=Paper=
{{Paper
|id=Vol-2181/paper-01
|storemode=property
|title=Towards a Practical Implementation of Contextual Reasoning on the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-2181/paper-01.pdf
|volume=Vol-2181
|authors=Sahar Aljalbout
|dblpUrl=https://dblp.org/rec/conf/semweb/Aljalbout18
}}
==Towards a Practical Implementation of Contextual Reasoning on the Semantic Web==
https://ceur-ws.org/Vol-2181/paper-01.pdf
Towards a Practical Implementation of
Contextual Reasoning on the Semantic Web
Sahar Aljalbout
Centre Universitaire d’informatique, University of Geneva, Switzerland
sahar.aljalbout@unige.ch
Abstract. Contextual knowledge representation and reasoning is an old
issue in the semantic web. Despite the fact that context representation
has for a long time been treated locally by many semantic web practi-
tioners, a recognized and widely accepted consensus regarding the precise
ways of encoding and even more reasoning on contextual knowledge has
not yet been reached by far. In this dissertation, we introduce an ap-
proach to represent and reason over contextual knowledge in RDF, while
committing to a formally defined semantics of a contextual description
logic. Our key contribution is the definition of a formally solid contex-
tual model (not only for contextual knowledge representation but also
for contextual reasoning) which is practically applicable using existing
semantic web languages and tools.
Keywords: contextual reasoning, contextual OWL, contexts. . .
1 Problem statement
The problem of representing and reasoning on contextual knowledge is a recog-
nized, and open issue in the semantic web. Many data providers and semantic
web practitioners have attempted local approaches for treating contexts repre-
sentation; however, there is, so far, no consensus regarding the precise ways of
encoding, and much less reasoning, on contextual knowledge. Nevertheless, the
representation of contexts in the semantic web has been considered separately
as, first, a data problem, giving rise to several proposals to encode contexts into
RDF [18][10][20][1]. And second, as a theoretical problem where several attempts
to include contexts in description logics have emerged [3][14]. However, contrary
to the field of context representation where research is abundant, reasoning with
contextual knowledge has been notably less explored.
This PhD proposal aims to strengthen the links between the theoretical and
practical communities working on diverse aspects of representing and reasoning
with contextual knowledge; which could hopefully lead one day to some forms of
standardization or at least good practice guidelines accepted by the community.
We propose an approach to represent and reason over contextual knowledge in
RDF while committing to a formally defined semantics of a contextual descrip-
tion logic. The key idea behind this approach is the definition of a formally
solid contextual model but also practically applicable to data while using exist-
ing semantic web languages and tools. Throughout this work, we have adopted
McCarthy’s theory of contexts [17], primarily because this theory offers an in-
strumental view on contexts, where contexts are considered as formal objects,
describable in first-order languages.
The preliminary results of this dissertation are the following:
– A survey of logical and practical models to encode contexts on the semantic
web.
– A contextual extension of the web ontology language, that we called OWLC .
– A contextual profile of the web ontology language inspired from OWL-RL,
that we called OWL-RLC , with contextual entailment rules for reasoning,
not only on contextual statements, but also on contexts.
– A study of the practical implementation of the contextual reasoning.
2 Relevancy
The goal of the semantic web (GSW) is to promote machine-understandability of
the information published on the web. For instance, if a dataset is published on
the web by an arbitrary agent, it should ideally lend itself correctly interpretable
by any other agent accessing it independently. However, in many situations,
information cannot be fully understood without making explicit assumptions
about the context in which it is stated. Therefore, if the web of data is not ac-
companied by a clear standard for the representation of contextual information,
the goal of machine understandability can never be fully achieved.
Succeeding in positioning our proposed approach will benefit many stake-
holders in the semantic web community. We are introducing a method to bridge
the gap between the theoretical and practical communities. If proven successful,
we will contribute to advance the field towards achieving the GSW as described
above.
3 Related works
In 1969, McCarthy [17] proposed a theory of contexts which consists of three
major postulates: 1) Contexts as formal objects. 2) Contexts having properties
3) Contexts organized in relational structures. Then, in 1993, F.Guinchiglia [11]
discussed the concept of contextual reasoning considering reasoning always local
to a subset of the known facts. More recent research can be divided in two
groups: theoretical and practical.
