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![Related Work
• Schema matching & schema mapping
– iMAP [Dhamankar et al., 2004], Clio [Fagin et al., 2009]
• Mapping databases and spreadsheets to ontologies
– Mapping languages: D2R [Bizer, 2003], D2RQ [Bizer and Seaborne, 2004], R2RML [Das et
al., 2012]
– Tools: RDOTE [Vavliakis et al., 2010], RDF123 [Han et al., 2008], XLWrap [Langegger
and Woß, 2009]
– String similarity between column names and ontology terms [Polfliet and Ichise, 2010]
• Understand semantics of Web tables
– Use column headers and cell values to find the labels and relations from a database of
labels and relations populated from the Web [Wang et al., 2012] [Limaye et al., 2010]
[Venetis et al., 2011]
• Exploit Linked Open Data (LOD)
– Link the values to the entities in LOD to find the types of the values and their
relationships [Muoz et al., 2013] [Mulwad et al., 2013]
• Learn Semantic Definitions of Online Information Sources [Carman,
Knoblock, 2007]
– Learns LAV rules from known sources
– Only learns descriptions that are conjunctive combinations of known descriptions
30](https://image.slidesharecdn.com/icsc2014-v2-140618131618-phpapp01/75/A-Scalable-Approach-to-Learn-Semantic-Models-of-Structured-Sources-31-2048.jpg)
![Related Work
• Schema matching & schema mapping
– iMAP [Dhamankar et al., 2004], Clio [Fagin et al., 2009]
• Mapping databases and spreadsheets to ontologies
– Mapping languages: D2R [Bizer, 2003], D2RQ [Bizer and Seaborne, 2004], R2RML [Das et
al., 2012]
– Tools: RDOTE [Vavliakis et al., 2010], RDF123 [Han et al., 2008], XLWrap [Langegger
and Woß, 2009]
– String similarity between column names and ontology terms [Polfliet and Ichise, 2010]
• Understand semantics of Web tables
– Use column headers and cell values to find the labels and relations from a database of
labels and relations populated from the Web [Wang et al., 2012] [Limaye et al., 2010]
[Venetis et al., 2011]
• Exploit Linked Open Data (LOD)
– Link the values to the entities in LOD to find the types of the values and their
relationships [Muoz et al., 2013] [Mulwad et al., 2013]
• Learn Semantic Definitions of Online Information Sources [Carman,
Knoblock, 2007]
– Learns LAV rules from known sources
– Only learns descriptions that are conjunctive combinations of known descriptions
30](https://crownmelresort.com/image.slidesharecdn.com/icsc2014-v2-140618131618-phpapp01/75/A-Scalable-Approach-to-Learn-Semantic-Models-of-Structured-Sources-31-2048.jpg)
Uploaded byMohsen Taheriyan
A Scalable Approach to Learn Semantic Models of Structured Sources
The document discusses a scalable method for learning semantic models from structured data sources, emphasizing the need for explicit semantics in various domain ontologies. It outlines a process involving sampling new data, leveraging known models, and generating ranked semantic models while addressing the limitations of existing approaches. Contributions include the use of a beam search algorithm for scoring and pruning semantic mappings, ultimately aimed at automating data integration and publishing RDF triples.
A Scalable Approach to Learn Semantic Models of Structured Sources
- 1. A Scalable Approachto Learn Semantic Models of Structured Sources Mohsen Taheriyan Craig Knoblock Pedro Szekely Jose Luis Ambite 8th IEEE International Conference on Semantic Computing
- 2. Motivation 1 How to expressthe intended meaning of data? Explicit semantics is missing in many of the structured sources creator? actor? rightsHolder? artwork? movie? referenced entity?
