"Most of the recent approaches to keyword search employ graph structured representation of data. Answers to queries are generally sub-structures of the graph, containing one or more keywords. While finding the nodes matching keywords is relatively easy, determining the connections between such nodes is a complex problem requiring on-the-fly time consuming graph exploration. Current techniques suffer from poorly performing worst case scenario or from indexing schemes that provide little support to the discovery of connections between nodes. In this paper, we present an indexing scheme for RDF that exposes the structural characteristics of the graph, its paths and the information on the reachability of nodes. This knowledge is exploited to expedite the retrieval of the sub-structures representing the query results. In addition, the index is organized to facilitate maintenance operations as the dataset evolves. Experimental results demonstrates the feasibility of our index that significantly improves the query execution performance.

"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.

"Approximate query answering relies on a similarity measure that evaluates the relevance, for a given query, of a set of data extracted from the underlying database. In the context of graph-modeled data, many methods (such as, subgraph isomorphism, graph edit distance, and maximum common subgraph) have been proposed to face this problem. Unfortunately, they are usually hard to compute and when they are used on RDF data, several drawbacks arise. In this paper, we propose a measure to evaluate the similarity between a (small) graph representing a query and a portion of a (large) graph representing an RDF data set. We show that this measure: (i) can be evaluated in linear time with respect to the size of the given graphs and, (ii) guarantees other interesting properties. In order to show the feasibility of our approach, we have used such similarity measure in a technique for approximate query answering. The technique has been implemented in a prototypical system and a number of experimental results obtained with this system confirm the effectiveness of the proposed measure.

A query over RDF data is usually expressed in terms of matching between a graph representing the target and a huge graph representing the source. Unfortunately, graph matching is typically performed in terms of subgraph isomorphism, which makes semantic data querying a hard problem. In this paper we illustrate a novel technique for querying RDF data in which the answers are built by combining paths of the underlying data graph that align with paths specified by the query. The approach is approximate and generates the combinations of the paths that best align with the query. We show that, in this way, the complexity of the overall process is significantly reduced and verify experimentally that our framework exhibits an excellent behavior with respect to other approaches in terms of both efficiency and effectiveness

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Keyword-based search over (semi)structured data is today considered an essential feature of modern information management systems and has become an hot topic in database research and development. Most of the recent approaches to this problem refer to a general scenario where: (i) the data source is represented as a graph, (ii) answers to queries are sub-graphs of the source containing keywords from queries, and (iii) solutions are ranked according to a relevance criteria. In this paper, we illustrate a novel approach to keyword search over semantic data that combines a solution building algorithm and a ranking technique to generate the best results in the first answers generated. We show that our approach is monotonic and has a linear computational complexity, greatly reducing the complexity of the overall process. Finally, experiments demonstrate that our approach exhibits very good efficiency and effectiveness, especially with respect to competing approaches.

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

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