Tutorials at ISWC 2019
Ruben Taelman, Joachim Van Herwegen, Miel Vander Sande and Ruben Verborgh
Solid is a decentralized application and storage ecosystem, built on the Linked Data principles. It enables true data ownership, where anyone can store data in a place of their choice, regardless of the applications they use. In order to build decentralized applications over Solid data pods, specialized querying techniques are required. Comunica is a flexible meta query engine that offers such querying capabilities. In order to enable developers and researchers to start building such decentralized applications, we offer a tutorial on Solid and Comunica. This tutorial consists of an overview of Solid, and on how Comunica can be used to query Solid data pods. As a result, participants from different backgrounds will have experience with hosting data through their own personal Solid data pod. Furthermore, they will be able to query over Solid data pods, using different querying techniques with Comunica. Ultimately, this will enable the development and research of decentralized applications with the Solid platform.
Practical and Scalable Pattern-based Ontology Engineering for Industry with Reasonable Ontology Templates
Martin G. Skjæveland, Johan W. Klüwer, Melinda Hodkiewicz, Leif Harald Karlsen and Daniel P. Lupp
A major barrier for the adoption of semantic web technologies in industry is the construction of sustainable knowledge bases; domain experts and end-users often find semantic web languages and tools difficult to use. Reasonable Ontology Templates (OTTR) is a language and framework that allows abstractions or modelling patterns over RDF/OWL to be succinctly represented and instantiated. This supports sound modelling principles by allowing the design of the knowledge base to be represented as compositional OTTR templates carefully curated by ontology experts, while the bulk content of the knowledge base is provided as structurally simple template instances. Through a mix of lectures, plenary and individual hands-on exercises, the tutorial will introduce OTTR templates and explain how to represent modelling patterns and use them to build and interact with knowledge bases. We will also show how logic-based methods can be used to maintain a library of templates, finding new patterns and discovering redundancies. Use-cases and examples will be taken from ongoing industrial projects. The tutorial is relevant for semantic web practitioners and ontology engineers who are eager to make efficient use of modelling patterns in their work, and for information managers from industry looking for possible ways to introduce ontology development into their enterprise.
The GraphQL framework offers a query-based Web interface over a graph-based data structure that may be composed of various types of underlying data sources. GraphQL is highly popular among Web developers, and a lot of developer-friendly tooling is available. While there are several similarities to related techniques used in the Semantic Web context, the actual relationships are not yet well explored. In order to uncover the potential of GraphQL for the Semantic Web community and vice versa, we offer a tutorial in which we introduce GraphQL. The tutorial gives a detailed overview of the different concepts and techniques within the GraphQL framework; the tutorial also contains an introductory hands-on part focused on writing queries in the GraphQL language and interacting with its data model. As a result, attendees will obtain a basic set of knowledge and skills that will allow them to apply GraphQL and to do research related to GraphQL.
Linked Data allows meaningful links to be created between pieces of data on the Web. Adoption of Linked Data technologies has shifted the Web from a space of connecting documents to a global space where pieces of data from different domains are semantically linked and integrated to create a globalWeb of Data. While small amounts of Linked Data can be handled in-memory or by standard relational database systems, big Linked Data graphs, which we nowadays have to deal with, are very hard to manage. Modern Linked Data management systems have to face large amounts of heterogeneous, inconsistent, and schema-free data. The aim of this tutorial is to provide a comprehensive overview of the state-of-the-art in linked data storage techniques, static/streaming query processing mechanisms, scalable reasoning approaches, and benchmarking.
The web and the semantic web are the most significant cases of environments where the information is distributed. In parallel, in the recent year we observed the rise of blockchain as a way to distribute assets and trust. It is therefore normal to ask ourselves: how are semantic web and blockchain different? Can they complement each other? In this tutorial, we want to analyse such questions from the semantic web perspective, with two goals: (i) understand how blockchain research can help semantic web to address its open challenges (e.g. trust and identity management), and (ii) understand how semantic web research can contribute to the blockchain development (e.g. annotations and data integration). The tutorial will target semantic web researchers with limited knowledge about blockchain: we will introduce the main concepts behind blockchain, and present successful use cases. Next, we will focus on the overlap between blockchain and semantic web, with an overview of ongoing research and results. Attendees will put in practice what they learned through hands-on sessions and demos. Finally, the tutorial will provide a space for discussion about future research directions.
