Faceted Search is a widely used interaction scheme in digital libraries, e-commerce, and recently also in Linked Data. Surprisingly, object ranking in the context of Faceted Search is not well studied in the literature. In this article, we propose an extension of the model with two parameters that enable specifying the desired answer size and the granularity of the sought object ranking. These parameters allow tackling the problem of too big or too small answers and can specify how refined the sought ranking should be. Then, we provide an algorithm that takes as input these parameters and by considering the hard-constraints (filters), the soft-constraints (preferences), as well as the statistical properties of the dataset (through various frequency-based ranking schemes), produces an object ranking that satisfies these parameters, in a transparent way for the user. Then, we present extensive simulation-based evaluation results that provide evidence that the proposed model also improves the answers and reduces the user’s cost. Finally, we propose GUI extensions that are required and present an implementation of the model.

Health‐related information is nowadays accessible from many sources and is one of the most searched‐for topics on the Internet. However, existing search systems often fail to provide users with a good list of medical search results, especially for classic (keyword‐based) queries. In this article we elaborate on whether and how we can exploit biomedicine‐related entities from the emerging Web of Data for improving (through reranking) the results returned by a search system. The aim is to promote relevant but low‐ranked hits containing entities that are important to the current search context. We introduce an approach that is based on entity extraction applied on the retrieved documents, yielding a graph of documents along with entities, which in turn is analyzed probabilistically using a Random Walk‐based method. The proposed approach is independent of the submitted query and the underlying retrieval models, and thus can be applied over any ranked list of medical search results. Evaluation results using the data set of TREC Clinical Decision Support track demonstrate that the proposed approach can significantly improve the results returned by classic and widely applicable retrieval models. The results also enabled us to identify cases where the proposed reranking method fails to improve the ranking.

In many applications one has to fetch and assemble pieces of information coming from more than one source for building a semantic warehouse offering more advanced query capabilities. In this paper the authors describe the corresponding requirements and challenges, and they focus on the aspects of quality and value of the warehouse. For this reason they introduce various metrics (or measures) for quantifying its connectivity, and consequently its ability to answer complex queries. The authors demonstrate the behaviour of these metrics in the context of a real and operational semantic warehouse, as well as on synthetically produced warehouses. The proposed metrics allow someone to get an overview of the contribution (to the warehouse) of each source and to quantify the value of the entire warehouse. Consequently, these metrics can be used for advancing data/endpoint profiling and for this reason the authors use an extension of VoID (for making them publishable). Such descriptions can be exploited for dataset/endpoint selection in the context of federated search. In addition, the authors show how the metrics can be used for monitoring a semantic warehouse after each reconstruction reducing thereby the cost of quality checking, as well as for understanding its evolution over time.

A big number of datasets has been published according to the principles of Linked Data and this number keeps increasing. Although the ultimate objective is linking and integration, it is not currently evident how connected the current LOD cloud is. Measurements (and indexes) that involve more than two datasets are not available although they are important: (a) for obtaining complete information about one particular URI (or set of URIs) with provenance (b) for aiding dataset discovery and selection , (c) for assessing the connectivity between any set of datasets for quality checking and for monitoring their evolution over time, (d) for constructing visualizations that provide more informative overviews. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this paper we introduce indexes (and their construction algorithms) that can speedup such tasks. In brief, we introduce (i) a namespace-based prefix index, (ii) a sameAs catalog for computing the symmetric and transitive closure of the owl:sameAs relationships encountered in the datasets, (iii) a semantics-aware element index (that exploits the aforementioned indexes), and finally (iv) two lattice-based incremental algorithms for speeding up the computation of the intersection of URIs of any set of datasets. We discuss the speedup obtained by the introduced indexes and algorithms through comparative results and finally we report measurements about connectivity of the LOD cloud that have never been carried out so far.

