Leveraging Existing Instrumentation to Automatically Infer Invariant-Constrained Models
Leveraging Linked Data to Infer Semantic Relations within Structured Sources
-
Upload
mohsen-taheriyan -
Category
Data & Analytics
-
view
545 -
download
0
Transcript of Leveraging Linked Data to Infer Semantic Relations within Structured Sources
![Page 1: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/1.jpg)
Leveraging Linked Data to Infer Semantic Relations within
Structured Sources
Mohsen Taheriyan Craig A. Knoblock
Pedro Szekely Jose Luis Ambite
Yinyi Chen
![Page 2: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/2.jpg)
Problem: How to map structured data to
a domain ontology?
![Page 3: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/3.jpg)
4
Semantic ModelMap the source to the classes & properties in an
ontologytitle date name
1 The Island 2009 Walton Ford
2 Excavation at Night 1908 George Wesley Bellows
3 Rose Garden 1901 Maria Oakey DewingSour
ceDo
mai
n On
tolo
gy
CIDOC-CRM 85 classes297 properties
![Page 4: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/4.jpg)
Semantic Types
E35_Title E82_Actor_Appellation
rdfs:label rdfs:label
5
E52_Time-Span
title date name1 The Island 2009 Walton Ford
2 Excavation at Night 1908 George Wesley Bellows
3 Rose Garden 1901 Maria Oakey Dewing
P82_at_some_time_within
![Page 5: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/5.jpg)
Relationships
E35_Title E82_Actor_Appellation
rdfs:label rdfs:label
6
E52_Time-Span
title date name1 The Island 2009 Walton Ford
2 Excavation at Night 1908 George Wesley Bellows
3 Rose Garden 1901 Maria Oakey Dewing
P82_at_some_time_within
E22_Man-Made_Object
E12_Production E21_Person
P102_has_title
P108_was_produced_by
P4_has_time-span
P14_carried_out_by
P131_is_identified_by
![Page 6: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/6.jpg)
7
Idea
• There is a huge amount of linked data available in many domains (RDF format)
• Use LOD as the background knowledge
• Exploit the relationships between instances
![Page 7: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/7.jpg)
8
Approach
Extract graph patterns from the linked data
• Target source (S)• Domain Ontologies (O)• Semantic labels of S• Linked Data (in the same domain)
Construct a graph from LOD patterns and the ontologyGenerate and rank semantic models
1
2
3
InputA ranked set of semantic models for S
Output
![Page 8: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/8.jpg)
9
Approach
Extract graph patterns from the linked data
• Target source (S)• Domain Ontologies (O)• Semantic labels of S• Linked Data (in the same domain)
Construct a graph from LOD patterns and the ontologyGenerate and rank semantic models
1
2
3
InputA ranked set of semantic models for S
Output
![Page 9: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/9.jpg)
10
LOD Patterns../person-
institution/57551 E21_Personrdf:type
Thomas Burgonskos:prefLabel
../person-institution/57551/birthP98i_was_born
../person-institution/57551/birth/date
P4_has_time-span
E67_Birthrdf:type
1787rdfs:label
E52_Time-Span
rdf:type
LOD fragment from the British Museum
![Page 10: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/10.jpg)
11
LOD Patterns
E67_BirthE21_Person
P98i_was_born
../person-institution/57551 E21_Person
rdf:type
Thomas Burgonskos:prefLabel
../person-institution/57551/birthP98i_was_born
../person-institution/57551/birth/date
P4_has_time-span
E67_Birthrdf:type
1787rdfs:label
E52_Time-Span
P4_has_time-span
E52_Time-Span
rdf:type
LOD fragment from the British Museum
Pattern
![Page 11: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/11.jpg)
12
Pattern Templates
• Many possible templates for patterns– Example: patterns for classes C1, C2, C3
• Consider only tree patterns• Limit the length of the patterns
![Page 12: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/12.jpg)
13
Extracting Patterns
• Use SPARQL to query RDF data• Example: patterns with length 1SELECT DISTINCT ?c1 ?p ?c2 (COUNT(*) as ?count) WHERE {
?x ?p ?y. ?x rdf:type ?c1.?y rdf:type ?c2. FILTER (?x != ?y).}
GROUP BY ?c1 ?p ?c2 ORDER BY DESC(?count);
![Page 13: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/13.jpg)
14
Approach
Extract graph patterns from the linked data
• Target source (S)• Domain Ontologies (O)• Semantic labels of S• Linked Data (in the same domain)
Construct a graph from LOD patterns and the ontologyGenerate and rank semantic models
1
2
3
InputA ranked set of semantic models for S
Output
![Page 14: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/14.jpg)
15
Merge the Patterns into a Graph
E12_ProductionE53_Title
P108i_was_produced_by
E52_Time-Span
E82_Actor_Appellation
E22_Man-Made_Object
E21_Person
P102_has_title
P14_carried_out_by P131_is_identified_by
E67_Birth
P98i_was_born
P4_has_time-span
P4_has_time-span
Links are weighted: less weight for more frequent linksLinks have tags: the identifier of the patterns containing the link
![Page 15: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/15.jpg)
16
E12_ProductionE53_Title
P108i_was_produced_by
E52_Time-Span
E82_Actor_Appellation
E22_Man-Made_Object
E21_Person
P102_has_title
P14_carried_out_by P131_is_identified_by
E67_Birth
P98i_was_born
P4_has_time-span
E39_Actor
P1_is_identified_by
P1_is_identified_by
P98i_was_born
P14_carried_out_by
P4_has_time-span
Add the paths from the Ontology
The links added from the patterns have much less weight compared to the links added from the ontology
![Page 16: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/16.jpg)
17
Approach
Extract graph patterns from the linked data
• Target source (S)• Domain Ontologies (O)• Semantic labels of S• Linked Data (in the same domain)
Construct a graph from LOD patterns and the ontologyGenerate and rank semantic models
1
2
3
InputA ranked set of semantic models for S
Output
![Page 17: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/17.jpg)
18
Map Semantic Labels to the Graph
E12_ProductionE53_Title
P108i_was_produced_by
E52_Time-Span
E82_Actor_Appellation
E22_Man-Made_Object
E21_Person
P102_has_title
P14_carried_out_by P131_is_identified_by
E67_Birth
P98i_was_born
P4_has_time-span
E39_Actor
P1_is_identified_by
P1_is_identified_by
P98i_was_born
P14_carried_out_by
P4_has_time-span
![Page 18: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/18.jpg)
19
Map Semantic Labels to the Graph
E12_ProductionE53_Title
P108i_was_produced_by
E52_Time-Span
E82_Actor_Appellation
E22_Man-Made_Object
E21_Person
P102_has_title
P14_carried_out_by P131_is_identified_by
E67_Birth
P98i_was_born
P4_has_time-span
E39_Actor
P1_is_identified_by
P1_is_identified_by
P98i_was_born
P14_carried_out_by
P4_has_time-span
![Page 19: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/19.jpg)
20
Generate and Rank Semantic Models
• Compute Steiner tree for the mapping– A minimal tree connecting nodes of
mapping– A customization of BANKS algorithm
[Bhalotia et al., 2002]• Our algorithm considers both
coherence and popularity• Each tree is a candidate model• Rank the models based on coherence
and cost
![Page 20: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/20.jpg)
21
What Is Coherence?