Manolis Koubarakis, Begüm Demir, George Stamoulis, Konstantina Bereta and Despina-Athanasia Pantazi
The research areas of Remote Sensing, Big Data, Linked Data, Ontologies, Spatiotemporal Data and Deep Learning are very crucial for Data Science for satellite data. The tutorial will start by explaining what satellite data is and why satellite data is a paradigmatic case of big spatiotemporal data giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. Examples of big satellite data, information and knowledge will be given for the case of the Copernicus programme of the European Union. We will teach the tutorial participants how to “break satellite data silos open” by publishing the metadata of satellite datasets as microformats to enable their discovery by modern search engines through services like Dataset Search of Google, how to extract important geospatial information from satellite datasets using deep learning technologies, how to interlink this information with other relevant information available on the Web, and how to make this wealth of data and information freely available on the Web to enable the easy development of geospatial applications. We will present a complete data science pipeline that starts with satellite datasets in various formats that are made freely available in the archives of space agencies, and ends with the deployment of an interactive visual application that uses satellite data utilizing linked data technologies. The tutorial will give an in-depth coverage of the relevant techniques, systems and some applications developed by the presenters in the last 8 years in the context of 5 European projects (TELEIOS, LEO, Melodies, Optique, Copernicus App Lab). The two teams presenting the tutorial (National and Kapodistrian University of Athens and Technical University of Berlin) come from different disciplines (Computer Science and Satellite Remote Sensing) and will offer an interdisciplinary presentation of the relevant theoretical and practical issues. The two teams currently lead the two most important European research projects in the research areas relevant to this tutorial: BigEarth, ExtremeEarth. The Big Earth project is the prestigious European Research Council Starting Grant to Prof. Demir.
Scientific reproducibility is in crisis in multiple disciplines, e.g., social sciences, natural sciences and biomedical research among many others. This crisis has been highlighted by a growing number of community and government initiatives, for example the European Union Open Research Data, the US National Institutes of Health (NIH) “Rigor and Reproducibility” guidelines, Research Data Alliance (RDA), Force11, and DataONE projects. Semantic web, together with provenance, software and method metadata play a central role in facilitating scientific reproducibility. The objective of this tutorial is to provide a landscape of the tools, standards and guidelines that an author should follow to make their work (i.e., data, software, methods, provenance and context) reproducible. – The first part of the tutorial will introduce a framework for different levels of reproducibility desired in scientific research and underline the challenges involved in achieving each of them. – In the second part, we will describe the role of semantic web standards, including the W3C PROV specifications in scientific reproducibility. – Finally, in the third part of the tutorial we will present real world examples of provenance-enabled scientific reproducibility projects. For example, the ProvCaRe project with the largest repository of semantic provenance extracted from biomedical literature for evaluating reproducibility, OntoSoft for tracking software metadata, Research Objects for packaging and annotating scientific research outputs, and W2Share for converting scripts into reproducible workflows. The SPSR tutorial will conclude with a discussion of open challenges in scientific reproducibility and the potential role of semantic web research in addressing these challenges.
Knowledge Graphs: How did we get here? A Half Day Tutorial on the History of Knowledge Graph’s Main Ideas
Juan F. Sequeda and Claudio Gutierrez
Knowledge Graphs can be considered the fulfilling of an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learning, among others, Knowledge Graphs have rapidly gain popularity in academia and industry in the past 5 years. The integration of such disparate disciplines and techniques give the richness to Knowledge Graphs, but also present the challenge to practitioners and theoreticians to know how current advances develop from early techniques in order, on one hand, take full advantage of them, and on the other, avoid reinventing the wheel. This tutorial will provide a historical context on the roots of Knowledge Graphs grounded in the advancements of Logic, Data and the combination thereof.