Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Probabilistic modelling of single cell multi-omics data

Multicellular organisms possess a diverse set of cells exhibiting unique properties and function. Despite their physiology and role each cell owns the same copy of genetic in- structions encoded in its DNA. The ability of cells to differentiate into various shapes and forms stems from a careful orchestration of gene expression through various regulatory mechanisms. Recent developments in single cell multi-omics protocols offer unprecedented opportu- nities to simultaneously quantify phenomena in epigenome and gene expression at a single cell resolution. Advances in cell isolation and barcoding eliminated various confounding phenomena, shedding light into the regulatory role of epigenome in gene expression over diverse tissues and cells. Yet, combining omics modalities introduces serious statistical and computational challenges. Limitations of single-omics get exacerbated when combined into multi-modal assays, making result interpretation hard. In this thesis, we argue that inconsistent treatment of technical variability offered by classical statistical tools can corrupt statistical analyses and produce misleading results. In the Bayesian template, we introduce probabilistic models that explicitly and transparently decouple technical variability from biological signal. These methods are then used to investigate how epigenetic regulatory mechanisms interact with gene expression, both at genomic and at a cellular level. Single cell sequencing technologies are notoriously affected by high sparsity, leaving scientists to wonder if data are a product of sample handling or some genes are not expressed. As a result, even simple correlative tools (eg. Pearson’s correlation) seeking to identify regions with strong regulatory patterns between molecular layers routinely pinpoint a handful of associations. To overcome some of these limitations we introduce SCRaPL (Single Cell Regulatory Pattern Learning), a Bayesian hierarchical model to infer correlation between different omics components. SCRaPL’s uncertainty quantification allows for accurate results and good control over false positives, compared to its counterparts. Existing limitations force practitioners to partially or fully discard molecular modalities from cell observations, significantly under-powering subsequent downstream analysis. An alternative solution for scaling datasets is to post-experimentally address protocol limitations using a generative model. We introduce single cell Multi View Inference (scMVI), a deep learning model designed to accommodate analyses on both partially and fully observed data. Using jointly quantified data, scMVI builds a low-dimensional joint latent space by aligning omcis representations for each cell. In similar cells, scMVI can match individual modalities creating more complex sets. Subsequently, this manifold is used to approximate the data generating process. Hence, in partially quantified cells missing observations could be imputed getting the full potential of the data. To summarize, this thesis proposes novel statistical tools to interpret the regulatory interactions between epigenome and gene expression using data from modern multi-omics sequencing experiments. Their flexible design along with robust uncertainty quantification, allow these methods to unlock the immense potential of existing and future sequencing protocols. We hope that with the increased adoption in these methods, SCRaPL and scMVI will become an integral part of downstream analysis

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry

An Automatic Matcher and Linker for Transportation Datasets

Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging while being isolated from previously-existing networks. This leads them to publish their data sources to the web, according to linked data principles, in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detecting geospatial properties and mapping them between two different schemas. On the other hand, we propose a new interlinking approach that enables the user to define rich semantic links between datasets in a flexible and customizable way

An Automatic Matcher and Linker for Transportation Datasets

Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging while being isolated from previously-existing networks. This leads them to publish their data sources to the web, according to linked data principles, in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detecting geospatial properties and mapping them between two different schemas. On the other hand, we propose a new interlinking approach that enables the user to define rich semantic links between datasets in a flexible and customizable way