1. Inference and Serialization
of Latent Graph Schemata
Using Shex
Speaker: Daniel Fernández-Álvarez
Category: Idea
Daniel Fernández-Álvarez* Jose Emilio Labra-Gayo* Herminio García-González*
danifdezalvarez@gmail.com labra@uniovi.es herminiogg@gmail.com
*Department of Computer Science
WESO Research Group
University of Oviedo
Oviedo, Spain

2. Motivational
example

3. Motivation: Torimbia Beach

4. Motivation: Torimbia Beach
• Country: Spain
• Region: Asturias
• Council/city: Llanes
• Lat/long: 43.44, -4.85
• Length: 500 m
• Width: 100 m
• Naturist: True

5. Motivation: Torimbia Beach
*Batu Ferringhi, Horseshoe Bay, Manly Beach, Marina Beach, Playa Arcadia, Red Beach
Region Lat/long Width
X
X
X
X
X
6 different random but relevant beaches in DBPedia*
The same happens with country, council/city, length and naturist

6. Motivation
I would like to…
check the concept of beach, not the instances
make a single query/click to discover usual schemata
be correct, coherent and exhaustive

7. Idea

8. Proposal
• Analysis of the neighborhood of nodes that fit in a certain condition
to induce usual schemata:
• Typical condition: rdf:type
• Serialization of inferred schemata with ShEx (Shape Expressions).
• Association to a type (class)
• Management of trustworthiness
• Handy for:
• Documentation
• Verification of quality
• Discovering “hidden” entities

9. How?

10. Workflow
ShEx
<Person> {
}
Source graph:
Dbpedia,
Wikidata…
Inference Serialization
Abstract
schemata
representation
Textual schemata
representation
with ShEx

11. Schemata Inference: current approaches
• Ontology integration to find shared core elements [Zhao,13]
• Association rule mining (Apriori)
• Rule-based classification (Decision Tables)
• Logical axioms at ontology level [Völker,11]
• Association rule mining (Apriori)
• Axioms represented with OWL 2 EL
• Graph schemata al class level[Christodoulou,15]
• Clusters of similar individuals (ideally, cluster=class).
• Results in an ad-hoc syntax.

12. Schemata Inference: our current status
Some promising ideas:
Instance clustering
Association rule mining
Some issues linked to the target graph:
Noise management
Adaptation to data model
Graph size & complexity
Completeness and coherence

13. Schemata Serialization I
Need: Standard syntax to express constraints in RDF graphs at class
level:
• XML: RelaxNG, DTD, Xml Schema
• Relational databases: DDL
• Json: Json Schema
RDF candidates:
ShEx
Grammar-oriented
Recursion
Human-friendly syntax
SHACL
Constraint-oriented
No recursion (by now)
RDF syntax (by now)

14. 19%
59%
83%
83%
87%
69%
32%
Schemata Serialization II
Pure ShEx
<Beach> {
dbp:width xsd:integer,
dbp:length xsd:integer,
geo:lat xsd:long,
geo:long xsd:long,
dbo:isPartOf @<Place>*
}
Anotated ShEx
<Beach> {
dbp:width xsd:integer,
dbp:length xsd:integer,
geo:lat xsd:long,
geo:long xsd:long,
geo:geometry @<Point>,
dbo:isPartOf @<Place>*,
dbo:country @<Country>
}

15. Use cases?

16. Context: Types of graphs
Specific purpose
Automatically built
Managed by a single agent
General purpose
Manually built
Managed by community
Reality

17. Context: Collaborative graphs
Key points:
• Schemata are not planned, they just emerge
• Schemata change in time
Posibilities:
• Schemata inference on users’ demand
• What is associated to a type, instead of how a type should be
• Freedom: ShEx as guide, not dogma

18. To summarize…

19. Conclusions and Future Work
What we have done:
Idea
Inference of Latent Graph Schemata
Serialization through ShEx syntax
What we want to do:
Prototype
Selection of techniques
Selection of target source/s
Tests
Usefulness in different domains
Feasibility: reached trustworthiness
User’s acceptance

20. References
• Zhao, L., & Ichise, R. (2013, May). Instance-based ontological
knowledge acquisition. In Extended Semantic Web Conference (pp.
155-169). Springer Berlin Heidelberg.
• [2] Völker, J., & Niepert, M. (2011, May). Statistical schema induction.
In Extended Semantic Web Conference (pp. 124-138). Springer Berlin
Heidelberg.
• [3] Christodoulou, K., Paton, N. W., & Fernandes, A. A. (2015).
Structure inference for linked data sources using clustering.
In Transactions on Large-Scale Data-and Knowledge-Centered
Systems XIX (pp. 1-25). Springer Berlin Heidelberg.

21. Inference and Serialization
of Latent Graph Schemata
Using Shex
Speaker: Daniel Fernández-Álvarez
Category: Idea
Daniel Fernández-Álvarez* Jose Emilio Labra-Gayo* Herminio García-González*
danifdezalvarez@gmail.com labra@uniovi.es herminiogg@gmail.com
*Department of Computer Science
WESO Research Group
University of Oviedo
Oviedo, Spain

22. Extra information for Torimbia example I
Latlong* Naturist
Batu Ferringhi
dbp:latd, dbp:longd, georss:point,
geo:geometry, geo:lat, geo:long X
Horseshoe Bay geo:geometry, geo:lat, geo:long X
Manly Beach
georss:point, geo:geometry, geo:lat,
geo:long X
Marina Beach
georss:point, geo:geometry, geo:lat,
geo:long X
Playa Arcadia
georss:point, geo:geometry, geo:lat,
geo:long X
Red Beach
dbp:latDeg, dbp:longDeg, georss:point,
geo:geometry, geo:lat, geo:long X
*Some lat/long properties has been omitted. Some of them work togheter in order to
get a precise coordinate (total degrees + orientation N/S E/W)

23. Extra information for Torimbia example II
Lenght Width Council Region Country
Batu
Ferringhi X X shared entity dbo:isPartOf dbo:country
Horseshoe
Bay X X description description
rdf:type
(BeachesOfBer
muda)
Manly Beach X X description
dct:subject
dbc:Beaches_of_N
ew_South_Wales description
Marina
Beach dbp:height description dct:subject dct:subject
Playa ArcadiaX X dct:subject X dct:subject
Red Beach X dbp:width dbp:city is dbp:south of description

24. Wikimedia Strategy: Templates and Mappings
• Mappings
• Designed to automatically import data from Wikipedia’s infoboxes and tables
into DBpedia.
• Wikipedia Templates define expected properties for certain types. Mappings
define which property should be used to create a triple when finding an
occurrence of an expected property.
PROS
• Preserves Wikipedia’s quality.
• Handy as guide for content
represented in Wikipedia.
• It may enrich both Wikipedia and
DBpedia
• Templates can evolve guided by
community
CONS
• Depends on Wikipedia’s quality.
• It can only manage content
represented in Wikipedia.
• Non transposable to standalone RDF
graph projects.
• It assumes that the community is
following the templates. It may not
reflect the real graph.

25. ShEx vs SHACL
ShEx
<UserShape> {
dbp:label xsd:string,
ex:role ( ex:User ) ?
}
SHACL
:UserShape
a sh:Shape ;
sh:property [
sh:predicate rdfs:label ;
sh:datatype xsd:string ;
sh:minCount 1 ;
sh:maxCount 1 ;
] ;
sh:property [
sh:predicate ex:role ;
sh:hasValue ex:User ;
sh:filterShape [
sh:property [
sh:predicate ex:role ;
sh:minCount 1 ;
]
] ;
sh:maxCount 1 ; ] .