Representation of defeasible information is of interest in description logics, as it is related t... more Representation of defeasible information is of interest in description logics, as it is related to the need of accommodating exceptional instances in knowledge bases. In this direction, in our previous works we presented a datalog translation for reasoning on (contextualized) OWL RL knowledge bases with a notion of justified exceptions on defeasible axioms. While it covers a relevant fragment of OWL, the resulting reasoning process needs a complex encoding in order to capture reasoning on negative information. In this paper, we consider the case of knowledge bases in $\textit{DL-Lite}_{\cal R}$, i.e. the language underlying OWL QL. We provide a definition for $\textit{DL-Lite}_{\cal R}$ knowledge bases with defeasible axioms and study their properties. The limited form of $\textit{DL-Lite}_{\cal R}$ axioms allows us to formulate a simpler encoding into datalog (under answer set semantics) with direct rules for reasoning on negative information. The resulting materialization method g...
Abstract. The aim of this work is to present a complex, web-based virtual museum application, int... more Abstract. The aim of this work is to present a complex, web-based virtual museum application, integrating several tools for flexible management of heterogeneous and highly structured knowledge. All the used tools are compliant to W3C's standards. In particular, the complex network of associations and relations among concepts and objects (as typically found in a virtual museum environment) has been faithfully represented adopting W3C's Semantic Web standards.
Abstract. Ontologies are a well-affirmed way of representing complex structured information and t... more Abstract. Ontologies are a well-affirmed way of representing complex structured information and they provide a sound conceptual foundation to Semantic Web technologies. On the other hand, a huge amount of information available on the web is stored in legacy relational databases. The issues raised by the collaboration between such worlds are well known and addressed by consolidated mapping languages.
As the interest in the representation of context dependent knowledge in the Semantic Web has been... more As the interest in the representation of context dependent knowledge in the Semantic Web has been recognized, a number of logic based solutions have been proposed in this regard. In our recent works, in response to this need, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework. CKR is not only a theoretical framework, but it has been effectively implemented over state-of-the-art tools for the management of Semantic Web data: inference inside and across contexts has been realized in the form of forward SPARQL-based rules over different RDF named graphs. In this paper we present the first evaluation results for such CKR implementation. In particular, in first experiment we study its scalability with respect to different reasoning regimes. In a second experiment we analyze the effects of knowledge propagation on the computation of inferences.
As the amount of available linked data expand and the
number of related applications increases, t... more As the amount of available linked data expand and the number of related applications increases, the management of aspects such as provenance and access control of such data begin to become an issue. Current approaches do not provide sufficient support for automatic reasoning over different metadata and their possible interdependencies. MetaReasons is a framework that supports the representation of metadata in a logical formalism and consequently to support automated reasoning on metadata. Different types of metadata, such as data-provenance and accessibility-restrictions are represented as distinct meta-theories, and dependencies between types of metadata are represented by rules between different meta-theories. In this paper we present the logic based definition of the MetaReasons framework and two examples of meta-theories for provenance and access control. Moreover, we propose a materialization calculus for concrete forward reasoning on the two aspects.
Representation of context dependent knowledge in the Semantic Web
has been recognized as a releva... more Representation of context dependent knowledge in the Semantic Web has been recognized as a relevant issue: as a consequence, a number of logic based formalisms have been proposed in this regard. In response to this need, in previous works, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework. Starting from this point, the first contribution of the paper is an extension of CKR with the possibility to represent defaults in context dependent axioms and a translation of extended CKRs to datalog programs with negation under answer sets semantics. The translation generates datalog programs which are sound and complete w.r.t. instance checking in CKRs. Exploiting this result, we have developed as a second contribution a prototype implementation that compiles a CKR based on OWL2RL to a datalog program. Finally, we compare our approach with major non-monotonic formalisms for description logics and contextual knowledge representation.
