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

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Motivation

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?

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Map the Source to the Domain Ontology

EDM: Europeana Data Model SKOS: Simple Knowledge Organization SystemFOAF: Friend of a FriendAAC: American Art CollaborativeElementsGr2: RDA Group 2 ElementsORE: Open Archive InitiativeDCTerms: 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

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Semantic Model

aac:CulturalHeritageObject edm:WebResou

rce

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

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Problem: How to automatically learn a semantic model for a source

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Main Idea

Sources in the same domain often have similar data

Exploit knowledge of known semantic models to hypothesize a semantic model for a new sources

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Previous Approach (ISWC 2013)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

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Output• A ranked set of semantic models for

S

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LimitationsInput

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

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ContributionsInput

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

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Example

New source: Indianapolis Museum of Art

EDM

SKOS

FOAF

AAC

ElementsGr2

ORE

DCTerms

Domain ontologies:

S1(title, creationDate, name, birthDate, deathDate, type)

Known Semantic Models:S1: Dallas MuseumS2: 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

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• Sample data from new source (S)

ApproachInput

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

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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

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

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• Sample data from new source (S)

ApproachInput

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

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Build Graph G: Add Known Models

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Build Graph G: Add Semantic Types

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Build Graph G: Expand with Paths from Ontologies

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• Sample data from new source (S)

ApproachInput

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

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Map Source Attributes to the GraphNew 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>

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Map Source Attributes to the GraphNew 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>

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Map Source Attributes to the GraphNew 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>

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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

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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

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Score the MappingsNew 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 = 0.44Confidence: 0.56Score: 0.5

Example Mapping 2

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Score the MappingsNew 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 = 0.66Confidence: 0.55Score: 0.605

Example Mapping 1

This mapping gets higher score even though it uses the 2nd ranked semantic type for artist

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• Sample data from new source (S)

ApproachInput

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

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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

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Steiner Tree

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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

How many of the learned relationships are correct?

How many of the correct relationships are learned?

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Quality

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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 280.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

precision (k=1)

recall (k=1)

precision (k=4)

recall (k=4)

precision (correct types)

recall (correct types)

Number of known semantic models

Performance

The previous approach was not able to learn semantic models for sources with more than 4 attributes in the

timeout of 1 hourExample: S16 with only 5 attributes 16,633,298 mappings (29*29*29*31*22)

0 5 10 15 20 25 300

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Previous Approach

New Approach

Number of Attributes

Time

(Kbeam = 100)

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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

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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