Building Heterogeneous Multi-context Systems by Semantic Bindings

Lecture Notes in Business Information Processing, 2011

Multi-Context Systems (MCSs) are an expressive framework for interlinking heterogeneous knowledge systems, called contexts. Possible contexts are ontologies, relational databases, logic programs, RDF triplestores, etc. MCSs contain bridge rules to specify knowledge exchange between contexts. We extend the MCS formalism and propose SPARQL-MCS where knowledge exchange is specified in the style of SPARQL CONSTRUCT queries. Different from previous approaches to variables in MCSs, we do not impose any restrictions on contexts. To achieve this, we introduce a general approach for variable substitutions in heterogeneous systems. We define syntax and semantics of SPARQL-MCS and investigate fixpoint evaluation of monotonic MCSs. This research has been supported by the Vienna Science and Technology Fund (WWTF) project ICT08-020. 1 RDF is a semantic web framework for storing information as graphs, encoded in (subject predicate object) triples.

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017

This paper presents a conceptual framework and multi-agent model for context-aware decision support in dynamic smart environments based on heterogeneous knowledge sources. The framework relies on distributed ontologies and allows us to model context-aware agents which reason using rules that are derived from ontologies using the notion of multi-context systems. The use of the proposed framework is illustrated using a simple system developed from ontologies considering three different smart environment domains.

Proceedings of the 23rd International Joint Conference on Artificial Intelligence, 2013

A novel contextual logic is presented that combines features of both multi-context systems and logics of context. Broadly, contextual logics are those with a formal notion of context — knowledge that is true only under specific assumptions. Multi-context systems use discrete logistic systems as individual contexts, related by meta-level rules, whereas logics of context partition a single knowledge base into contexts, related using object-level rules. The contextual logic presented here is strongly-local, in that knowledge and inference is discrete for individual contexts, but which are nevertheless part of a single logistic system that relates contexts at the object-level, so combining advantages of both. A deduc- tive system of contextual inference and a possible- worlds based semantics is given, with formal results including soundness and completeness, and a number of properties are examined.

International Journal of Intelligent Systems, 1995

We propose multicontext systems (MC systems) as a formal framework for the specification of complex reasoning. MC systems provide the ability to structure the specification of "global" reasoning in terms of "local" reasoning subpatterns. Each subpattern is modeled as a deduction in a context, formally defined as an axiomatic formal system. The global reasoning pattern is modeled as a concatenation of contextual deductions via bridge rules, i.e., inference rules that infer a fact in one context from facts asserted in other contexts. Besides the formal framework, in this article we propose a threelayer architecture designed to specify and automatize complex reasoning. At the first level we have object-level contexts (called s-contexts) for domain specifications. Problem-solving principles and, more in general, meta-level knowledge about the application domain is specified in a distinct context, called Problem-Solving Context (PSC). On top of s-contexts and PSC, we have a further context, called MT, where it is possible to specify strategies to control multicontext reasoning spanning through s-contexts and PSC. We show how GETFOL can be used as a computer tool for the implementation of MC systems and for the automatization of multicontext deductions. 0 1995 John Wiley & Sons, Inc.

2009

Multi-Context Systems are logical formalizations of distributed context theories connected through mapping rules, which enable information flow between different contexts. Reasoning in Multi-Context Systems introduces many challenges that arise from the heterogeneity of contexts with regard to the language and inference system that they use, and from the potential conflicts that may arise from the interaction of context theories through the mappings. The current paper proposes four alternative strategies for using context and preference information to resolve conflicts in a Multi-Context Framework, in which contexts are modeled as rule theories, mappings as defeasible rules, and global inconsistency is handled using methods of distributed defeasible reasoning. Motivation and Background A Multi-Context System consists of a set of contexts and a set of inference rules (known as mapping or bridge rules) that enable information flow between different contexts. A context can be thought o...

Proceedings of the Sixth International Conference, …, 1998

In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief. This paper is a substantially revised and extended version of a paper with the same title presented at the 1998 Knowledge Representation and Reasoning conference (KR'98). The order of the names is alphabetical.

2017

Multi-Context Systems (MCS) model in Computational Logic distributed systems composed of heterogeneous sources, or “contexts”, interacting via special rules called “bridge rules”. In this paper, we consider how to enhance flexibility and generality in bridge-rules definition and application. In particular, we introduce and discuss some formal extensions of MCSs useful for a practical use in dynamic environments, and we try to provide guidelines for implementations.

2005

Abstract. We propose a two-fold use of context in reasoning about agents. The first concerns the modelling of beliefs which are entertained only within the scope of some assumption. We illustrate this use by describing as logical model of an agent which reasons in a natural deduction style by making assumptions to derive new formulas. The second use of context we propose here concerns the modelling of reasoning as a step-by-step temporal process, allowing us to model agents whose resources are temporally bounded.

IEEE Transactions on Knowledge and Data …, 2010

The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents and provide methods for handling the imperfect and distributed nature of context. This paper proposes a solution based on the Multicontext Systems paradigm in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To handle imperfect context, we extend Multicontext Systems with nonmonotonic features: local defeasible theories, defeasible mapping rules, and a preference ordering on the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through the mappings, and a distributed algorithm for query evaluation.

Journal of Experimental and Theoretical Artificial Intelligence, 2000

In this paper we provide a foundation of a theory of contextual reasoning from the perspective of a theory of knowledge representation. Starting from the so-called metaphor of the box, we £rstly show that the mechanisms of contextual reasoning proposed in the literature can be classi£ed into three general forms (called localised reasoning, push and pop, and shifting). Secondly, we provide a justi£cation of this classi£cation, by showing that each mechanism corresponds to operating on a fundamental dimension along which context dependent representations may vary (namely, partiality, approximation, and perspective). From the previous analysis, we distill two general principles of a logic of contextual reasoning. Finally, we show that these two principles can be adequately formalised in the framework of MultiContext Systems. In the last part of the paper, we provide a practical illustration of the ideas discussed in the paper by formalising a simple scenario, called the Magic Box problem.