Recently, representation of context dependent knowledge in the Semantic
Web has been recognized a... more Recently, representation of context dependent knowledge in the Semantic Web has been recognized as a relevant issue and a number of logic based solutions have been proposed in this regard: among them, in our previous works we presented the Contextualized Knowledge Repository (CKR) framework. A CKR knowledge base has a two layered structure, modelled by a global context and a set of local contexts: the global context not only contains the metaknowledge defining the properties of local contexts, but also holds the global (context independent) object knowledge that is shared by all of the local contexts. In many practical cases, however, it is desirable to leave the possibility to “override” the global object knowledge at the local level, by recognizing the axioms that can allow exceptional instances in the local contexts. This clearly requires to add a notion of non monotonicity across the global and the local parts of a CKR. In this paper we present an extension to the semantics of CKR to introduce such notion of defeasible axioms. By extending a previously proposed datalog translation, we obtain a representation for CKR as a datalog program with negation under answer set semantics. This representation can be exploited as the basis for implementation of query answering for the proposed extension of CKR.
The capability of dealing with context sensitive knowledge is recognized
as a crucial aspect in t... more The capability of dealing with context sensitive knowledge is recognized as a crucial aspect in the management of massive amounts of Semantic Web (SW) data. Contextual knowledge can be modelled either by adopting the primitives from RDF/OWL based SW languages or by extending such languages with new specific constructs for context representation. In this paper, we show the benefits of the context-based solution by comparing modelling and reasoning in the two approaches on the paradigmatic use case of FIFA World Cup. The comparison considers the three key aspects of engineering and exploiting knowledge: (i) simplicity and expressivity of the (formal) language; (ii) compactness of the representation; and (iii) efficiency of reasoning. As for (i), we show that the context-based language enables the construction of simpler and more intuitive models while the RDF/OWL “flat” model presents practical limitations in modelling cross-contextual knowledge. For (ii), we show that the contextualized model is more compact than the OWL based model. Finally for (iii), query answering in the context-based model outperforms in most of the cases performances on the flat model.
Contextualized Knowledge Repository (CKR) is a DL-based framework for representation and reasonin... more Contextualized Knowledge Repository (CKR) is a DL-based framework for representation and reasoning with context dependent knowledge. It addresses the widely recognized need for contextualization of the Semantic Web data sources. Reasoning with CKR is possible thanks to a reduction to standard DL, and more recently a NExpTime tableaux algorithm was introduced for ALC-based CKR. In this paper we present an ExpTime tableaux algorithm for ALC-based CKR. The algorithm not only formally defines a tableaux decision procedure with optimal complexity, it is also presented in a form that can be effectively applied in practice employing a suitable rule application strategy together with node caching.
. Representation of context dependent knowledge in the Semantic Web
is becoming a recognized issu... more . Representation of context dependent knowledge in the Semantic Web is becoming a recognized issue and a number of DL-based formalisms have been proposed in this regard: among them, in our previous works we introduced the Contextualized Knowledge Repository (CKR) framework. In CKR, contexts are organized hierarchically according to a broader-narrower relation and knowledge propagation across contexts is limited among contexts hierarchically related. In several applications, however, this structure is too restrictive, as they demand for a more flexible and scalable framework for representing and reasoning about contextual knowledge. In this work we present an evolution of the original CKR (based on OWL RL), where contexts can be organized in any graph based structure (declared as a metaknowledge base) and knowledge propagation is allowed among any pair of contexts via a new ”evaluate-in-context” operator. In particular, we detail a materialization calculus for reasoning over the revised CKR framework and prove its soundness and completeness. Moreover, we outline the current implementation of the calculus on top of SPRINGLES, an extension of standard RDF triple stores for representing and rule-based inferencing over multiple RDF named graphs.
Abstract The combination of often heterogeneous and contradicting knowledge sources in the Semant... more Abstract The combination of often heterogeneous and contradicting knowledge sources in the Semantic Web demands for methods to represent and reason with a notion of context. Among context representation frameworks, Contextualized Knowledge Repository (CKR) is a novel proposal grounded in the well-studied AI theories of context and aims at bringing the advantages of contextual representation to the Semantic Web. In this paper we show that reasoning in ALC-based CKR is decidable and EXPTIME-complete.