Place
Person
organizer
Event
location
p1 p2 p3
Place
Person
bornIn
Place
isPartOf
PlacePlace
bornIn
Person
diedIn
Patte
rns
![Page 21: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/21.jpg)
22
What Is Coherence?
Place
Person
organizer
Event
location
p1 p2 p3
Place
Person
bornIn
Place
isPartOf
PlacePlace
bornIn
Person
diedIn
PlacePerson
organizer
Event
location1 1
bornIn
0.5
p2, p3
p1p1Place
isPartOfp21
1diedInp3
Patte
rns
Grap
h
![Page 22: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/22.jpg)
23
What Is Coherence?
Place
Person
organizer
Event
location
p1 p2 p3
Place
Person
bornIn
Place
isPartOf
PlacePlace
bornIn
Person
diedIn
Patte
rns
Grap
h
Labe
ls PersonPlaceEvent
PlacePerson
organizer
Event
location1 1
bornIn
0.5
p2, p3
p1p1Place
isPartOfp21
1diedInp3
![Page 23: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/23.jpg)
24
What Is Coherence?
Place
Person
organizer
Event
location
p1 p2 p3
Place
Person
bornIn
Place
isPartOf
PlacePlace
bornIn
Person
diedIn
Patte
rns
Grap
h
Labe
ls PersonPlaceEvent
PlacePerson
organizer
Event
location1 1
bornIn
0.5
p2, p3
p1p1Place
isPartOfp21
1diedInp3
Stei
ner
Tree
s Place
Person
organizer
Event
bornIn
p1
p2, p3
Place
Person
location
Event
bornIn
p1
p2, p3
Place
Person
organizer
Event
p1location
p1
Not minimal model
but more coherent
![Page 24: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/24.jpg)
25
Steiner Tree
E12_ProductionE53_Title
P108i_was_produced_by
E52_Time-Span
E82_Actor_Appellation
E22_Man-Made_Object
E21_Person
P102_has_title
P14_carried_out_by P131_is_identified_by
E67_Birth
P98i_was_born
P4_has_time-span
E39_Actor
P1_is_identified_by
P1_is_identified_by
P98i_was_born
P14_carried_out_by
P4_has_time-span
![Page 25: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/25.jpg)
Evaluation
• Correct semantic types given• Linked data: 3,398,350 triples published by Smithsonian
American Art Museum• Extracted patterns of length 1 and 2• Compute precision and recall between learned links and
correct links26
Evaluation Dataset# sources 29# classes in the ontologies 147# properties in the ontologies 409# nodes in the gold standard models 812# links in the gold standard models 785
![Page 26: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/26.jpg)
27
Example
Person
Artwork
location
Museum
creator
correct model
Person
Museum
location
Artwork
founder
learned model
<Artwork,location,Museum><Artwork,creator,Person>
<Museum,founder,Person><Artwork,location,Museum>
Precision: 0.5Recall: 0.5
![Page 27: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/27.jpg)
28
Gold Standard Models - Example 1
![Page 28: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/28.jpg)
29
Gold Standard Models - Example 2
![Page 29: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/29.jpg)
30
Results
background knowledge precision recall time
(s)
domain ontology 0.07 0.05 0.17
domain ontology + patterns of length 1 0.65 0.55 0.75domain ontology + patterns of length 1 and 2 0.78 0.70 0.46
![Page 30: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/30.jpg)
31
Related Work• Mapping databases and spreadsheets to ontologies
– Mapping languages and tools (D2R, R2RML)– String similarity between column names and ontology terms
• 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
• Exploit Linked Open Data (LOD)– Link the values to the entities in LOD to find the types of the
values and their relationships
• Learn semantic models of structured data sources from previously modeled sources– Learn from the popular and coherent patterns in known
semantic models
![Page 31: Leveraging Linked Data to Infer Semantic Relations within Structured Sources](https://reader036.fdocuments.net/reader036/viewer/2022062310/58758e531a28ab901c8b63ed/html5/thumbnails/31.jpg)
32
Discussion & Future Work
• Automatically Infer semantic relations from LOD
• Help to publish consistent RDF data
• Extract longer patterns from LOD