=Paper=
{{Paper
|id=Vol-2180/paper-15
|storemode=property
|title=Inference of Latent Shape Expressions Associated to DBpedia Ontology
|pdfUrl=https://ceur-ws.org/Vol-2180/paper-15.pdf
|volume=Vol-2180
|authors=Daniel Fernández-Álvarez,Herminio García-González,Johannes Frey,Sebastian Hellmann,José Emilio Labra Gayo
|dblpUrl=https://dblp.org/rec/conf/semweb/Fernandez-Alvarez18
}}
==Inference of Latent Shape Expressions Associated to DBpedia Ontology==
https://ceur-ws.org/Vol-2180/paper-15.pdf
Inference of Latent Shape Expressions
Associated to DBpedia Ontology
Daniel Fernández-Álvarez1 , Herminio Garcı́a-González1 , Johannes Frey2 ,
Sebastian Hellmann2 , and José Emilio Labra Gayo1
1
Department of Computer Science, University of Oviedo,
Oviedo 33007, Spain
{danifdezalvarez,herminiogg}@gmail.com, labra@uniovi.es
2
Agile Knowledge Engineering and Semantic Web, University of Leipzig,
04109 Leipzig, Germany
{frey,hellmann}@informatik.uni-leipzig.de
Abstract. In order to perform any operation in an RDF graph, it is rec-
ommendable to know the expected topology of the targeted information.
Some technologies have been developed in the last years to describe the
expected shapes in an RDF graph, such as ShEx or SHACL. In general,
a domain expert can define the expected shapes in a graph, but there are
some scenarios in which the schema cannot be predicted a priori, but it
emerges at the same time that the graph is filled with new information
(the shapes are latent). We have developed a prototype which is able to
infer shapes of classes in a knowledge graph and used it with classes of
DBpedia ontology. We serialize our results using ShEx.
Keywords: RDF · ShEx · Inference · DBpedia · Knowledge Graph ·
Schema
1 Introduction
The most common way to perform queries against any Resource Description
Framework (RDF) store is using SPARQL. In order to perform an effective
SPARQL query against a Knowledge Graph (KG), one may need to know the
expected topology of the KG. A wrong pick of properties, data-types or classes
may cause a certain query to ignore relevant information or to update data in a
way that does not fit with the current KG’s topology.
Ontologies define the meaning and the correct usage of properties and classes,
but they are not intended to specify the expected shape of a group of nodes in
the context of a specific KG. In the RDF world, Shape Expressions (ShEx) [6]
and Shapes Constraint Language (SHACL)[4] have been proposed for describing
and validating RDF.
Usually, the topology of a KG can be designed or predicted by domain experts
in controlled scenarios. However, there are situations in which a KG does not
have a planned schema, but the shapes emerge while the content keeps growing.
Insightful examples of that are community-driven approaches such as DBpedia
or Wikidata. In those cases, as suggested in [2], discovering the latent schemata
associated to classes by applying inference can be useful in several ways:
– Guideline for users. Knowing the shape associated to a class is helpful to
build queries about their instances.
– Measure of data quality. The process of inference may produce shapes
with different levels of trustworthiness w.r.t how homogeneously the knowl-
edge is represented. That trustworthiness may be used as a data quality
measure or as an input for some methods of error detection [5], especially
w.r.t mistypings or absence of typings in Assertion Box (A-Box) terms.
We implemented a prototype which is able to infer Shape Expressions asso-
ciated to the classes in a KG and applied it on the English chapter of DBpedia3 .
Our prototype calculates a score of how trustworthy the constraints inferred
in the shapes are, i.e., how many of the total of instances really conform to it.
Then, it serializes the results using ShEx. Some other works have already studied
emergent schemata in RDF sources[3] and serialization or visualization of this in-
formation[1]. The novelty of our approach consists of the usage of ShEx. Despite
SHACL is a W3C recommendation, at this stage we have chosen to work with
ShEx instead of SHACL because ShEx presents a more concise human-readable
syntax. Nevertheless, we are presenting a work in progress. Future versions of
our prototype will support both technologies.
2 Shape Inference
Our prototype receives as input a list of class URIs from a KG and infers a shape
for each class. It works with the following workflow: 1) Find all the instances of
the target classes; 2) For each class, find all the triples whose subject is one of
its instances and use them all to build a profile of the class, consisting of a list
of triple constraints4 ; and 3) turn each profile into a ShEx. Some configuration
can be provided to filter some tripe constraints from the results.
Our prototype has a linear complexity related to the total number of triples
of the target classes’ instances. However, the underlying algorithm can be paral-
lelized or even adapted to a server process, triggering some changes in a limited
number of schemata when there is some update.