Abstract The growth of the Semantic Web, a space where heterogeneous and contradicting knowledge ... more Abstract The growth of the Semantic Web, a space where heterogeneous and contradicting knowledge sources coexist and are often combined, demands for effective methods to consider also the context within which the knowledge sources are intended to be valid. Contextualized Knowledge Repository (CKR) is a novel representation framework grounded in the well studied AI theories of context, whose aim is to bring the advantages of contextual representation to the Semantic Web.
Representation of defeasible information is of interest in description logics, as it is related t... more Representation of defeasible information is of interest in description logics, as it is related to the need of accommodating exceptional instances in knowledge bases. In this direction, in our previous works we presented a datalog translation for reasoning on (contextualized) OWL RL knowledge bases with a notion of justified exceptions on defeasible axioms. While it covers a relevant fragment of OWL, the resulting reasoning process needs a complex encoding in order to capture reasoning on negative information. In this paper, we consider the case of knowledge bases in $\textit{DL-Lite}_{\cal R}$, i.e. the language underlying OWL QL. We provide a definition for $\textit{DL-Lite}_{\cal R}$ knowledge bases with defeasible axioms and study their properties. The limited form of $\textit{DL-Lite}_{\cal R}$ axioms allows us to formulate a simpler encoding into datalog (under answer set semantics) with direct rules for reasoning on negative information. The resulting materialization method g...
Abstract. The aim of this work is to present a complex, web-based virtual museum application, int... more Abstract. The aim of this work is to present a complex, web-based virtual museum application, integrating several tools for flexible management of heterogeneous and highly structured knowledge. All the used tools are compliant to W3C's standards. In particular, the complex network of associations and relations among concepts and objects (as typically found in a virtual museum environment) has been faithfully represented adopting W3C's Semantic Web standards.
Abstract. Ontologies are a well-affirmed way of representing complex structured information and t... more Abstract. Ontologies are a well-affirmed way of representing complex structured information and they provide a sound conceptual foundation to Semantic Web technologies. On the other hand, a huge amount of information available on the web is stored in legacy relational databases. The issues raised by the collaboration between such worlds are well known and addressed by consolidated mapping languages.
As the interest in the representation of context dependent knowledge in the Semantic Web has been... more As the interest in the representation of context dependent knowledge in the Semantic Web has been recognized, a number of logic based solutions have been proposed in this regard. In our recent works, in response to this need, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework. CKR is not only a theoretical framework, but it has been effectively implemented over state-of-the-art tools for the management of Semantic Web data: inference inside and across contexts has been realized in the form of forward SPARQL-based rules over different RDF named graphs. In this paper we present the first evaluation results for such CKR implementation. In particular, in first experiment we study its scalability with respect to different reasoning regimes. In a second experiment we analyze the effects of knowledge propagation on the computation of inferences.
As the amount of available linked data expand and the
number of related applications increases, t... more As the amount of available linked data expand and the number of related applications increases, the management of aspects such as provenance and access control of such data begin to become an issue. Current approaches do not provide sufficient support for automatic reasoning over different metadata and their possible interdependencies. MetaReasons is a framework that supports the representation of metadata in a logical formalism and consequently to support automated reasoning on metadata. Different types of metadata, such as data-provenance and accessibility-restrictions are represented as distinct meta-theories, and dependencies between types of metadata are represented by rules between different meta-theories. In this paper we present the logic based definition of the MetaReasons framework and two examples of meta-theories for provenance and access control. Moreover, we propose a materialization calculus for concrete forward reasoning on the two aspects.
Representation of context dependent knowledge in the Semantic Web
has been recognized as a releva... more Representation of context dependent knowledge in the Semantic Web has been recognized as a relevant issue: as a consequence, a number of logic based formalisms have been proposed in this regard. In response to this need, in previous works, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework. Starting from this point, the first contribution of the paper is an extension of CKR with the possibility to represent defaults in context dependent axioms and a translation of extended CKRs to datalog programs with negation under answer sets semantics. The translation generates datalog programs which are sound and complete w.r.t. instance checking in CKRs. Exploiting this result, we have developed as a second contribution a prototype implementation that compiles a CKR based on OWL2RL to a datalog program. Finally, we compare our approach with major non-monotonic formalisms for description logics and contextual knowledge representation.
