }
  31. 31. Query: Cities around the world which have Modigliani art work PREFIX dbpedia: <http://dbpedia.org/resource/> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX ot: <http://www.ontotext.com/> PREFIX ptop: <http://proton.semanticweb.org/protont#> PREFIX p-ext: <http://proton.semanticweb.org/protonue#> SELECT DISTINCT ?painting ?owner ?city WHERE { ?p p-ext:author dbpedia:Amedeo_Modigliani ; p-ext:ownership [ ptop:isOwnedBy ?ow ] ; ot:preferredLabel ?painting . ?ow ot:preferredLabel ?owner . ?ow ptop:locatedIn [ rdf:type pupp:City ; ot:preferredLabel ?city]. }
  32. 32. Outline • Introduction • Problem • Conceptual Solution • Approaches to Matching Ontologies• Approaches to Matching Ontologies • Mapping Methods • Proton Extension • Statistics • Experimentation • Future Work
  33. 33. Future Work • Two-level intermediary layer to access FactForge mapping PROTON to UMBEL Dataset1 Dataset2 Dataset3 Dataset4Datasets Upper Level Ontology 2Two level Ontology 1 Ontology 2 Ontology 3 Ontology 4 Upper Mapping and Binding Exchange Layer A lightweight, subject concept reference structure for the Web Upper Mapping and Binding Exchange Layer A lightweight, subject concept reference structure for the Web Query triples ?S P O ?S P1 ?O1 ?O1 P2 O2 Upper Level Ontology 2Two level Intermediate layer Upper Level Ontology 1 http://www.ontotext.com/news.html#umb_25oct10 UMBEL - Upper Mapping and Binding Exchange Layer A lightweight, subject concept reference structure for the Web 20 000 concepts mapped to OpenCyc Strategic partnership Ontotext – Structured Dynamics
  34. 34. Future Work • Official FactForge release with the presented mapping • Publish Proton and mapping as LOD • Cover more datasets from the LOD cloud• Cover more datasets from the LOD cloud • Experiment with the balance between the datasets and the ontologies describing them • Extend the property mapping
  35. 35. http://factforge.net (a version with Proton mapping is currently available at http://ldsr4.ontotext.com) Service available at Thank you for your attention! Questions? Contact: mariana.damova@ontotext.com
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