The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5\% of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases. Yet, this task requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES, a novel time-efficient approach for link discovery in metric spaces. Our approach utilizes the mathematical characteristics of metric spaces during the mapping process to filter out a large number of those instance pairs that do not suffice the mapping conditions. We present the mathematical foundation and the core algorithms employed in LIMES. We evaluate our algorithms with synthetic data to elucidate their behavior on small and large data sets with different configurations and compare the runtime of LIMES with another state-of-the-art link discovery tool.

Recent research has highlighted the benefits of completeness as a retrieval criterion in recommender systems. In complete retrieval, any subset of the constraints in a given query that can be satisfied must be satisfied by at least one of the retrieved products. Minimal completeness (i.e., always retrieving the smallest set of products needed for completeness) is also beginning to attract research interest as a way to minimize cognitive load in the approach. Other important features of a retrieval algorithm’s behavior include the diversity of the retrieved products and the order in which they are presented to the user. In this paper, we present a new algorithm for minimally complete retrieval (MCR) in which the ranking of retrieved products is primarily based on the number of constraints that they satisfy, but other measures such as similarity or utility can also be used to inform the retrieval process. We also present theoretical and empirical results that demonstrate our algorithm’s ability to minimize cognitive load while ensuring the completeness and diversity of the retrieved products.

In this paper we take a step towards using Argumentation in Social Networksand introduce Social Abstract Argumentation Frameworks, an extension of Dung'sAbstract Argumentation Frameworks that incorporates social voting.We propose a class of semantics for these new Social Abstract Argumentation Frameworks and prove some important non-trivial properties which are crucialfor their applicability in Social Networks.

Extracting a subset of a given ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of locality-based modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained.

Largely motivated by Semantic Web applications, many highly scalable, but incomplete, query answering systems have been recently developed. Evaluating the scalability-completeness trade-off exhibited by such systems is an important requirement for many applications. In this paper, we address the problem of formally comparing complete and incomplete systems given an ontology schema (or TBox) T. We formulate precise conditions on TBoxes T expressed in the EL, QL or RL profile of OWL 2 under which an incomplete system is indistinguishable from a complete one w.r.t. T, regardless of the input query and data. Our results also allow us to quantify the "degree of incompleteness" of a given system w.r.t. T as well as to automatically identify concrete queries and data patterns for which the incomplete system will miss answers.

In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ignore the dependency relations between classes. We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification. The method consists of two phases. In the first phase, a Bayesian network (BN) that represents the dependency relations between the class variables is learned from data. In the second phase, several chain classifiers are built, such that the order of the class variables in the chain is consistent with the class BN. At the end we combine the results of the different generated orders. Our method considers the dependencies between class variables and takes advantage of the conditional independence relations to build simplified models. We perform experiments with a chain of naive Bayes classifiers on different benchmark multidimensional datasets and show that our approach outperforms other state-of-the-art methods.

We discuss a novel approach for dealing with single-stage stochastic constraint satisfaction problems (SCSPs) that include random variables over a continuous or large discrete support. Our approach is based on two novel tools: sampled SCSPs and (α,ϑ)-solutions. Instead of explicitly enumerating a very large or infinite set of future scenarios, we employ statistical estimation to determine if a given assignment is consistent for a SCSP. As in statistical estimation, the quality of our estimate is determined via confidence interval analysis. In contrast to existing approaches based on sampling, we provide likelihood guarantees for the quality of the solutions found. Our approach can be used in concert with existing strategies for solving SCSPs.

How should we manage a sensor network to optimally guard security-critical infrastructure? How should we coordinate search and rescue helicopters to best locate survivors after a major disaster? In both applications, we would like to control sensing resources in uncertain, adversarial environments. In this paper, we introduce RSense, an efficient algorithm which guarantees near-optimal randomized sensing strategies whenever the detection performance satisfies submodularity, a natural diminishing returns property, for any fixed adversarial scenario. Our approach combines techniques from game theory with submodular optimization. The RSense algorithm applies to settings where the goal is to manage a deployed sensor network or to coordinate mobile sensing resources (such as unmanned aerial vehicles). We evaluate our algorithms on two real-world sensing problems.

We propose a new additive decomposition of probability tables that preserves equivalence of the joint distribution while reducing the size of potentials, without extra variables. We formulate the Most Probable Explanation (MPE) problem in belief networks as a Weighted Constraint Satisfaction Problem (WCSP). Our pairwise decomposition allows to replace a cost function with smaller-arity functions. The resulting pairwise decomposed WCSP is then easier to solve using state-of-the-art WCSP techniques. Although testing pairwise decomposition is equivalent to testing pairwise independence in the original belief network, we show how to efficiently test and enforce it, even in the presence of hard constraints. Furthermore, we infer additional information from the resulting nonbinary cost functions by projecting and subtracting them on binary functions. We observed huge improvements by preprocessing with pairwise decomposition and project&subtract compared to the current state-of-the-art solvers on two difficult sets of benchmark.

This paper is devoted to sequential decision making under uncertainty, in the multi-prior framework of Gilboa and Schmeidler [1989]. In this setting, a set of probability measures (priors) is defined instead of a single one, and the decision maker selects a strategy that maximizes the minimum possible value of expected utility over this set of priors. We are interested here in the resolute choice approach, where one initially commits to a complete strategy and never deviates from it later. Given a decision tree representation with multiple priors, we study the problem of determining an optimal strategy from the root according to min expected utility. We prove the intractability of evaluating a strategy in the general case. We then identify different properties of a decision tree that enable to design dedicated resolution procedures. Finally, experimental results are presented that evaluate these procedures.