A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration

This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data. The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the ‘Best Student Paper Award’)

Best Practices for Publishing, Retrieving, and Using Spatial Data on the Web

Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. With the rapid growth of connected embedded devices, GPS-enabled mobile devices, and various organizations that publish their location-based data (i.e., weather and traffic services), maps and geographical and spatial information (i.e., GIS and open maps), spatial data on the Web is becoming ubiquitous and voluminous. However, the heterogeneity of the available spatial data, as well as some challenges related to spatial data in particular make it difficult for data users, web applications and services to discover, interpret and use the information in large and distributed web systems. This paper summarizes some of the efforts that have been undertaken in the joint W3C/OGC Working Group on Spatial Data on the Web, in particular the effort to describe the best practices for publishing spatial data on the Web. This paper presents the set of principles that guide the selection of these best practices, describes best practices that are employed to enable publishing, discovery and retrieving (querying) this type of data on the Web, and identifies some areas where a best practice has not yet emerged

Charting Past, Present, and Future Research in the Semantic Web and Interoperability

Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field. View Full-Tex

Responsible Data Governance of Neuroscience Big Data

Open access article.Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of “responsible data governance,” applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP)

Proposing a Methodology for Designing an Enterprise Knowledge Graph to Ensure Interoperability Between Heterogeneous Data Sources