Listing 1.1 shows an example of a small RDF graph about countries, and
Listing 1.2 presents the shape that our prototype infers from it5 . Every triple
constraint induced in a shape is associated with a percentage that indicates how
many instances of the target class conform with the constraint. The common case
3
The used source code as well as an extended explanation of our experiments is
available at https://github.com/DaniFdezAlvarez/dbpedia-shexer
4
Triple constraints are the basic building block in ShEx. They are composed of a
property, a node constraint and a cardinality.
5
The prefixes employed in this paper are the common ones that can be resolved by
the service http://prefix.cc/
in real scenarios is that not all of the instances conform with a given constraint
rule, with the exception of the constraint rdf:type [:nameOfTheClass], which they
all share.
Listing 1.1. RDF example graph Listing 1.2. Example Country Shape
dbr : Spain rdf : type dbo : Country ;
dbp : capital dbr : Madrid ; : Country
rdfs : label " Spain " ; {
rdfs : label " Kingdom of Spain " . rdf : type [ dbo : Country ] ; # 100%
dbp : capital IRI ; # 100%
dbr : France rdf : type dbo : Country ; rdfs : label xsd : string + # 100%
dbp : capital dbr : Paris ; # 50% have cardinality {1}
rdfs : label " France " . }
Listing 1.3 shows an example of Country Shape inferred by analyzing the
actual content of DBpedia.
Listing 1.3. Country Shape (trustworthiness of 80%)
: Country {
r d f : type [ dbo : Country ] ; # 100.0 %
dbo : wikiPageID xsd : i n t e g e r ; # 97.108 %
owl : sameAs IRI +; # 96.933 %
f o a f : name xsd : s t r i n g +; # 96.758 %
dcterms : s u b j e c t IRI +; # 96.028 %
dbo : d i s s o l u t i o n Y e a r xsd : gYear +; # 83.148 %
# 8 2 . 5 9 3 % have c a r d i n a l i t y {1}
dbo : f o u n d i n g Y e a r xsd : gYear +; # 82.009 %
# 8 1 . 4 5 4 % have c a r d i n a l i t y {1}
dbp : c o n t i n e n t rdf : langString + # 80.607 %
# 8 0 . 3 4 4 % have c a r d i n a l i t y {1}
}
The main features of our prototype are the following:
Trustworthiness score. Every triple constraint inferred is associated with the
relative amount of instances that fit with it. We provide that information in a
comment. That allows for sorting the constraint w.r.t. its trustworthiness, as well
as filtering constraints that are not frequent enough. The threshold to accept or
reject a constraint w.r.t how trustworthy it is can be configured.
Literals and IRIs recognition. All kinds of literals are recognized and treated
separately when inferring the constraints. In case a literal is not explicitly asso-
ciated with a type in the original KG, xsd:string is assumed. If the object of a
triple is an IRI, the macro IRI is used to represent it in the inferred constraints.
Special treatment of rdf:type. The only exception to the previous feature hap-
pens when analyzing triples whose predicate is rdf:type. In those cases, we create
a triple constraint whose object is a value set containing a single element, which
is the actual object of the original triple. We introduced this exception just for
rdf:type because we consider that the information related to typing is specially
relevant for the context. Two classes with a high number of overlapped instances
may have also a highly overlapped schema. Future versions of this prototype will
allow to customize which properties point to value sets.
Cardinality management. Some of the triples of a given instance may fit in an
infinite number of constraint triples with the same predicate and object but
different cardinalities. For example, if an instance has a single label, that makes
it fit with infinite triple constraints of the form {rdfs:label xsd:string C}, where
C can be {1}, +, {1,2}, {1,3},... At this stage, our prototype considers rules with
exact cardinality or + closure. We avoid using rules with * closure because every
triple constraint with that cardinality will match with 100% of the instances, not
mattering the predicate or the object of the constraint.
When serializing the shapes, our prototype can be configured to prioritize the
least specific cardinality or the most specific one if its trustworthiness is high
enough. Information about cardinality which is not given in the constraint itself
is provided through comments.
3 Conclusions and Future Work
We have applied automatic inference over DBpedia to discover latent Shapes
associated to classes of the DBpedia ontology using a statistical approach. We
have serialized the latent shapes using ShEx, which can be useful as a guideline on
how to manipulate the data. Our approach associates a score of trustworthiness
to each rule, so it can also be used as a metric of homogeneity of the dataset.
We are presenting a work in progress research. The algorithm underlying
our prototype can be extended with extra features, including more complex
inferences, such as inter-shape referencing or more precise cardinalities; regular
expressions for some literals; or generation of serializations different to ShEx,
such as SHACL or example SPARQL queries associated to each class.
Acknowledgments. This work is partially funded by the Spanish Ministry of
Economy and Competitiveness (Society challenges: TIN2017-88877-R)
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