Semantic models of data sources describe the meaning of the data in terms of the concepts and relationships defined by a domain ontology. Building such models is an important step toward integrating data from different sources, where we need to provide the user with a unified view of underlying sources. In this paper, we present a scalable approach to automatically learn semantic models of a structured data source by exploiting the knowledge of previously modeled sources. Our evaluation shows that the approach generates expressive semantic models with minimal user input, and it is scalable to large ontologies and data sources with many attributes.

1. A Scalable Approach to 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 express the intended meaning of data?
Explicit semantics is missing in many of the
structured sources
creator? actor? rightsHolder?
artwork? movie? referenced entity?

3. Map the Source to 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
Key ingredient to automate source discovery, data integration,
and publishing RDF triples

5. 4
Problem:
How to automatically learn a
semantic model for a source

6. Main Idea
5
Sources in the same domain often have similar data
Exploit knowledge of known semantic models to
hypothesize a semantic model for a new sources

7. Previous Approach (ISWC 2013)
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 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
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 types for 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: Indianapolis Museum 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 data from 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 data from 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 data from 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 Attributes to 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 Attributes to 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 Attributes to 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
• Multiple mappings 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
New source: 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
New source: 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 data from 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. Steiner Tree
26

28. Evaluation
• Dataset
– 29 museum 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 approach was 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
• 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

32. Future Work
• Scalability regarding 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