Ο κύριος ερευνητικός στόχος αυτής της διπλωματικής εργασίας είναι να προτείνει μια προσέγγιση για το σχεδιασμό ενός Γνωσιακού Γράφου Επιχειρήσεων (EKG) για τη διασφάλιση της διαλειτουργικότητας μεταξύ διαφορετικών ετερογενών πηγών, λαμβάνοντας υπόψη τις ήδη υπάρχουσες προσπάθειες και διαδικασίες αυτοματισμού που αναπτύχθηκαν από την ENGIE στην προσπάθειά τους να δημιουργήσουν ένα Γνωσιακό Γράφο ειδικού σκοπού. Για την επίτευξη αυτού του στόχου, απαιτείται η βαθιά κατανόηση των ήδη υπαρχόντων σύγχρονων προσεγγίσεων EKG, των τεχνολογιών τους με έμφαση στη μετατροπή δεδομένων και στις μεθόδους επερώτησης και τέλος η συγκριτική παρουσίαση τυχόν νέων ευρημάτων σε αυτή τη νέα πρόκληση καθορισμού ενός EKG. Τα κριτήρια αξιολόγησης των διαφόρων διαδικασιών έχουν αποφασιστεί με τρόπο που να καλύπτει τις ακόλουθες ερωτήσεις. (i) Ποιες είναι οι επιπτώσεις και τα πρακτικά αποτελέσματα των διαφορετικών στρατηγικών σχεδιασμού για τον ορισμό ενός EKG; (ii) Πώς αυτές οι στρατηγικές επηρεάζουν τη σημασιολογική πολυπλοκότητα και μειώνουν ή αυξάνουν την απόδοση; (iii) Είναι δυνατόν να διατηρηθεί χαμηλά η καθυστέρηση και να έχουμε μόνιμες ενημερώσεις; Επιπλέον, η εργασία μας περιορίστηκε σε ένα σενάριο χρήσης που ορίστηκε από την ENGIE για να διερευνήσει τα ανοιχτά δεδομένα ατυχημάτων και τα δεδομένα του οδικού χάρτη ως μια αφετηρία στην κατασκευή ενός EKG. Θα πειραματιστούμε με τον μετασχηματισμό δεδομένων από ετερογενείς πηγές δεδομένων σε μία τελική ενιαία συλλογή δεδομένων (RDF) έτοιμη να χρησιμοποιηθεί ως τη βάση του Γνωσιακού Γράφου. Στη συνέχεια, θα παρουσιάσουμε τις τεχνικές προκλήσεις, το λεξιλόγιο και τις μεθόδους που χρησιμοποιούνται για την επίλυση του προβλήματος ορισμού ενός EKG. Τέλος, ένας παράλληλος στόχος της διπλωματικής εργασίας είναι η πρακτική δοκιμή και σύγκριση τεχνικών μεθόδων για την ενσωμάτωση, τον εμπλουτισμό και τον μετασχηματισμό δεδομένων. Το σημαντικότερο για εμάς, είναι ότι θα δοκιμάσουμε την ικανότητα επερωτήσεων γεωχωρικών πληροφοριών που αποτελούν βασικό στοιχείο για αυτό το Γνωσιακό Γράφο ειδικού σκοπού. Σε αυτήν την εργασία, παρουσιάζουμε τις διαφορετικές υλοποιήσεις των RDF stores που υποστηρίζουν μια γλώσσα γεωγραφικών επερωτήσεων για δεδομένα RDF (GeoSPARQL), ένα πρότυπο του W3C για γεωγραφική και σημασιολογική αναπαράσταση γεωγραφικών δεδομένων. Επιπλέον, δημιουργήθηκαν δοκιμαστικά δεδομένα, τα οποία συγκρίνουν τον μετασχηματισμό και τη σύνδεση διαφορετικών δεδομένων, αξιοποιώντας τις αρχές του Σημασιολογικού Ιστού.The main research goal of this thesis is to propose an approach for designing an Enterprise Knowledge Graph (EKG) to ensure interoperability between different heterogeneous sources, taking into account the already existing efforts and automation processes developed by ENGIE in their attempt to build a domain-based EKG. Reaching this goal, demands a deep understanding of the already existing state-of-the-art on EKG approaches, their technologies with a focus on data-transformation and query methods and finally a comparative presentation of any new findings in this new challenge of defining an end-to-end formula for EKG construction. The criteria of evaluating the different works have been decided in a way to cover the following questions. (i) Which are the Implications and practical expectations of different design strategies to realize an EKG? (ii) How do those strategies affect semantic complexity and decrease or increase performance? (iii) Is it possible to maintain low latency and permanent updates? Furthermore, our work was limited to one use case defined by ENGIE to explore the open data of accident and the data of road map as a starting point experience in EKG construction. We shall experiment with data transformation from heterogenous data sources into a final unified RDF datastore ready to be used as the foundation of an EKG. After, we are going to present the technical challenges, the vocabulary and the methods used to achieve a solution to the EKG definition problem. Finally, a side goal of the thesis is to practically test and compare technical methods for data integration, enrichment and transformation. Most importantly we are going to test the ability to query Geospatial information which is a key element for this domain-based EKG. In this work, we are presenting the different implementations of RDF stores which support a Geographic Query Language for RDF Data (GeoSPARQL), a W3C standard for geo-related and Semantic representation of geographical data. Furthermore, we have formed our test data as a subset coming from ENGIE's big data. Our test data have been put and benchmarked against the knowledge transformation and linkage phases, using state-of-the-art Semantic tools

A provenance-based semantic approach to support understandability, reproducibility, and reuse of scientific experiments

Understandability and reproducibility of scientific results are vital in every field of science. Several reproducibility measures are being taken to make the data used in the publications findable and accessible. However, there are many challenges faced by scientists from the beginning of an experiment to the end in particular for data management. The explosive growth of heterogeneous research data and understanding how this data has been derived is one of the research problems faced in this context. Interlinking the data, the steps and the results from the computational and non-computational processes of a scientific experiment is important for the reproducibility. We introduce the notion of end-to-end provenance management'' of scientific experiments to help scientists understand and reproduce the experimental results. The main contributions of this thesis are: (1) We propose a provenance modelREPRODUCE-ME'' to describe the scientific experiments using semantic web technologies by extending existing standards. (2) We study computational reproducibility and important aspects required to achieve it. (3) Taking into account the REPRODUCE-ME provenance model and the study on computational reproducibility, we introduce our tool, ProvBook, which is designed and developed to demonstrate computational reproducibility. It provides features to capture and store provenance of Jupyter notebooks and helps scientists to compare and track their results of different executions. (4) We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility) for the end-to-end provenance management. This collaborative framework allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational steps in an interoperable way. We apply our contributions to a set of scientific experiments in microscopy research projects