Tutorials

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. We will also present a query answering system over geospatial knowledge graphs, that allows non-experts to access linked geospatial data using natural language. The tutorial will give an in-depth coverage of the relevant techniques, systems and some applications developed by the presenters in the last 10 years in the context of 8 European projects (TELEIOS, LEO, Melodies, Optique, Big Data Europe, Copernicus App Lab, WDAQUA, ExtremeEarth), 1 ESA project (Prod-Trees) and 2 projects funded by the Greek government (SCARE and GeoQA). The two teams presenting the tutorial (National and Kapodistrian University of Athens and Technische Universität 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 University of Athens currently leads one of the most important European research projects in the research areas relevant to this tutorial, the ExtremeEarth project (http://earthanalytics.eu/).

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. It is designed to address the needs and fit the expertise of domain experts, ontology engineers, and data managers in creating and maintaining large knowledge bases. The tutorial will give an introduction to the OTTR framework, discuss modelling best practices for sustainable knowledge bases using templates, and demonstrate practical use of the framework’s software API. 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 size of knowledge graphs has reached the scale where centralised analytical approaches have become infeasible. Recent technological progress has enabled tools for powerful distributed in-memory analytics that have been shown to work well on elementary data structures but they are not specialised for knowledge graph (KG) processing. Scalable Semantic Analytics Stack (SANSA) is a library built on top of one such tool, Apache Spark, and it offers several APIs covering different facets of scalable KG processing. SANSA is organized into several layers: (1) RDF data handling e.g. filtering, computation of RDF statistics, and quality assessment (2) SPARQL querying (3) inference reasoning (4) analytics over KGs. In addition to processing native RDF, SANSA also allows users to query a wide range of heterogeneous data sources (e.g. files stored in Hadoop or other popular NoSQL stores) uniformly using SPARQL. This tutorial aims to provide an up to date overview of the stack, together with detailed discussions on the previous releases, technical add-ons and developments. Furthermore, a hands-on session on SANSA, covering all the aforementioned layers using simple use-cases will be provided.

The success of Semantic Web technology has boosted the publication of Knowledge Graphs, and several technologies to access them have become available covering different spots in the spectrum of expressivity: from the highly expressive SPARQL to the controlled access of Linked Data APIs, with GraphQL in between. Many of these technologies have reached industry-grade maturity. Finding the trade-offs between them is often difficult in the daily work of developers, interested in quick API deployment and easy data ingestion. In this tutorial, we will cover this in-between technology space, with the main goal of providing strategies and tools for publishing Web APIs that ensure the easy consumption of data coming from SPARQL endpoints. Together with an overview of state-of-art technologies, the tutorial focuses on two novel technologies: SPARQL Transformer, which allows to get a more compact JSON structure for SPARQL results, decreasing the effort required by developers in interfacing JavaScript and Python applications; and grlc, an automatic way of building APIs on top of SPARQL endpoints by sharing queries on collaborative platforms. Moreover, we will present recent developments to combine the two, offering a complete resource for developers and researchers. Hands-on sessions will be proposed to internalize those concepts with practice.

Despite the emergence of RDF knowledge bases, exposed via SPARQL endpoints or as Linked Data, formats like CSV, JSON or XML are still the most used for exposing data on the web. Some solutions have been proposed to describe and integrate these resources using declarative mapping languages (e.g. RML, CSVW, KR2RML, etc) and many of those are equipped with associated RDF generators (e.g. RMLMapper, CSVW generator, etc). The use of these technologies enables the construction of knowledge graphs in a declarative way. However, they have a steep learning curve for new users. Our aim in this tutorial is, from a practical perspective, to explain in detail the process of constructing knowledge graphs, from writing mappings to their use with suitable tools. First, we describe the mapping structure and a tool to ease writing mappings, Mapeathor, showing the main guidelines for attendants to create their own mappings. Then, we present Morph-CSV, a framework for virtual knowledge graph access over tabular data. Finally, we present HELIO, a Linked Data publisher that provides unified access in real-time to multiple heterogeneous data sources.

Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths and weaknesses. Our tutorial will survey the most important commonsense knowledge resources, and introduce a new commonsense knowledge graph (CSKG) to integrate several existing resources. The tutorial will also introduce several tools to work with CSKG including query mechanisms, knowledge graph embeddings, and a framework to create a commonsense question answering systems. In a hands-on session, participants will use the framework and tools to build a question answering application using CSKG and language models.

Mobile devices have become ubiquitous in today’s society, with functionality ranging from straightforward calendars and messengers to sophisticated mHealth and augmented reality apps. In this half-day tutorial, we will introduce Semantic Web and Linked Data practitioners to the world of mobile app building through Punya, a drag-and-drop visual programming environment based on MIT App Inventor that incorporates Jena and other semantic technologies. The goal of Punya is to democratize the ability to ​consume​, ​produce​, and ​act on Linked Data. Participants in this tutorial will gain knowledge of how to build their own apps leveraging JSON-LD to integrate Linked Data from the web. No prior knowledge on mobile app development will be required. The first portion of the tutorial focuses on quickly prototyping mobile apps using traditional create, read, update, and delete actions on a Linked Data repository. The second part will explore newer distributed Linked Data technologies, such as integration with the SOLID platform, device-to-device interaction via the Internet and Web of Things, and lightweight reasoning through technologies like OWL 2 RL. Tutorial attendees will increase their knowledge and skills of (linked) data-driven mobile app development and a greater sense of how to build distributed apps that leverage Linked Data.

The purpose of this tutorial is to provide an brief introduction of semantic explainability and to make the audience familiar to the use of an end-to-end platform supporting the generation of natural language recommendations concerning the self-monitoring of people behaviors. The content of this tutorial can be applied to many domains but for making this event feasible to be performed during a half day timespan, we will focus on the healthcare one. In particular, we will show how to set up a semantic explainability platform for supporting the generation of food advises. The tutorial purpose is to teach how to use the services provided by the HORUS.AI platform. This is an AI-based system built upon the integration of semantic web technologies and persuasive techniques for motivating people to adopt healthy lifestyle or for supporting them to cope with the self-management of chronic diseases. The system collects data from users’ devices, explicit users’ inputs, or from the external environment and interacts with users by using a goal-based metaphor. The attendants will master all the Web APIs provided by the platform in order to build healthcare applications based on HORUS.AI.

Building and hosting a Knowledge Graph requires some effort and a lot of experience in semantic technologies. Turning this Knowledge Graph into a useful resource for problem solving requires even more effort. An important consideration is to provide cost‐sensitive methods to build a Knowledge Graph that is a useful resource for various applications: “There are two main goals of Knowledge Graph refinement: (a) adding missing knowledge to the graph, i.e., completion, and (b) identifying wrong information in the graph, i.e. error detection.” (Paulheim et al. 2017) This tutorial is targeting the process from knowledge creation over knowledge hosting, knowledge curation to knowledge deployment – applied to a Knowledge Graph using schema.org and domain specific extensions of schema.org as an ontology. The tutorial will be based on a book the lecturers co-authored: “Knowledge Graphs – Methodology, Tools and Selected Use Cases” (Fensel et al. 2020) and is an extended and adapted version of a tutorial the lecturers gave at SEMANTICS2019.

Two technologies have been proposed for RDF validation based on the notion of shape: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). The adoption of both languages in different semantic web applications and domains has been gradually increasing since their appearance and they are claiming their space in the semantic web stack. ShEx was designed as a concise, human-readable language for RDF description and validation, while SHACL was designed as an RDF vocabulary that allows to describe constraints on RDF data. Although both have some similarities, they were designed from different perspectives. In this tutorial we will present both ShEx and SHACL using examples, discuss the rationales for their design and compare them. We will also present some tools that are being developed around shapes, as well as some applications like the wikidata entity schema namespace which allows to create an ecosystem of shape expressions to validate wikidata entities.