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

A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

Large-scale Reasoning with Nonmonotonic and Imperfect Knowledge Through Mass Parallelization

Due to the recent explosion of available data coming from the Web, sensor readings, social media, government authorities and scientific databases, both academia and industry have increased their interest in utilizing this knowledge. Processing huge amounts of data introduces several scientific and technological challenges, and creates new opportunities. Existing works on large-scale reasoning through mass parallelization (namely parallelization based on utilizing a large number of processing units) concentrated on monotonic reasoning, which can process only consistent datasets. The question arises whether and how mass parallelization can be applied to reasoning with huge amounts of imperfect (e.g. inconsistent, incomplete) information. Potential scenarios involving such imperfect data and knowledge include ontology evolution, ontology repair and smart city applications combining a variety of heterogeneous data sources. In this thesis, we overcome the limitations of monotonic reasoning, by studying several nonmonotonic logics that have the ability to handle imperfect knowledge, and it is shown that large-scale reasoning is indeed achievable for such complex knowledge structures. This work is mainly focused on adapting existing methods, thus ensuring that the proposed solutions are parallel and scalable. Initially, preliminaries and literature review are presented in order to introduce the reader to basic background and the state-of-the-art considering large-scale reasoning. Subsequently, each chapter presents an approach for large-scale reasoning over a given logic. Large-scale reasoning over defeasible logic is supported allowing conflict resolution by prioritizing the superiority among rules in the rule set. A solution for stratified semantics is presented where rules may contain both positive and negative subgoals, thus allowing reasoning over missing information in a given dataset. The approach for stratified semantics is generalized in order to fully support the well-founded semantics, where recursion through negation is allowed. Finally, conclusion includes observations from a preliminary investigation on a restricted form of answer set programming, a generic evaluation framework for large-scale reasoning, a discussion of the main findings of this work, and opportunities for future work

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

Reasoning about Action: An Argumentation - Theoretic Approach

We present a uniform non-monotonic solution to the problems of reasoning about action on the basis of an argumentation-theoretic approach. Our theory is provably correct relative to a sensible minimisation policy introduced on top of a temporal propositional logic. Sophisticated problem domains can be formalised in our framework. As much attention of researchers in the field has been paid to the traditional and basic problems in reasoning about actions such as the frame, the qualification and the ramification problems, approaches to these problems within our formalisation lie at heart of the expositions presented in this paper