In the theoretical group, in 2001, [9] introduced the idea of locality and com-
patibility where reasoning is considered mainly local and uses only part of what
is potentially available. Compatibility is argued to be used among the reasoning
performed in different contexts. In 2003, [5] introduced the concept of distributed
description logics. The authors consider that there are binary relations that de-
scribe the correspondences. An advantage of DDLs is its support for multiple
ontologies. However, the coordination between a pair of ontologies can only hap-
pen with the use of bridge rules. In 2004, a new concept called E-connections
[15] emerged: ontologies are interconnected by defining new links between indi-
viduals belonging to distinct ontologies. One major disadvantage is that it does
not allow concepts to be subsumed by concepts of another ontology, which limits
the expressiveness of the language. Then, in 2006, [3] attempted to extend de-
scription logics with new constructs with relative success. In 2011, a proposition
was argued to use a two dimensional- description logics with a context language
supporting context descriptions and an object language equipped with context
operators for representing object knowledge relative to contexts. Results showed
that this approach does not necessarily increase the computational complexity of
reasoning. In 2012, [6] argues that treating contexts in the semantic web needs
more advanced means, such that contexts should be explicitly presented and
logically treated...
In the practical group, many attempts to find a solution to the syntactic
restriction of RDF binary relations emerged, because an RDF property holding
for a specific context is a relation involving three resources (a subject, an object,
and a context). Three types of works were proposed:
(a) Extending the data model: the triple data structure is extended by adding
a fourth element to each triple, which is intended to express the context [7]
of a set of triples.
(b) Extending the semantic of RDF: In 2014, RDF* [12] was proposed. The idea
is to extend the RDF data model with a notion of nested triples. Another
approach is Singleton property [18] which recommends the creation of a
special instance for every triple predicate for which we want to provide the
context. A drawback of the singleton properties proposal is that it introduces
a large number of unique predicates.
(c) Using design patterns: Pat Hayes [13] presents ways to attach temporal in-
dexing to sentences of the form R(a,b). It could be categorized along three
axis: 3D, 3D+1, 4D. We extend this categorization to many dimensions of
contexts and use it to classify contextual patterns.
– 3D representation: the contextual index co is attached to the sentence
R(a,b) and thus R(a,b) holds for co such as RDF reification [4]. The
major drawback of this method is that it is supported in DL reasoning.
– 3D+1 representation: the contextual index co is attached to the relation
R(a,b,co). An example of this representation is situation pattern [8]. One
advantage is being able to talk about assertions as (reifying) individuals,
but the disadvantage is being unable to use them as properties. A second
example is FluentRelations [1] [2]. Two advantages of this method are
1) considers contexts as objects; 2) don’t cause objects proliferation.
– 4D representation: the contextual index co is attached to the object terms
R(a@co, b@co) where co is the contextual-slice of the thing named. The
first example is context slices [19]. This pattern introduces new entities:
the contextual projections and assignments of the individuals as well as
a context index that takes the indexes. Another example is Ndfluents
which is an extension of the 4dfuents model [20] for the temporal di-
mension to a generic contextual model. The drawback of this method is
that it introduces many contextualized individuals which causes objects
proliferation.
4 Research questions and hypotheses
We have formulated the following research questions:
Q1 What are the practical requirements of contextual reasoning that are not
fulfilled by the existing approaches and are essential for accomplishing a commu-
nity consensus?
Q2 Can we significantly extend the web ontology language with contexts with-
out increasing the computational complexity of the language? Is it possible to
implement contextual reasoning using the existing reasoners?
Q3 To what extent linking the contexts by means of semantic relations1 can
enhance the expressiveness of the contextual language and push forward the dis-
covery of hidden knowledge? And what are the drawbacks in terms of computa-
tional complexity?
Q4 What is the cost of the transformation of existing knowledge graphs to
adhere to the proposed model? And how can we identify hidden contextual knowl-
edge?
Our hypotheses derive directly from the research questions:
H1 There are many ways to encode contexts on the graph level, yet we be-
lieve this is not enough to provide the semantic web with a contextualized state
provided that reasoning on contexts is still an open problem. We hypothesize
that adopting a two language approach with an object language and a context
language will reduce the reasoning cost on the semantic web.
H2 The use of design patterns to encode the notion of contexts is more realistic
then extending the RDF data model in the semantic web community. Although
there is no best design pattern, each one is suitable for a specific target and
dimension of contexts.