Recently, representation of context dependent knowledge in the Semantic
Web has been recognized a... more Recently, representation of context dependent knowledge in the Semantic Web has been recognized as a relevant issue and a number of logic based solutions have been proposed in this regard: among them, in our previous works we presented the Contextualized Knowledge Repository (CKR) framework. A CKR knowledge base has a two layered structure, modelled by a global context and a set of local contexts: the global context not only contains the metaknowledge defining the properties of local contexts, but also holds the global (context independent) object knowledge that is shared by all of the local contexts. In many practical cases, however, it is desirable to leave the possibility to “override” the global object knowledge at the local level, by recognizing the axioms that can allow exceptional instances in the local contexts. This clearly requires to add a notion of non monotonicity across the global and the local parts of a CKR. In this paper we present an extension to the semantics of CKR to introduce such notion of defeasible axioms. By extending a previously proposed datalog translation, we obtain a representation for CKR as a datalog program with negation under answer set semantics. This representation can be exploited as the basis for implementation of query answering for the proposed extension of CKR.
The capability of dealing with context sensitive knowledge is recognized
as a crucial aspect in t... more The capability of dealing with context sensitive knowledge is recognized as a crucial aspect in the management of massive amounts of Semantic Web (SW) data. Contextual knowledge can be modelled either by adopting the primitives from RDF/OWL based SW languages or by extending such languages with new specific constructs for context representation. In this paper, we show the benefits of the context-based solution by comparing modelling and reasoning in the two approaches on the paradigmatic use case of FIFA World Cup. The comparison considers the three key aspects of engineering and exploiting knowledge: (i) simplicity and expressivity of the (formal) language; (ii) compactness of the representation; and (iii) efficiency of reasoning. As for (i), we show that the context-based language enables the construction of simpler and more intuitive models while the RDF/OWL “flat” model presents practical limitations in modelling cross-contextual knowledge. For (ii), we show that the contextualized model is more compact than the OWL based model. Finally for (iii), query answering in the context-based model outperforms in most of the cases performances on the flat model.
Contextualized Knowledge Repository (CKR) is a DL-based framework for representation and reasonin... more Contextualized Knowledge Repository (CKR) is a DL-based framework for representation and reasoning with context dependent knowledge. It addresses the widely recognized need for contextualization of the Semantic Web data sources. Reasoning with CKR is possible thanks to a reduction to standard DL, and more recently a NExpTime tableaux algorithm was introduced for ALC-based CKR. In this paper we present an ExpTime tableaux algorithm for ALC-based CKR. The algorithm not only formally defines a tableaux decision procedure with optimal complexity, it is also presented in a form that can be effectively applied in practice employing a suitable rule application strategy together with node caching.
. Representation of context dependent knowledge in the Semantic Web
is becoming a recognized issu... more . Representation of context dependent knowledge in the Semantic Web is becoming a recognized issue and a number of DL-based formalisms have been proposed in this regard: among them, in our previous works we introduced the Contextualized Knowledge Repository (CKR) framework. In CKR, contexts are organized hierarchically according to a broader-narrower relation and knowledge propagation across contexts is limited among contexts hierarchically related. In several applications, however, this structure is too restrictive, as they demand for a more flexible and scalable framework for representing and reasoning about contextual knowledge. In this work we present an evolution of the original CKR (based on OWL RL), where contexts can be organized in any graph based structure (declared as a metaknowledge base) and knowledge propagation is allowed among any pair of contexts via a new ”evaluate-in-context” operator. In particular, we detail a materialization calculus for reasoning over the revised CKR framework and prove its soundness and completeness. Moreover, we outline the current implementation of the calculus on top of SPRINGLES, an extension of standard RDF triple stores for representing and rule-based inferencing over multiple RDF named graphs.
