Development of a Logic Layer in the Semantic Web: Research Issues

The ontology layer of the semantic web is now mature enough (i.e. standards like RDF, RDFs, OWL, OWL 2) and the next step is to work on a logic layer for the development of advanced reasoning capabilities for knowledge extraction and efficient decision making. Adding logic to the web means using rules to make inferences. Rules are a means of expressing business processes, policies, contracts etc but most of the studies have focused on the use of monotonic logics in layered development of the semantic web which provides no mechanism for representing or handling incomplete or contradictory information respectively. This paper discusses argumentation, semantic web and defeasible logic programming with their distinct features and identifies the different research issues that need to be addressed in order to realize defeasible argumentative reasoning in the semantic web applications

Integration of rules and ontologies with defeasible logic programming

The Semantic Web is a vision of the current Web where resources have exact meaning assigned in terms of ontologies, thus enabling agents to reason about them. As inconsistencies cannot be treated by standard reasoning approaches, we use Defeasible Logic Programming (DeLP) to reason with possibly inconsistent ontologies. In this article we show how to integrate rules and ontologies in the Semantic Web. We show how to use a possibly inconsistent set of rules represented by a DeLP program to reason on top of a set of (possibly inconsistent) ontologies.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Knowledge Representation Concepts for Automated SLA Management

Outsourcing of complex IT infrastructure to IT service providers has increased substantially during the past years. IT service providers must be able to fulfil their service-quality commitments based upon predefined Service Level Agreements (SLAs) with the service customer. They need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. The complexity of contractual logic in SLAs requires new forms of knowledge representation to automatically draw inferences and execute contractual agreements. A logic-based approach provides several advantages including automated rule chaining allowing for compact knowledge representation as well as flexibility to adapt to rapidly changing business requirements. We suggest adequate logical formalisms for representation and enforcement of SLA rules and describe a proof-of-concept implementation. The article describes selected formalisms of the ContractLog KR and their adequacy for automated SLA management and presents results of experiments to demonstrate flexibility and scalability of the approach.Comment: Paschke, A. and Bichler, M.: Knowledge Representation Concepts for Automated SLA Management, Int. Journal of Decision Support Systems (DSS), submitted 19th March 200

Large-scale Parallel Stratified Defeasible Reasoning

We are recently experiencing an unprecedented explosion of available data from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts

Integration of rules and ontologies with defeasible logic programming

The Semantic Web is a vision of the current Web where resources have exact meaning assigned in terms of ontologies, thus enabling agents to reason about them. As inconsistencies cannot be treated by standard reasoning approaches, we use Defeasible Logic Programming (DeLP) to reason with possibly inconsistent ontologies. In this article we show how to integrate rules and ontologies in the Semantic Web. We show how to use a possibly inconsistent set of rules represented by a DeLP program to reason on top of a set of (possibly inconsistent) ontologies.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A defeasible logic programming approach to the integration of rules and ontologies

The Semantic Web is a vision of the current Web where resources have exact meaning assigned in terms of ontologies, thus enabling agents to reason about them. As inconsistencies cannot be treated by standard reasoning approaches, we use Defeasible Logic Programming (DeLP) to reason with possibly inconsistent ontologies. In this article we show how to integrate rules and ontologies in the Semantic Web. We present an approach that can be used to suitably extend the SWRL standard by incorporating classical and default negated literals in SemanticWeb rules in the presence of incomplete and possibly inconsistent information. The rules and ontologies will be interpreted as a DeLP program allowing the rules to reason on top of a set of (possibly inconsistent) ontologies.Facultad de Informátic

Defeasible Reasoning in SROEL: from Rational Entailment to Rational Closure

In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.Comment: Accepted for publication on Fundamenta Informatica