1
Temporal contexts can be linked using Allens interval algebra (https :
//en.wikipedia.org/wiki/Allen%27si ntervala lgebra), spatial contexts with RCC8
(https : //en.wikipedia.org/wiki/Regionc onnectionc alculus) etc.
5 Approach
In order to achieve a formally solid contextual model, but also practically appli-
cable to linked data, we proceed as follows:
– To begin, we provided a contextual extension of the web ontology language
and we called it OWLC . This extension is based on a two-dimensional de-
scription logic [14] with one language for the representation of contexts-
dependent concepts, roles, or axioms; and, a second language for the repre-
sentation of contexts and their relations. The reasons behind this choice are:
first, there is no additional cost in the complexity of reasoning2 , and second,
the approach was designed to be applied to several practical scenarios.
– Then, we plan to define a generic upper vocabulary for describing contextual
metadata and meaningful relations between contexts. If this vocabulary is
adopted by the community, it can facilitate data interoperability.
– Additionally, we adapted the OWL-RL profile to OWLC and we called it
OWL-RLC . The latter contains new contexts-dependent rules and novel rules
for handling the new constructs.
– At this point, we must choose an adequate reasoning approach to validate our
contextual model. We have preliminary results utilizing SPARQL inferencing
notation (SPIN); however, we intend to apply other choices.
– We also propose to apply the entailments rules on different graphs imple-
menting a variety of design patterns; by doing that, we aim to identify pos-
sible effects that design patterns have on the reasoning.
– Finally, we propose to test the complete model on different types of knowl-
edge graphs among them Wikidata where qualifiers and references are at-
tached to every statement.
6 Preliminary results
A contextual web ontology language OWLC . It is based on a two-dimensional
description logic [14] that includes a core and a context vocabulary. A contextual
interpretation is a pair of interpretations: a core interpretation and a context in-
terpretation. The core vocabulary defines contexts-dependent description of the
concepts, roles and axioms of the ALCO fragment3 .
– [2000] Student is a contextualized concept which refers to students in the
year “2000” where “2000” is the temporal context.
– [wikipedia] birthPlace is a contextualized role which refers to the birthPlace
property in the context of “wikipedia” where “wikipedia” is the provenance
context.
2
Because as mentioned by Klarman the cost is already hidden in the shift from one
dimensional to two dimensional semantics
3
which is proven to be sound [14]
– [before1970] (CanVote v Aged21orMore) is a contextualized axiom that il-
lustrates the fact that,“ before 1970”, voting was restricted to people who
were at least 21. The same applies for the other axioms included in ALCO.
– [2000] Student(John) is a contextualized concept assertion which means that
John was a student in the year “2000”.
We additionally use the rigid designator hypothesis [16] for individuals, which
means that the interpretation of an individual is the same in all contexts.
The context language introduces two contextual constructors:
– (hCountryi Citizen) illustrates the existential contextual operator. The ex-
ample refers to the concept citizen in some context of type country.
– ([Country]Citizen) illustrates the universal contextual operator. The exam-
ple refers to the concept citizen in all context of type country.
Contextual Reasoning with OWL-RLC . We defined a profile for the con-
textual web ontology language that we presented previously, by adapting4 the
idea of OWL 2 RL to OWLC . We call this new profile OWL-RLC . Due to space
limitations, we introduce only one rule of each language in table 1. We use a
quaternary predicate Q(s; p; o; co)5 where s is the subject, p is the predicate,
o is the object and co is the context for which the predicate holds. Variables in
the implications are preceded with a question mark. For instance, the contextual
entailment rule of the universal constructor of the core language (∀role.Concept)
takes three forms where: 1) both the corresponding class and role are contex-
tual, 2) only the class is contextual, 3) only the role is contextual. Table 1
illustrates the first case. On the other hand, defining the rules of the context
language is crucial because it imposes the declaration of new predicates such
as: owl-rlc :onClass (i.e. declares the class on which the constructs apply), owl-
rlc :inAllContextOf (i.e. similar to owl:allvaluesFrom but for the contexts only)
among others.