Abstract The combination of often heterogeneous and contradicting knowledge sources in the Semant... more Abstract The combination of often heterogeneous and contradicting knowledge sources in the Semantic Web demands for methods to represent and reason with a notion of context. Among context representation frameworks, Contextualized Knowledge Repository (CKR) is a novel proposal grounded in the well-studied AI theories of context and aims at bringing the advantages of contextual representation to the Semantic Web. In this paper we show that reasoning in ALC-based CKR is decidable and EXPTIME-complete.
Abstract The growth of the Semantic Web, a space where heterogeneous and contradicting knowledge ... more Abstract The growth of the Semantic Web, a space where heterogeneous and contradicting knowledge sources coexist and are often combined, demands for effective methods to consider also the context within which the knowledge sources are intended to be valid. Contextualized Knowledge Repository (CKR) is a novel representation framework grounded in the well studied AI theories of context, whose aim is to bring the advantages of contextual representation to the Semantic Web.
More and more applications require real-time processing of massive, dynamically generated, ordere... more More and more applications require real-time processing of massive, dynamically generated, ordered data, where order is often an essential factor reflecting recency. Data stream management techniques provide reactive and reliable processing mechanisms over such data. Key to their success is the use of streaming algorithms that harness the natural or enforceable orders in the data. This trend started to be visible also in the Web, where an increasing number of streaming sources and datasets are becoming available. They originate from social networks, sensor networks, Internet of Things (IoT) and many other technologies that find in the Web a platform for sharing data. This is resulting in new Web-centric efforts such as the Web of Things, which studies how to expose and describe IoT using the Web, or the Social Web, which investigates protocols, vocabularies, and APIs to facilitate access to social functionality as part of the Web. In the Semantic Web context emerged efforts like Stream Reasoning and RDF Stream Processing. Stream Reasoning aims at combing data stream management and semantic technologies to perform reasoning over massive, heterogeneous and dynamic data;, while RDF Stream Processing studies the continuous query answering process over data streams modelled accordingly to the RDF model. The workshop put together such sub-communities to discuss and to investigate holistic processing models for streams over the Web, which consider the issues about publishing data streams on the Web as well as processing them with queries and inference processes. The event has contributed in the creation of an active community interested in integrating stream processing and reasoning by using methods inspired by data and knowledge management. WOMoCoE 2017: Ontology Modularity, Contextuality, and Evolution In the Semantic Web and Linked Data, knowledge is rarely considered to be a monolithic and static entity. Instead, partitioning knowledge into distinct modular structures is central to organize knowledge bases, from the design of the latter to their creation, from their maintenance to their use in knowledge sharing. From a different perspective, representing and reasoning about a context with respect to knowledge in distinct modules is essential for the correct exploitation knowledge bases, and for reliable and effective reasoning in changing situations. Finally, evolution of knowledge resources, in terms of updates by newly acquired knowledge, is an important factor ensuring that over time stored knowledge remains meaningful. The 2nd International Workshop on Ontology Modularity, Contextuality, and Evolution (WOMoCoE 2017) provided a forum for practitioners and researchers to discuss current trends in modularity, contextuality, and evolution of knowledge resources. The workshop aimed at bringing together an interdisciplinary audience that is interested in its topics both from a theoretical, formal point of view but as well from an application-oriented perspective. WOMoCoE 2017 attracted 5 submissions. Each submission was reviewed by at least three members of the Program Committee, and 4 were accepted for oral presentation at the workshop and are included in this volume. The workshop also featured an invited talk by Pascal Hitzler.
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Papers by Loris Bozzato
number of related applications increases, the management of aspects
such as provenance and access control of such data begin to become
an issue. Current approaches do not provide sufficient support for
automatic reasoning over different metadata and their possible interdependencies.
MetaReasons is a framework that supports the representation
of metadata in a logical formalism and consequently to
support automated reasoning on metadata. Different types of metadata,
such as data-provenance and accessibility-restrictions are represented
as distinct meta-theories, and dependencies between types
of metadata are represented by rules between different meta-theories.