- 3. Map the Sourceto the Domain Ontology 2 EDM: Europeana Data Model SKOS: Simple Knowledge Organization System FOAF: Friend of a Friend AAC: American Art Collaborative ElementsGr2: RDA Group 2 Elements ORE: Open Archive Initiative DCTerms: Dublin Core Metadata Terms Data Source: artworks in the Indianapolis Museum of Art Domain ontologies Semantic Model: a mapping from the source to the concepts and relationships defined by the domain ontologies
- 4. Semantic Model 3 aac:CulturalHeritageObject edm:WebResourc e skos:Concept aac:Person edm:EuropeanaAggregation dcterms:title edm:aggregatedCHO skos:prefLabel ElementsGr2: nameOfThePerson rdf:type edm:hasResource dcterms:creator edm:hasType dcterms:description Keyingredient to automate source discovery, data integration, and publishing RDF triples
- 5. 4 Problem: How to automaticallylearn a semantic model for a source
- 6. Main Idea 5 Sources inthe same domain often have similar data Exploit knowledge of known semantic models to hypothesize a semantic model for a new sources
- 7. Previous Approach (ISWC2013) 6 Input Learn semantic types for attributes(s) • Sample data from new source (S) • Domain Ontologies (O) • Known semantic models Construct Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S
- 8. Limitations 7 Input Learn semantic typesfor attributes(s) • Sample data from new source (S) • Domain Ontologies (O) • Known semantic models Construct Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S Consider only one semantic type (label) for each attribute Not scalable to sources with a large number of attributes
- 9. Contributions 8 Input Learn semantic typesfor attributes(s) • Sample data from new source (S) • Domain Ontologies (O) • Known semantic models Build Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S Consider K candidate semantic types per attribute A Beam search algorithm to score and prune the mappings
- 10. Example 9 New source: IndianapolisMuseum of Art EDM SKOS FOAF AAC ElementsGr2ORE DCTermsDomain ontologies: S1(title, creationDate, name, birthDate, deathDate, type) Known Semantic Models: S1: Dallas Museum S2: The Metropolitan Museum of Art S2(name, copyright, materials, dimensions, imageUrl) S(title, label, image, type, artist) Goal: Semantic model for source S Semantic model of S1 Semantic model of S2
- 11. • Sample datafrom new source (S) Approach 10 Input Learn semantic types for attributes(s) • Domain Ontologies (O) • Known semantic models Construct Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S
- 12. Learn Semantic Types •A CRF-based machine learning technique to learn Semantic Types for each attribute from its data • Semantic Type – Ontology Class: <class_uri> – Data Property + Domain Class: <class_uri, property_uri> • Pick top K semantic types according to their confidence values 11 New source: S(title, label, image, type, artist) title <aac:CulturalHeritageObject, dcterms:title> 0.19 <aac:CulturalHeritageObject, rdfs:label> 0.08 label <aac:CulturalHeritageObject, dcterms:description> 0.7 <aac:Person, ElementsGr2:note> 0.03 image <edm:WebResource> 0.58 <foaf:Document> 0.41 type <skos:Concept, skos:prefLabel> 0.82 <skos:Concept, rdfs:label> 0.15 name <foaf:Person, foaf:name> 0.27 <aac:Person, ElementsGr2:nameOfThePerson> 0.19
- 13. • Sample datafrom new source (S) Approach 12 Input Learn semantic types for attributes(s) • Domain Ontologies (O) • Known semantic models Construct Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S
- 14. Build Graph G:Add Known Models 13
- 15. Build Graph G:Add Semantic Types 14
- 16. Build Graph G:Expand with Paths from Ontologies 15
- 17. • Sample datafrom new source (S) Approach 16 Input Learn semantic types for attributes(s) • Domain Ontologies (O) • Known semantic models Construct Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S
- 18. Map Source Attributesto the Graph 17 New source: S(title, label, image, type, artist) title <aac:CulturalHeritageObject, dcterms:title> <aac:CulturalHeritageObject, rdfs:label> label <aac:CulturalHeritageObject, dcterms:description> <aac:Person, ElementsGr2:note> image <edm:WebResource> <foaf:Document> type <skos:Concept, skos:prefLabel> <skos:Concept, rdfs:label> name <foaf:Person, foaf:name> <aac:Person, ElementsGr2:nameOfThePerson>
