The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area, and identify the most pertinent summarization method for a variety of usage scenarios.

PICSEL is an information integration system over sources that are distributed and possibly heterogeneous. The approach which has been chosen in PICSEL is to define an information server as a knowledge-based mediator in which CARIN is used as the core logical formalism to represent both the domain of application and the contents of information sources relevant to that domain. In this paper, we describe the way the expressive power of the CARIN language is exploited in the PICSEL information integration system, while maintaining the decidability of query answering. We illustrate it on examples coming from the tourism domain, which is the first real case that we have to consider in PICSEL, in collaboration with the travel agency Degriftour. see

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peerto-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers.

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.

Several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. The first contribution of this paper is a practical approach for computing the query answers under three well-known such semantics, namely the AR, IAR and brave semantics, in the lightweight description logic DL-LiteR. We show that query answering under the intractable AR semantics can be performed efficiently by using IAR and brave semantics as tractable approximations and encoding the AR entailment problem as a propositional satisfiability (SAT) problem. The second issue tackled in this work is explaining why a tuple is a (non-)answer to a query under these semantics. We define explanations for positive and negative answers under the brave, AR and IAR semantics. We then study the computational properties of explanations in DL-LiteR. For each type of explanation, we analyze the data complexity of recognizing (preferred) explanations and deciding if a given assertion is relevant or necessary. We establish tight connections between intractable explanation problems and variants of SAT, enabling us to generate explanations by exploiting solvers for Boolean satisfaction and optimization problems. Finally, we empirically study the efficiency of our query answering and explanation framework using a benchmark we built upon the well-established LUBM benchmark.

A promising method for efficiently querying RDF data consists of translating SPARQL queries into efficient RDBMS-style operations. However, answering SPARQL queries requires handling RDF reasoning, which must be implemented outside the relational engines that do not support it.We introduce the database (DB) fragment of RDF, going beyond the expressive power of previously studied RDF fragments. We devise novel sound and complete techniques for answering Basic Graph Pattern (BGP) queries within the DB fragment of RDF, exploring the two established approaches for handling RDF semantics, namely reformulation and saturation. In particular, we focus on handling database updates within each approach and propose a method for incrementally maintaining the saturation; updates raise specific difficulties due to the rich RDF semantics. Our techniques are designed to be deployed on top of any RDBMS(-style) engine, and we experimentally study their performance trade-offs.

As increasing volumes of RDF data are being produced and analyzed, many massively distributed architectures have been proposed for storing and querying this data. These architectures are characterized first, by their RDF partitioning and storage method, and second, by their approach for distributed query optimization, i.e., determining which operations to execute on each node in order to compute the query answers. We present CliqueSquare, a novel optimization approach for evaluating conjunctive RDF queries in a massively parallel environment. We focus on reducing query response time, and thus seek to build flat plans, where the number of joins encountered on a root-to-leaf path in the plan is minimized. We present a family of optimization algorithms, relying on n-ary (star) equality joins to build flat plans, and compare their ability to find the flattest possibles. We have deployed our algorithms in a MapReduce-based RDF platform and demonstrate experimentally the interest of the flat plans built by our best algorithms.Résumé : Pour faire face à l'explosion du volume de données RDF produites et analysées quotidiennement, de nombreux systèmes de stockage et d'interrogation de données RDF massivement distribués ont été développés. Ces architectures sont caractérisées par leur méthode de partitionnement et de stockage de données RDF d'une part, et, d'autre part, par la façon dont elles optimisent les requêtes, c'est-à-dire la manière dont les calculs sont distribués entre les différents noeuds afin de calculer les réponses.Cet article présente CliqueSquare, une nouvelle approche d'optimisation pour l'évaluation de requêtes RDF conjonctives dans un environnement massivement parallèle. Notre but principal est de réduire le temps de réponse des requêtes; pour cela, nous nous intéressons aux plans d'éxecution "plats" (de faible hauteur), dans lesquels le nombre de jointures successives sur un chemin allant de la racine du plan d'exécution jusqu'à l'un de ses opérateurs feuilles est minimisé. Nous présentons une famille d'algorithmes d'optimisation, basés sur des jointures d'égalité n-aires (en "étoile"), pour construire des plans plats et comparons leurs capacités à trouver les plans les plus plats possibles. Nous avons implémenté nos algorithmes dans une plate-forme RDF basée sur MapReduce; nos expériences démontrent l'intérêt des plans plats construits par nos meilleurs algorithmes d'optimisation.

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