In this paper we present the logic based definition of the MetaReasons
framework and two examples of meta-theories for provenance
and access control. Moreover, we propose a materialization calculus
for concrete forward reasoning on the two aspects.
has been recognized as a relevant issue: as a consequence, a number of logic
based formalisms have been proposed in this regard. In response to this need, in
previous works, we presented the description logic-based Contextualized Knowledge
Repository (CKR) framework. Starting from this point, the first contribution
of the paper is an extension of CKR with the possibility to represent defaults in
context dependent axioms and a translation of extended CKRs to datalog programs
with negation under answer sets semantics. The translation generates datalog
programs which are sound and complete w.r.t. instance checking in CKRs.
Exploiting this result, we have developed as a second contribution a prototype
implementation that compiles a CKR based on OWL2RL to a datalog program.
Finally, we compare our approach with major non-monotonic formalisms for description
logics and contextual knowledge representation.
Web has been recognized as a relevant issue and a number of logic based
solutions have been proposed in this regard: among them, in our previous works
we presented the Contextualized Knowledge Repository (CKR) framework.
A CKR knowledge base has a two layered structure, modelled by a global context
and a set of local contexts: the global context not only contains the metaknowledge
defining the properties of local contexts, but also holds the global (context
independent) object knowledge that is shared by all of the local contexts. In many
practical cases, however, it is desirable to leave the possibility to “override” the
global object knowledge at the local level, by recognizing the axioms that can
allow exceptional instances in the local contexts. This clearly requires to add a
notion of non monotonicity across the global and the local parts of a CKR.
In this paper we present an extension to the semantics of CKR to introduce such
notion of defeasible axioms. By extending a previously proposed datalog translation,
we obtain a representation for CKR as a datalog program with negation
under answer set semantics. This representation can be exploited as the basis for
implementation of query answering for the proposed extension of CKR.
as a crucial aspect in the management of massive amounts
of Semantic Web (SW) data. Contextual knowledge can be modelled
either by adopting the primitives from RDF/OWL based SW
languages or by extending such languages with new specific constructs
for context representation. In this paper, we show the benefits
of the context-based solution by comparing modelling and
reasoning in the two approaches on the paradigmatic use case of
FIFA World Cup. The comparison considers the three key aspects
of engineering and exploiting knowledge: (i) simplicity and expressivity
of the (formal) language; (ii) compactness of the representation;
and (iii) efficiency of reasoning. As for (i), we show
that the context-based language enables the construction of simpler
and more intuitive models while the RDF/OWL “flat” model
presents practical limitations in modelling cross-contextual knowledge.
For (ii), we show that the contextualized model is more compact
than the OWL based model. Finally for (iii), query answering
in the context-based model outperforms in most of the cases performances
on the flat model.
is becoming a recognized issue and a number of DL-based formalisms have been
proposed in this regard: among them, in our previous works we introduced the
Contextualized Knowledge Repository (CKR) framework. In CKR, contexts are
organized hierarchically according to a broader-narrower relation and knowledge
propagation across contexts is limited among contexts hierarchically related. In
several applications, however, this structure is too restrictive, as they demand
for a more flexible and scalable framework for representing and reasoning about
contextual knowledge.
In this work we present an evolution of the original CKR (based on OWL RL),
where contexts can be organized in any graph based structure (declared as a metaknowledge
base) and knowledge propagation is allowed among any pair of contexts
via a new ”evaluate-in-context” operator. In particular, we detail a materialization
calculus for reasoning over the revised CKR framework and prove its
soundness and completeness. Moreover, we outline the current implementation of
the calculus on top of SPRINGLES, an extension of standard RDF triple stores
for representing and rule-based inferencing over multiple RDF named graphs.
number of related applications increases, the management of aspects
such as provenance and access control of such data begin to become
an issue. Current approaches do not provide sufficient support for
automatic reasoning over different metadata and their possible interdependencies.
