The document presents a study on enhancing the quality of RDF knowledge graphs by repairing hidden links between entities represented as string literals and their corresponding IRIs. The authors propose a method utilizing ontological axioms and data profiling to identify and convert these string literals, resulting in improved graph connectivity with notable increases in metrics. The approach shows high recall and precision in detecting entity relations, suggesting substantial potential for future improvements in knowledge graph integrity.

1. REPAIRING HIDDEN LINKS
IN
LINKED DATA
ENHANCING THE QUALITY OF RDF KNOWLEDGE GRAPHS
Nandana Mihindukulasooriya, Mariano Rico, Idafen Santana-Pérez,
Raúl García-Castro and Asunción Gómez-Pérez
December 5th, 2017
The Ninth International Conference on Knowledge Capture
K-CAP 2018, Austin, Texas, United States

2. Things, not strings!
2

3. Things as strings!
3
dbr:Tim_Cook dbr:Apple_Pie
“Apple”
dbo:employer dbo:ingredient
• Introduce model inconsistencies
• Increases ambiguity
• Reduces connectivity

4. … after correctly linked
4
dbr:Tim_Cook dbr:Apple_Pie
dbr:Apple_Inc dbr:Apple
dbo:Company
dbr:Consumer
_electronics
dbr:IPhone
yago:Fruit
2.4
dbr:Rosacese
rdf:type rdf:typedbo:industry
dbo:family
dbp:fiber
dbo:product
dbo:employer dbo:ingredient

5. Is this a common problem?
• Web Data Commons (JSON-LD embedded data)
• http://webdatacommons.org/structureddata/
5
Property # of objects # of string
literals
Literal % IRI %
schema:creator 16,573,426 15,1782,874 95.23 4.77
schema:brand 4,694,411 2,420,908 51.57 48.43
schema:author 26,193,682 1,500,898 5.73 94.37
schema:
hiringOrganization
1,252,870 1,231,840 98.84 1.16
schema:publisher 31,317,151 560,577 1.79 98.21

6. Research Questions
• How to identify string literals that denote
entities in a KG?
• How to transform those string literals
into IRIs that correspond to entities they
denote?
• How to measure the improvement in
quality because of the transformation?
6

7. Entity relations
• Entity relations link two entities.
• All objects of entity relations should be entities.
• Not all relations are entity relations.
7
an entity relation
Person Company
Company
“Company Description”
a non-entity
relation

8. String to Entity IRI Transformation
Such string literals can be transformed to their
corresponding entity IRIs with high precision using both
ontological axioms and data profiling information.
8
“Apple”
dbr:Apple
dbr:Apple_Inc
dbr:Apple
_Bank
dbr:The_Apple_Film

9. Connectivity
Connectivity of a knowledge graph can be improved by
transforming literal nodes in entity relations into their
corresponding entity IRIs.
9

10. Related Problems - I
• Named entity disambiguation from text
10
Kill Bill was directed by Quentin Tarantino and stars Uma Thurman.
dbr:Kill_Bill:_Volume_1 dbr:Quentin_Tarantino dbr:Uma_Thurman

11. Related Problems - II
• Web Table Matching
11
company ind. loc.
IBM technology United
States
United airlines USA
… … …
dbr:United_States
dbr:Technology
dbr:IBM
dbr:United_Airlines
dbr:Airline
dbo:country

12. Approach
12
• Using ontological axioms
• Using data profiling information
Identification
of
entity relations
• Context generation
• Type identification (entity relation
range)
• Entity IRI identification
String literal to
IRI conversion

13. Identification of entity relations
13
Identification of
entity relations
RDF Graph Ontology Definitions
Entity
relations
Other
relations
P1 P2
P3 Pn
PX PY

14. Identification of entity relations
14
Identification of
entity relations
OWL Object Properties
Property range
Entity Relation Classifier
Features
IRI %, Lit%, DistinctIRI%,
DistinctLit%, String%, Num%,
Date%
Training Data
Known entity relations
Manually annotated rdfs props
Ontology-
driven
Data-
driven
see the paper for
the algorithm

15. Approach
15
• Using ontological axioms
• Using data profiling information
Identification
of
entity relations
•Type identification (entity relation
range)
•Context generation
•Entity disambiguation
String literal to
IRI conversion

16. Type identification and entity linking
16
employer
dbr:Google
dbr:Microsoft
dbr:Yahoo!
dbr: Amazon_(company)
“Apple”
“I.B.M”
“Johnson & Johnson”
“Mars”
“Oracle”
“Sun”
range information (ontology)
Type
Prediction
(range)
Type
restrictions
Type frequency
analysis
Entity linking
Entity
disambiguation

17. Context generation
17
Tim Cook employer Apple.
Timothy Donald "Tim" Cook is an American
business executive, industrial engineer and
developer. Cook is the current and seventh
Chief Executive Officer of Apple Inc.,
previously serving as the company's Chief
Operating Officer, under its ....
Apple Pie ingredient Apple.
An apple pie is a fruit pie, in which the
principal filling ingredient is apple. It is, on
occasion, served with whipped cream or ice
cream on top, or alongside cheddar cheese.
The pastry is generally used top-and-bottom
...

18. Evaluation
18

19. Evaluation - I
• Can the approach correctly identity entity relations?
• manually-annotated gold standard
• 3 annotators with high inter-annotator agreement
19
Class Entity
Relations
Detected relations Prec. Recall
Correct Incorrect
dbo:Athlete 183 178 53 77.06% 97.27%
dbo:SportsTeam 157 156 48 76.47% 99.36%
dbo:SportsEvent 116 115 41 73.71% 99.14%
Total 456 449 142 75.97% 98.47%
High Recall

20. Evaluation - II
• Can the approach correctly disambiguate and link the
identified strings?
• Manual verification of a random sample of 300
20
Class Sample
Size
Disambiguated Prec. Recall
Correct Incorrect
dbo:Athlete 100 51 50 98.04% 50%
dbo:SportsTeam 100 44 44 100% 44%
dbo:SportsEvent 100 58 55 94.83% 56%
Total 300 153 149 97.38% 49.67%
High Precision

21. Graph metrics
21
# of
edges
# of
components
Isolated
components
largest component
size
8 6 3 5
• connected components

22. Graph metrics
22
# of
edges
# of
components
Isolated
components
largest component
size
8 6 3 5
12 (+4) 2 (-4) 0 (-3) 12 (+7)
• connected components

23. Evaluation - III
• Does this transformation increase the connectivity of
the graph?
23
Graph edges components isolated Largest component
size % of
nodes
Original 828,310 119,623 112,331 168,128 54.28
Repaired 1,035,912 99,137 93,507 192,805 64.16
+207,602 -20,486 -18,824 +24,677 +9.88
+25.06% -17.12% -16.76% +14.68%
Improved Connectivity

24. Conclusions and future work
• A large number of entities are represented as strings
in Linked Data .
• The proposed approach can detect such strings with
high recall (98%) and covert them to their
corresponding entity IRIs with high precision (97%).
• We could add 25% more links and improved the
connectivity of a subgraph of DBpedia by 17%
• In future, improve the algorithm by using more
context information from the graph for the task.
24

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