Context Aware Source Selection for Linked Data

The traditional Web is evolving into the Web of Data, which gathers huge collections of structured data over distributed, heterogeneous data sources. Live queries are needed to get current information out of this global data space. In live query processing, source selection allows the identification of the sources that most likely contain relevant content. Due to the semantic heterogeneity of the Web of Data, however, it is not always easy to assess relevancy. Context information might help in interpreting the user\u2019s information needs. In this paper, we discuss how context information can be exploited to improve source selection

Recurring retrieval needs in diverse and dynamic dataspaces: Issues and reference framework

Processing information requests over heterogeneous data spaces is very challenging because aimed at guaranteeing user satisfaction with respect to conflicting requirements on result quality and response time. In [3], it has been argued that, in dynamic contexts pre-venting substantial user involvement in interpreting requests, information on similar requests recurring overtime can be exploited during query processing. In this paper, referring to a graph-based modeling of dataspaces and requests, we propose a preliminary approach in this direction centered on the enabling concept of Profiled Graph Query Pattern (PGQP) as an aggregation of information on past evaluations of a set of previously executed queries. The information maintained in PGQP is not query results, as in materialized queries, but can include different kinds of data and metadata

The role of geographic knowledge in sub-city level geolocation

Geolocation of microblog messages has been largely investigated in the literature. Many solutions have been proposed that achieve good results at the city level. Existing approaches are mainly data-driven (i.e., they rely on a training phase). However, the development of algorithms for geolocation at sub-city level is still an open problem. In this paper, we investigate the role that external geographic knowledge can play in geolocation approaches. We show how different geographical data sources can be combined with a semantic layer within a knowledge base to achieve reasonably accurate sub-city level geolocation

The Root Mycobiota of Betula aetnensis Raf., an Endemic Tree Species Colonizing the Lavas of Mt. Etna (Italy)

Betula aetnensis is an endemic tree of high conservation value, which thrives on the nutrientpoor volcanic soils of Mount Etna. Since plant–microbe interactions could play a crucial role in plant growth, resource uptake, and resistance to abiotic stresses, we aimed to characterize the root and rhizosphere microbial communities. Individuals from natural habitat (NAT) and forest nursery (NURS) were surveyed through microscopy observations and molecular tools: bacterial and fungal automated ribosomal intergenic spacer analysis (ARISA), fungal denaturing gradient gel electrophoresis (DGGE). B. aetnensis was found to be simultaneously colonized by arbuscular (AM), ectomycorrhizal (ECM), ericoid (ERM) fungi, and dark septate endophytes (DSE). A high diversity of the bacterial community was observed whilst the root fungal assemblage of NAT plants was richer than that of NURS. Root and rhizosphere fungal communities from NAT plants were characterized by Illumina MiSeq sequencing. Most of the identified sequences were affiliated to Helotiales, Pezizales, and Malasseziales. Ascomycota and Basidiomycota dominated roots and rhizosphere but differed in community structure and composition. ECM in the roots mainly belonged to Tylospora and Leccinum, while Rhizopogon was abundant in the rhizosphere. The Helotiales, including ERM (mostly Oidiodendron) and DSE (mostly Phialocephala), appeared the dominant component of the fungal community. B. aetnensis harbors an extraordinarily wide array of root-associated soil microorganisms, which are likely to be involved in the adaptation and resistance mechanisms to the extreme environmental conditions in volcano Etna. We argue that nursery-produced seedlings could lack the necessary microbiota for growth and development in natural conditions

Rapid and reliable MALDI-TOF mass spectrometry identification of Candida non-albicans isolates from bloodstream infections

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