We present a principled framework for modular web rule bases, called MWeb. According to this framework, each predicate defined in a rule base is characterized by its defining reasoning mode, scope, and exporting rule base list. Each predicate used in a rule base is characterized by its requesting reasoning mode and importing rule base list. For valid MWeb modular rule bases S, theMWebAS andMWebWFS semantics of each rule base s ∈ S w.r.t. S are defined, model-theoretically. These semantics extend the answer set semantics (AS) and the well-founded semantics with explicit negation (WFSX) on ELPs, respectively, keeping all of their semantical and computational characteristics. Our framework supports: (i) local semantics and different points of view, (ii) local closed-world and open-world assumptions, (iii) scoped negation-as-failure, and (iv) restricted propagation of local inconsistencies. Additionally, it guarantees monotonicity of reasoning, in the case that new rule bases are added to the modular rule base, while the importing rule base list of the predicates of the old rule bases remains the same.

Multi-Context Systems (MCS) are formalisms that enable the interlinkage of single knowledge bases, called contexts, via bridge rules. Recently, the evaluation of heterogeneous, nonmonotonic MCS was considered in Dao-Tran et al. (2010), where a fully distributed algorithm was described. In this paper, we continue this line of work and present a decomposition technique for MCS which analyzes the topology of an MCS. It applies pruning techniques to get economically small representations of context dependencies. Orthogonal to this, we characterize minimal interfaces for information exchange between contexts, such that data transmissions can be minimized. We then present a novel evaluation algorithm that operates on a query plan which is compiled with topology pruning and interface minimization. The effectiveness of the optimization techniques is demonstrated by a prototype implementation, which uses an off-the-shelf SAT solver and shows encouraging experimental results.

Abstract. We present a principled framework for modular web rule bases, called MWeb. According to this framework, each predicate defined in a rule base is characterized by its defining reasoning mode, scope, and exporting rule base list. Each predicate used in a rule base is characterized by its requesting reasoning mode and importing rule base list. For legal MWeb modular rule bases S, the MWebAS and MWebWFS semantics of each rule base s ∈ S w.r.t. S are defined model-theoretically. These semantics extend the answer set semantics (AS) and the well-founded semantics with explicit negation (WFSX) on ELPs, respectively, keeping all of their semantical and computational characteristics. Our framework supports: (i) local semantics and different points of view, (ii) local closed-world and open-world assumptions, (iii) scoped negation-as-failure, (iv) restricted propagation of local inconsistencies, and (v) monotonicity of reasoning, for “fully shared ” predicates.

We provide two approaches for explaining inconsistency in multi-context systems, where decentralized and heterogeneous system parts interact via nonmonotonic bridge rules. Inconsistencies arise easily in such scenarios, and nonmonotonicity calls for specific methods of inconsistency analysis. Both our approaches characterize inconsistency in terms of involved bridge rules: either by pointing out rules which need to be altered for restoring consistency, or by finding combinations of rules which cause inconsistency. We show duality and modularity properties, give precise complexity characterizations, and provide algorithms for computation using HEXprograms. Our results form a basis for inconsistency management in heterogeneous knowledge integration systems.

Abstract. We motivate, formalize and investigate the notions of data quality assessment and data quality query answering as context dependent activities. Contexts for the assessment and usage of a data source at hand are modeled as collections of external databases, that can be materialized or virtual, and mappings within the collections and with the data source at hand. In this way, the context becomes “the complement” of the data source wrt a data integration system. The proposed model allows for natural extensions, like considering data quality predicates, and even more expressive ontologies for data quality assessment. 1

argumentation where the argumentation context, that is information about preferences among arguments, values, validity, reasoning mode (skeptical vs. credulous) and even the chosen semantics can be explicitly represented. The framework consists of a collection of abstract argument systems connected via mediators. Each mediator integrates information coming from connected argument systems (thereby handling conflicts within this information) and provides the context used in a particular argumentation module. The framework can be used in different directions; e.g., for hierarchic argumentation as typically found in legal reasoning, or to model group argumentation processes. 1

Abstract. The Multi-Context System Inconsistency Explainer allows for evaluation of semantics and explanation of inconsistencies in systems where heterogeneous knowledge bases are linked via nonmonotonic rules. The implementation is based on the dlvhex tool, which is an extension of answer set programming with external atoms and higher order features. 1

Abstract. The developments in information technology during the last decade have been rapidly changing the possibilities for data and knowledge access. To respect this, several declarative knowledge representation formalisms have been extended with the capability to access data and knowledge sources that are external to a knowledge base. This article reviews some of these formalisms that are centered around Answer Set Programming, viz. HEX-programs, modular logic programs, and multi-context systems, which were developed by the KBS group of the Vienna University of Technology in cooperation with external colleagues. These formalisms were designed with different principles and four different settings, and thus have different properties and features; however, as argued, they are not unrelated. Furthermore, they provide a basis for advanced knowledge-based information systems, which are targeted in ongoing research projects. 1

Abstract. The evaluation of logic programs with access to external knowledge sources requires to interleave external computation and model building. Deciding where and how to stop with one task and proceed with the next is a difficult problem, and existing approaches have severe scalability limitations in many real-world application scenarios. We introduce a new approach for organizing the evaluation of logic programs with external knowledge sources and describe a configurable framework for dividing the non-ground program into overlapping possiblysmaller parts called evaluation units. These units will then be processed by interleaving external evaluations and model building according to an evaluation and a model graph, and by combining intermediate results. Experiments with our prototype implementation show a significant improvement of this technique compared to existing approaches. Interestingly, even for ordinary logic programs (with no external access), our decomposition approach speeds up existing state of the art ASP solvers in some cases, showing its potential for wider usage. 1

The DMCS system is an implementation of the equilibrium semantics for heterogeneous and nonmonotonic multi-context systems (MCS) [3], which feature contexts with heterogeneous and possibly nonmonotonic logics. Each context in an MCS comprises of two parts: a local knowledge base and a set of bridge rules that can access the beliefs of