- 19. Map Source Attributesto the Graph 18 New source: S(title, label, image, type, artist) title <aac:CulturalHeritageObject, dcterms:title> <aac:CulturalHeritageObject, rdfs:label> label <aac:CulturalHeritageObject, dcterms:description> <aac:Person, ElementsGr2:note> image <edm:WebResource> <foaf:Document> type <skos:Concept, skos:prefLabel> <skos:Concept, rdfs:label> name <foaf:Person, foaf:name> <aac:Person, ElementsGr2:nameOfThePerson>
- 20. Map Source Attributesto the Graph 19 New source: S(title, label, image, type, artist) title <aac:CulturalHeritageObject, dcterms:title> <aac:CulturalHeritageObject, rdfs:label> label <aac:CulturalHeritageObject, dcterms:description> <aac:Person, ElementsGr2:note> image <edm:WebResource> <foaf:Document> type <skos:Concept, skos:prefLabel> <skos:Concept, rdfs:label> name <foaf:Person, foaf:name> <aac:Person, ElementsGr2:nameOfThePerson>
- 21. Scalability Issue • Multiplemappings from attributes(S) to nodes of G – Each attribute has more than one semantic type – Multiple matches for each semantic type • Not feasible to generate all possible mappings – The size of graph may be large – The source may have many attributes • Exponential in terms of number of attributes – N attributes, M match for each MN mappings 20
- 22. Prune the Mappings •Score the partial mappings after mapping each attribute – Coherence: number of nodes in a mapping that belong to same component – Confidence: average confidence of semantic types in m – Score = arithmetic mean of coherence and size reduction • Beam Search – Keep only top K mappings after mapping each attribute • Number of mappings will not exceed a fixed size after mapping each attribute 21
- 23. Score the Mappings 22 Newsource: S(title, label, image, type, artist) title <aac:CulturalHeritageObject, dcterms:title>, 0.19 <aac:CulturalHeritageObject, rdfs:label> label <aac:CulturalHeritageObject, dcterms:description>, 0.7 <aac:Person, ElementsGr2:note> image <edm:WebResource>, , 0.58 <foaf:Document> type <skos:Concept, skos:prefLabel>, 0.82 <skos:Concept, rdfs:label> name <foaf:Person, foaf:name>, 0.27 <aac:Person, ElementsGr2:nameOfThePerson> Coherence: 4/9 = 0.44 Confidence: 0.56 Score: 0.5 Example Mapping 2
- 24. Score the Mappings 23 Newsource: S(title, label, image, type, artist) title <aac:CulturalHeritageObject, dcterms:title>, 0.19 <aac:CulturalHeritageObject, rdfs:label> label <aac:CulturalHeritageObject, dcterms:description>, 0.7 <aac:Person, ElementsGr2:note> image <edm:WebResource>, , 0.58 <foaf:Document> type <skos:Concept, skos:prefLabel>, 0.82 <skos:Concept, rdfs:label> name <foaf:Person, foaf:name> <aac:Person, ElementsGr2:nameOfThePerson>, 0.19 Coherence: 6/9 = 0.66 Confidence: 0.55 Score: 0.605 Example Mapping 1 This mapping gets higher score even though it uses the 2nd ranked semantic type for artist
- 25. • Sample datafrom new source (S) Approach 24 Input Learn semantic types for attributes(s) • Domain Ontologies (O) • Known semantic models Construct Graph G=(V,E) Generate mappings between attributes(S) and V Generate and rank semantic models 1 2 3 4 Output • A ranked set of semantic models for S
- 26. Generate Semantic Models •Select top M mappings • Compute a Steiner tree for each mapping – A minimal tree connecting nodes of mapping • Each tree is a candidate model • Rank candidate models (Steiner trees) – Cost – Score of the corresponding mapping 25
- 27.
- 28. Evaluation • Dataset – 29museum data sources – 332 attributes (average 11 per source) • Domain ontologies – EDM ,SKOS, FOAF, ORE, ElementsGr2, AAC, DCTerms – 119 classes, 351 properties • Compute precision and recall between learned models and correct models 27 precision = rel(sm)Çrel(sm') rel(sm') recall = rel(sm)Çrel(sm') rel(sm) How many of the learned relationships are correct? How many of the correct relationships are learned?