While more and more semantic data are published on the Web, an important question is how typical web users can access and exploit this body of knowledge. Although, existing interaction paradigms in semantic search hide the complexity behind an easy-to-use interface, they have not managed to cover common search needs. In this paper, we present X-ENS (eXplore ENtities in Search), a web search application that enhances the classical, keyword-based, web searching with semantic information, as a means to combine the pros of both Semantic Web standards and common Web Searching. X-ENS identifies entities of interest in the snippets of the top search results which can be further exploited in a faceted interaction scheme, and thereby can help the user to limit the - often very large - search space to those hits that contain a particular piece of information. Moreover, X-ENS permits the exploration of the identified entities by exploiting semantic repositories.

The ability to compute the differences that exist between two RDF/S Knowledge Bases (KB) is an important step to cope with the evolving nature of the Semantic Web (SW). In particular, RDF/S deltas can be employed to reduce the amount of data that need to be exchanged and managed over the network in order to build SW synchronization and versioning services. By considering deltas as sets of change operations, in this article we introduce various RDF/S differential functions which take into account inferred knowledge from an RDF/S knowledge base. We first study their correctness in transforming a source to a target RDF/S knowledge base in conjunction with the semantics of the employed change operations (i.e., with or without side-effects on inferred knowledge). Then we formally analyze desired properties of RDF/S deltas such as size minimality, semantic identity, redundancy elimination, reversibility, and composability, as well as identify those RDF/S differential functions that satisfy them. Subsequently, we experimentally evaluate the computing time and size of the produced deltas over real and synthetic RDF/S knowledge bases.

In this paper, we measure and analyze the graph features of semantic Web (SW) schemas with focus on power-law degree distributions. Our main finding is that the majority of SW schemas with a significant number of properties (respectively, classes) approximate a power law for total-degree (respectively, the number of subsumed classes) distribution. Moreover, our analysis revealed some emerging conceptual modeling practices of SW schema developers: (1) each schema has a few focal classes that have been analyzed in detail (that is, they have numerous properties and subclasses), which are further connected with focal classes defined in other schemas, (2) class subsumption hierarchies are mostly unbalanced (that is, some branches are deep and heavy, while others are shallow and light), (3) most properties have as domain/range classes that are located high at the class subsumption hierarchies, and (4) the number of recursive/multiple properties is significant. The knowledge of these features is essential for guiding synthetic SW schema generation, which is an important step toward benchmarking SW repositories and query language implementations.

Indexing and retrieval in Web catalogs can benefit from using faceted taxonomies. A faceted taxonomy consists of a set of facets, where each facet consists of a predefined set of terms structured by a subsumption relation. We propose two extensions of faceted taxonomies, which allow inferring conjunctions of terms that are valid in the underlying domain. We give a model-theoretic interpretation to these extended faceted taxonomies and we provide mechanisms for inferring the valid conjunctions of terms. This inference service can be exploited for preventing errors during the indexing process and for dynamically generating navigation trees that are suitable for browsing through the Web. The proposed scheme has several advantages by comparison to the hierarchical classification schemes that are currently employed by Web catalogs, namely: (a) conceptual clarity: it is easier to understand; (b) compactness: it takes less space; and (c) scalability: the update operations can be formulated easier and be performed more efficiently.

We propose a model for providing integrated and unified access to multiple information sources of the kind of Web catalogs. We assume that each source comprises two parts: (a) an ontology i.e. a set of terms structured by a subsumption relation, and (b) a database that stores objects of interest under the terms of the ontology. We assume that these objects belong to an underlying domain that is common to all sources (e.g. a set of Web pages of interest), and that different sources may use different ontologies with terms that correspond to different natural languages or to different levels of granularity. Information integration is obtained through a mediator comprising two parts: (a) an ontology, and (b) a set of articulations to the sources. Here, by articulation to a source we mean a set of relationships between terms of the mediator and terms of that source. Information requests (queries) are addressed to the mediator whose task is to analyze each query into sub-queries, translate them into queries to the appropriate sources, then merge the results to answer the original query. We study the querying and answering process in such a model and present algorithms for handling the main tasks of the mediator, namely, query translation between the mediator and the sources, source selection and result merging to produce the final answer.