MetaReasons is a framework that supports the representation
of metadata in a logical formalism and consequently to
support automated reasoning on metadata. Different types of metadata,
such as data-provenance and accessibility-restrictions are represented
as distinct meta-theories, and dependencies between types
of metadata are represented by rules between different meta-theories.
In this paper we present the logic based definition of the MetaReasons
framework and two examples of meta-theories for provenance
and access control. Moreover, we propose a materialization calculus
for concrete forward reasoning on the two aspects.
has been recognized as a relevant issue: as a consequence, a number of logic
based formalisms have been proposed in this regard. In response to this need, in
previous works, we presented the description logic-based Contextualized Knowledge
Repository (CKR) framework. Starting from this point, the first contribution
of the paper is an extension of CKR with the possibility to represent defaults in
context dependent axioms and a translation of extended CKRs to datalog programs
with negation under answer sets semantics. The translation generates datalog
programs which are sound and complete w.r.t. instance checking in CKRs.
Exploiting this result, we have developed as a second contribution a prototype
implementation that compiles a CKR based on OWL2RL to a datalog program.
Finally, we compare our approach with major non-monotonic formalisms for description
logics and contextual knowledge representation.
Web has been recognized as a relevant issue and a number of logic based
solutions have been proposed in this regard: among them, in our previous works
we presented the Contextualized Knowledge Repository (CKR) framework.
A CKR knowledge base has a two layered structure, modelled by a global context
and a set of local contexts: the global context not only contains the metaknowledge
defining the properties of local contexts, but also holds the global (context
independent) object knowledge that is shared by all of the local contexts. In many
practical cases, however, it is desirable to leave the possibility to “override” the
global object knowledge at the local level, by recognizing the axioms that can
allow exceptional instances in the local contexts. This clearly requires to add a
notion of non monotonicity across the global and the local parts of a CKR.
In this paper we present an extension to the semantics of CKR to introduce such
notion of defeasible axioms. By extending a previously proposed datalog translation,
we obtain a representation for CKR as a datalog program with negation
under answer set semantics. This representation can be exploited as the basis for
implementation of query answering for the proposed extension of CKR.
as a crucial aspect in the management of massive amounts
of Semantic Web (SW) data. Contextual knowledge can be modelled
either by adopting the primitives from RDF/OWL based SW
languages or by extending such languages with new specific constructs
for context representation. In this paper, we show the benefits
of the context-based solution by comparing modelling and
reasoning in the two approaches on the paradigmatic use case of
FIFA World Cup. The comparison considers the three key aspects
of engineering and exploiting knowledge: (i) simplicity and expressivity
of the (formal) language; (ii) compactness of the representation;
and (iii) efficiency of reasoning. As for (i), we show
that the context-based language enables the construction of simpler
and more intuitive models while the RDF/OWL “flat” model
presents practical limitations in modelling cross-contextual knowledge.
For (ii), we show that the contextualized model is more compact
than the OWL based model. Finally for (iii), query answering
in the context-based model outperforms in most of the cases performances
on the flat model.
is becoming a recognized issue and a number of DL-based formalisms have been
proposed in this regard: among them, in our previous works we introduced the
Contextualized Knowledge Repository (CKR) framework. In CKR, contexts are
organized hierarchically according to a broader-narrower relation and knowledge
propagation across contexts is limited among contexts hierarchically related. In
several applications, however, this structure is too restrictive, as they demand
for a more flexible and scalable framework for representing and reasoning about
contextual knowledge.
In this work we present an evolution of the original CKR (based on OWL RL),
where contexts can be organized in any graph based structure (declared as a metaknowledge
base) and knowledge propagation is allowed among any pair of contexts
via a new ”evaluate-in-context” operator. In particular, we detail a materialization
calculus for reasoning over the revised CKR framework and prove its
soundness and completeness. Moreover, we outline the current implementation of
the calculus on top of SPRINGLES, an extension of standard RDF triple stores
for representing and rule-based inferencing over multiple RDF named graphs.