- 29. Quality 28 k = 1 correct semantic type learned only for 62% of attributes k = 4 correct semantic type was among the 4 learned types for 87% of attributes 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 4 8 12 16 20 24 28 Number of known semantic models precision (k=1) recall (k=1) precision (k=4) recall (k=4) precision (correct types) recall (correct types)
- 30. Performance The previous approachwas not able to learn semantic models for sources with more than 4 attributes in the timeout of 1 hour Example: S16 with only 5 attributes 16,633,298 mappings (29*29*29*31*22) 0 10 20 30 40 50 60 0 5 10 15 20 25 30 Time Number of Attributes Previous Approach New Approach (Kbeam = 100)
- 31. Related Work • Schemamatching & schema mapping – iMAP [Dhamankar et al., 2004], Clio [Fagin et al., 2009] • Mapping databases and spreadsheets to ontologies – Mapping languages: D2R [Bizer, 2003], D2RQ [Bizer and Seaborne, 2004], R2RML [Das et al., 2012] – Tools: RDOTE [Vavliakis et al., 2010], RDF123 [Han et al., 2008], XLWrap [Langegger and Woß, 2009] – String similarity between column names and ontology terms [Polfliet and Ichise, 2010] • Understand semantics of Web tables – Use column headers and cell values to find the labels and relations from a database of labels and relations populated from the Web [Wang et al., 2012] [Limaye et al., 2010] [Venetis et al., 2011] • Exploit Linked Open Data (LOD) – Link the values to the entities in LOD to find the types of the values and their relationships [Muoz et al., 2013] [Mulwad et al., 2013] • Learn Semantic Definitions of Online Information Sources [Carman, Knoblock, 2007] – Learns LAV rules from known sources – Only learns descriptions that are conjunctive combinations of known descriptions 30
- 32. Future Work • Scalabilityregarding number of the known models – Create a more compact graph – Consolidate overlapping segments of known models • Leverage Linked Open Data (LOD) – Exploit the relationships between instances – Improve the accuracy of the learned relations • Integrate the new approach in Karma – http://www.isi.edu/integration/karma – @KarmaSemWeb 31
Editor's Notes
- #7 http://www.metmuseum.org/collection/the-collection-online/search?deptids=1&pg=1&ft=french&od=on&ao=on&noqs=true http://americanart.si.edu/collections/search/artwork/results/index.cfm?rows=10&fq=online_media_type%3A%22Images%22&q=Search+by+Artist%2C+Work%2C+or+Keyword&page=1&start=0&x=62&y=5 http://museum.dma.org:9090/emuseum/view/objects/asitem/2031/0/title-desc?t:state:flow=ff495581-e0d2-4334-bec9-19988f9eeb20 http://www.mfah.org/art/100-highlights/commemorative-head-king/ Leverage relationships in known semantic models to hypothesize relationships for new sources
- #24 confidence = (0.19+0.7+0.58+0.82+0.27)/5
- #25 coherence=number of nodes in same component / total number of nodes in mapping confidence = (0.19+0.7+0.58+0.82+0.19)/5 u (maximum number of nodes in mapping) = 2 * n (#attributes) = 10 l (minimum number of nodes in mapping) = n+1 = 6 size reduction = [u - size(mapping)] / [u - l + 1] = (10 - 8) / (10 - 6 + 1) = 2 / 5 = 0.4
- #32 An et al. [An et al., 2007] generate declarative mapping expressions between two tables with different schemas starting from element correspondences. They create a graph from the conceptual model (CM) of each schema and then suggest plausible mappings by exploring low-cost Steiner trees that connect those nodes in the CM graph that have attributes participating in element correspondences. Known: Table 1, CM graph 1, marked nodes (nodes in CM1 corresponding to columns), s-tree1: a semantic tree that expresses the correct semantic of Table1 by connecting its marked nodes in CM1 Known: Table 2, CM graph 2, marked nodes (nodes in CM2 corresponding to columns), s-tree2: a semantic tree that expresses the correct semantic of Table2 by connecting its marked nodes in CM2 Goal: Find a mapping from Table1 to Table2 (a subgraph of CM1, called CSG1, to a subgraph of CM2, called CSG2 Method: If CSG2 is known (e.g., it is the s-tree2), find the Steiner tree in CM1 connecting marked nodes, preferring the edges from s-tree1 Strong assumption: we know semantic of each table (s-trees) Use-case example: Table 1 has 10 columns with a large s-tree, and table 2 has only 3 columns. This approach finds a minimal tree in CM1 (maximum overlap with s-tree1) that connects the marked nodes of table1 corresponding to the marked nodes of table2. ======================================================================================= Schema matching Finds correspondence between elements of the source and target schemas Example: iMAP [Dhamankar et al., 2004] Schema mapping Generate declarative mappings expressible as queries in SQL or Datalog Example: Clio [Fagin et al., 2009] Semantic annotation of Web services Languages: SAWSDL [Farrell and Lausen, 2007] Tools: SWEET [Maleshkova et al., 2009] Annotate input and output parameters [Heß et al., 2003] [Lerman et al., 2006] [Saquicela e al., 2011]