Please provide a short abstract of 100 to 250 words. The abstract should not contain any undefined abbreviations or unspecified references. Work planned but not completed should not appear in the abstract.

User Modeling and User-Adapted Interaction

In this paper, we introduce a family of expressive extensions of Datalog, called Datalog+/-, as a new paradigm for query answering over ontologies. The Datalog+/- family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. We show in particular that Datalog+/- generalizes the DL-Lite family of tractable description logics, which are the most common tractable ontology languages in the context of the Semantic Web and databases. We also show how stratified negation can be added to Datalog+/- while keeping ontology querying tractable. Furthermore, the Datalog+/- family is of interest in its own right and can, moreover, be used in various contexts such as data integration and data exchange.

/pdf/a-general-datalog-based-framework-for-tractable-query-1zyz34puul.pdf

A general datalog-based framework for tractable query answering over ontologies

A general Datalog-based framework for tractable query answering over ontologies

Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an up-to-date comparison of the most interesting, data-oriented approaches available in the literature.

/pdf/a-data-oriented-survey-of-context-models-3vgxaaa4s1.pdf

A data-oriented survey of context models

The SPARQL query language is a recent W3C standard for processing RDF data, a format that has been developed to encode information in a machine-readable way. We investigate the foundations of SPARQL query optimization and (a) provide novel complexity results for the SPARQL evaluation problem, showing that the main source of complexity is operator OPTIONAL alone; (b) propose a comprehensive set of algebraic query rewriting rules; (c) present a framework for constraint-based SPARQL optimization based upon the well-known chase procedure for Conjunctive Query minimization. In this line, we develop two novel termination conditions for the chase. They subsume the strongest conditions known so far and do not increase the complexity of the recognition problem, thus making a larger class of both Conjunctive and SPARQL queries amenable to constraint-based optimization. Our results are of immediate practical interest and might empower any SPARQL query optimizer.

Foundations of SPARQL Query Optimization

Graph Database Management Systems provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In this framework, the conversion of the persistent layer of an application from a relational to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.

/pdf/converting-relational-to-graph-databases-4khy6z1b35.pdf

Converting relational to graph databases

Databases have been designed to store large volumes of data and to provide efficient query interfaces. Semantic Web formats are geared towards capturing domain knowledge, interlinking annotations, and offering a high-level, machine-processable view of information. However, the gigantic amount of such useful information makes efficient management of it increasingly difficult, undermining the possibility of transforming it into useful knowledge. The research presented by De Virgilio, Giunchiglia and Tanca tries to bridge the two worlds in order to leverage the efficiency and scalability of database-oriented technologies to support an ontological high-level view of data and metadata. The contributions present and analyze techniques for semantic information management, by taking advantage of the synergies between the logical basis of the Semantic Web and the logical foundations of data management. The books leitmotif is to propose models and methods especially tailored to represent and manage data that is appropriately structured for easier machine processing on the Web. After two introductory chapters on data management and the Semantic Web in general, the remaining contributions are grouped into five parts on Semantic Web Data Storage, Reasoning in the Semantic Web, Semantic Web Data Querying, Semantic Web Applications, and Engineering Semantic Web Systems. The handbook-like presentation makes this volume an important reference on current work and a source of inspiration for future development, targeting academic and industrial researchers as well as graduate students in Semantic Web technologies or database design.

Semantic Web Information Management: A Model-Based Perspective

Mobile devices provide a variety of ways to access information resources available on the Web and a high level of adaptability to different aspects of the context (such as the device capabilities, the network QoS, the user preferences, and the location) is strongly required in this scenario. In this paper, we present a rule-based approach supporting the automatic adaptation of content delivery in Web Information Systems. The approach relies on the general notions of profile and configuration. The former is used to model a variety of context characteristics in a uniform way. The latter describes, in abstract terms, how to build the various levels of a suitable Web interface (content, navigation and presentation). We propose an original notion of adaptation rule that can be used to specify, in a declarative way, how to build a configuration that satisfies the requirements of adaptation for a profile. The evaluation process defined for these rules supports: (1) the handling of many separately specified adaptation requirements according to different aspects of the context, possibly not fixed in advance, and (2) their integration into one coherent recipe for adaptation. We also describe the architecture and functionality of a prototype implementing the proposed approach and illustrate experimental results supporting its flexibility and efficiency.

http://www.inf.uniroma3.it/~torlone/pubs/wwwj07.pdf

Rule-based Adaptation of Web Information Systems

Graph Database Management Systems (GDBMS) are rapidly emerging as an effective and efficient solution to the management of very large data sets in scenarios where data are naturally represented as a graph and data accesses mainly rely on traversing this graph. Currently, the design of graph databases is based on best practices, usually suited only for a specific GDBMS. In this paper, we propose a model-driven, system-independent methodology for the design of graph databases. Starting from a conceptual representation of the domain of interest expressed in the Entity-Relationship model, we propose a strategy for devising a graph database in which the data accesses for answering queries are minimized. Intuitively, this is achieved by aggregating in the same node data that are likely to occur together in query results. Our methodology relies a logical model for graph databases, which makes the approach suitable for different GDBMSs. We also show, with a number of experimental results over different GDBMSs, the effectiveness of the proposed methodology.

/pdf/model-driven-design-of-graph-databases-yo0gk676zy.pdf

Model-Driven Design of Graph Databases

The detection of communities in social networks is a challenging task. A rigorous way to model communities considers maximal cliques, that is, maximal subgraphs in which each pair of nodes is connected by an edge. State-of-the-art strategies for nding maximal cliques in very large networks decompose the network in blocks and then perform a distributed computation. These approaches exhibit a trade-o between eciency and completeness: decreasing the size of the blocks has been shown to improve eciency but some cliques may remain undetected since high-degree nodes, also called hubs, may not t with all their neighborhood into a small block. In this paper, we present a distributed approach that, by suitably handling hub nodes, is able to detect maximal cliques in large networks meeting both completeness and eciency. The approach relies on a two-level decomposition process. The rst level aims at recursively identifying and isolating tractable portions of the network. The second level further decomposes the tractable portions into small blocks. We demonstrate that this process is able to correctly detect all maximal cliques, provided that the sparsity of the network is bounded, as it is the case of real-world social networks. An extensive campaign of experiments conrms the

/pdf/finding-all-maximal-cliques-in-very-large-social-networks-407sbsp7fo.pdf

Roberto De Virgilio

Papers

Converting relational to graph databases

Semantic Web Information Management: A Model-Based Perspective

Rule-based Adaptation of Web Information Systems

Model-Driven Design of Graph Databases

Finding All Maximal Cliques in Very Large Social Networks