Statistical analysis of the owl:sameAs network for aligning concepts in the linking open data cloud

The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets. <br/

Intrinsic alignments of group and cluster galaxies in photometric surveys

Intrinsic alignments of galaxies have been shown to contaminate weak gravitational lensing observables on linear scales, $r>$ 10 $h^{-1}$Mpc, but studies of alignments in the non-linear regime have thus far been inconclusive. We present an estimator for extracting the intrinsic alignment signal of galaxies around stacked clusters of galaxies from multiband imaging data. Our estimator removes the contamination caused by galaxies that are gravitationally lensed by the clusters and scattered in redshift space due to photometric redshift uncertainties. It uses posterior probability distributions for the redshifts of the galaxies in the sample and it is easily extended to obtain the weak gravitational lensing signal while removing the intrinsic alignment contamination. We apply this algorithm to groups and clusters of galaxies identified in the Sloan Digital Sky Survey `Stripe 82' coadded imaging data over $\sim 150$ deg$^2$. We find that the intrinsic alignment signal around stacked clusters in the redshift range $0.1<z<0.4$ is consistent with zero. In terms of the tidal alignment model of Catelan et al. (2001), we set joint constraints on the strength of the alignment and the bias of the lensing groups and clusters on scales between 0.1 and $10\,h^{-1}$ Mpc, $b_LC_1\rho_{\rm crit} = -2_{-14}^{+14} \times 10^{-4}$. This constrains the contamination fraction of alignment to lensing signal to the range between $[-18,23]$ per cent below scales of 1 $h^{-1}$ Mpc at 95 per cent confidence level, and this result depends on our photometric redshift quality and selection criteria used to identify background galaxies. Our results are robust to the choice of photometric band in which the shapes are measured ($i$ and $r$) and to centring on the Brightest Cluster Galaxy or on the geometrical centre of the clusters.Comment: 30 pages, 16 figures, published in MNRA

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools

Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition

The SP theory of computing and cognition, described in previous publications, is an attractive model for intelligent databases because it provides a simple but versatile format for different kinds of knowledge, it has capabilities in artificial intelligence, and it can also function like established database models when that is required. This paper describes how the SP model can emulate other models used in database applications and compares the SP model with those other models. The artificial intelligence capabilities of the SP model are reviewed and its relationship with other artificial intelligence systems is described. Also considered are ways in which current prototypes may be translated into an 'industrial strength' working system