Practical implementation:
– Encoding contexts in RDF: we showed in [1] that the fluent model is capable
of supporting semantic relations between multiple time intervals. As a first
attempt, we extended this model to any dimension of contexts and adapt it
to support the representation of the context dependent concepts ( roles, and
axioms too) of the core language. We implicitly used the standard mapping6
4
Syntactically, we are considering only a subset (fragment) of OWL 2 RL whose con-
structors and axioms correspond to ALCO, i.e. (approximately) the intersection of
OWL 2 RL and ALCO. Or, equivalently, ALCO with the sub class axiom restrictions
of OWL 2 RL. Additionally, the semantics is contextual.
5
In the original version, they use a predicate T which is a generalization of RDF
triples
6
https://www.w3.org/TR/owl2-mapping-to-rdf/
Table 1. Two rules of OW L − RLc
IF THEN
T(?x, owl:allValuesFrom, ?y)
T(?x, owl:onProperty, ?p)
∀p.Y Q(?v, rdf:type, ?y, ?co)
Q(?u, rdf:type, ?x, ?co)
Q(?u, ?p, ?v, ?co)
T(?e, owlc :onClass, ?d)
T(?e, owlc :inAllContextOf, ?c)
([C]D) T(?y, rdf:type, ?c)
T(?x, rdf:type, ?e)
Q(?x, rdf:type, ?d, ?y)
of OWL to RDF to represent the concepts, roles and axioms of the core vo-
cabulary and extended it to handle the contextual constructs of the contexts
vocabulary.
– Implementation of OWL-RLC using SPIN rules: the majority of the rules
for the core vocabulary generate a quadruple Q. That means, there is a gen-
eration of new objects, some of which, are the context instances that could
be handled neither by OWL reasoners, nor by an addition of SWRL rules.
Therefore, we decided to use SPARQL spin notation. SPIN can be used to
encapsulate reusable SPARQL queries as templates. Then, they can be in-
stantiated in any RDF or OWL ontology to add inference rules and constraint
checks. Using Sparql rules, we managed to generate the new objects while
committing to some predefined constraints, for instance, the non-generation
of existing contextual statements which is incorporated directly as a filter in
the sparql rule.
7 Evaluation plan
We have designed a multi-dimensional design space for the evaluation of the
overall model :
– Expressiveness of the model in terms of description logics varieties. This will
involve a theoretical investigation of the expressiveness of the logic as well
as a user study to check to what extent the proposed logic addresses specific
application scenarios. In particular, we tend to evaluate the model on an
ongoing project in digital humanities [2], where linguists are interested in
revealing temporal aspects of Ferdinand de Saussure’s manuscripts, using
the associated contextualized knowledge graph.
– Usability of the contextual language. To measure the usability of the lan-
guage, we will check if domain specialists (e.g. historians, linguists, etc.) can
use it to express contextual facts and axioms.
– Degree of object proliferation. Using a model that introduces many properties
and objects can lead to undesirable graph size increases, which oftentimes
cause detrimental memory performance. The worst case scenario could lead
to an explosion of the number of triples. Therefore, we plan to measure the
number of objects and predicates using our context representation approach
and compare it to other approaches.
– Time needed to check the consistency of the model.
– Ability to deal with polymorphism when adding new dimensions of contexts.
In other words, the model should be flexible enough to make the knowledge
base grow linearly when adding new dimensions of contexts.
– Generation of non-desired inferences. A previous work [10] showed that the
adoption of certain patterns can generate undesired inferences. To avoid
that, we will study the behavior of every rule separately to check if there is
a generation of non-desired inferences.
8 Reflections
To conclude, we identified from the state of the art approaches that the task of
contextual reasoning on the web of data is still in early stages. In our research,
we intend to push this forward with a practical implementation. We introduced
a contextual extension of the web ontology language OWLC based on a two-
dimensional description logic. Additionally, we created a profile for contextual
reasoning by adapting the idea of OWL-RL to OWLC . OWL-RLC contains new
context-dependent rules and novel rules for handling the new constructs. We
did not consider the semantic relations that could exist between the contexts,
but we plan to work on this in the next phase. We would also like to study the
requirements to extend the model to a fragment larger than ALCO. On a lower
level, we consider that the problem of encoding contextual knowledge in RDF
datasets is a minor issue because it is already performed locally by a lot of data
providers. We believe that what should be settled is an upper vocabulary to be
commonly used for describing such metadata.
Acknowledgments: I would like to thanks my PhD advisors Prof. Gilles
Falquet and Prof. Didier Buchs.
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