- Constructing Domain-specific Knowledge Graphs (KGC)
- How to Build a Stream Reasoning Application (StreamApp)
- HDT: Queryable Compression Format for Linked Data (HDT)
- Methods and Tools for Developing Ontology-Based Data Access Solutions (MT4OBDA)
- Semantic Data Management in Practices (SemDaPra)
- Semantic Web meets Internet of Things (IoT) and Web of Things (WoT) (SWoTIoT)
- Representation and Querying for Linked Geospatial Data (ReQuLGD)
Mayank Kejriwal, Pedro Szekely and Craig Knoblock
The vast amounts of ontologically unstructured information on the Web, including semi-structured HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to Semantic Web researchers if extracted robustly, efficiently and semi-automatically as an RDF knowledge graph. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This tutorial will synthesize and present KGC techniques, especially information extraction (IE) in a manner that is accessible to Semantic Web researchers. The presenters of the tutorial will use their experience as instructors and Semantic Web researchers, as well as lessons from actual IE implementations, to accomplish this purpose through visually intuitive and example-driven slides, accessible, high-level overviews of related work, instructor demos, and at least five IE participatory activities that attendees will be able to set up on their laptops.
Daniele Dell’Aglio, Emanuele Della Valle, Thu Le-Pham, Alessandra Mileo and Riccardo Tommasini
The goal of the tutorial “How to build a stream reasoning application” is threefold: to (1) present interesting research problems for Semantic Web that arise in querying and reasoning on a variety of highly dynamic data, (2) introduce stream reasoning techniques to Semantic Web researchers as powerful tools to use when addressing a data-centric problem characterised both by variety and velocity, and (3) to guide the participants through the construction of a stream reasoning application.
Wouter Beek, Ruben Verborgh and Javier D. Fernández
The steady adoption of Linked Data in recent years has led to a significant increase in the volume of RDF datasets. The potential of this Semantic Big Data is under-exploited when data management is based on traditional, human-readable RDF representations, which add unnecessary overheads when storing, exchanging and consuming RDF in the context of a large-scale and machine-understandable Semantic Web. HDT tackles this issue by proposing a binary representation for RDF data. HDT can be seen as a compressed, self-contained triple store for RDF. On the one hand, HDT represents RDF with compact data structures that enable the storage, parsing and loading of Big Semantic Data in compressed space. At the same time, “the HDT data are the index”, and thus it can be used as a graph data store that reports competitive querying performance. In this tutorial we will focus on providing a hands-on experience with HDT. We will also welcome external presentations on related topics and a discussion on next steps for the interested community.
Giuseppe De Giacomo, Domenico Lembo, Antonella Poggi, Valerio Santarelli and Domenico Fabio Savo
The tutorial illustrates methodologies for developing ontology-based data access (OBDA) applications, which aim at coupling conceptual views of information, expressed as Description Logic ontologies, with actual and possibly pre-existing data stores. In the tutorial we will present the basics of OBDA, introduce a graphical model for quick development of OWL 2 ontologies, survey typical mechanisms to link ontologies with data, and discuss some special reusable patterns for modeling recurrent representation needs. We will conduct an hands-on-session in which participants will develop (small) OBDA applications and will experiment OBDA functionalities, such as answering SPARQL queries, by exploiting state-of-the-art OBDA tools.
Olaf Hartig and Olivier Curé
After years of research and development, standards and technologies for semantic data are sufficiently mature to be used as the foundation of novel data science projects that employ semantic technologies in various application domains such as bio-informatics, materials science, criminal intelligence, and social science. Typically, such projects are carried out by domain experts who have a conceptual understanding of semantic technologies but lack the expertise to choose and to employ existing data management solutions for the semantic data in their project. For such experts, including domain-focused data scientists, project coordinators, and project engineers, our tutorial will deliver a practitioner’s guide to semantic data management. We will discuss the following important aspects of semantic data management and demonstrate how to address these aspects in practice by using mature, production-ready tools: Storing and querying semantic data; understanding, searching, and visualizing the data; automated reasoning; integrating external data and knowledge; and, cleaning the data.
Amelie Gyrard, Pankesh Patel, Soumya Kanti Datta, Maria Maleshkova and Muhammad Intizar Ali
An ever growing interest and wide adoption of Internet of Things (IoT) technologies is unleashing a true potential of designing a broad range of advanced consumer applications. Smart cities, smart buildings and e-health are among various application domains, which are benefiting from IoT technologies. Diversity, dynamicity and heterogeneity of IoT devices, networks and data are among major challenges hindering the wide adoption of IoT technologies. Semantic web technologies (SWT) have been effectively used in various domains, in particular to address the heterogeneity aspect. SWT allows (i) easing the IoT data representation \& management, (ii) deducing new knowledge to build smart applications and (iii) maintaining interoperability at IoT data processing level. We will familiarize our audience with the “evolution” of IoT to Web of things (WoT), which is based on existing Web standards. The combining of Semantic web technologies and Web of Things paves the way for the Semantic Web of Things. This tutorial will introduce the building blocks of the IoT and WoT that enable rapid development of semantic Web of Things applications. Towards that goal, the tutorial will also demonstrate how semantic web technologies are employed for semantic annotation and reasoning on the IoT data to build interoperable applications. One key aspect is helping IoT developers in dealing with semantic web technologies by reducing the learning curve as much as possible. We will showcase real-world use case scenarios which are designed using semantically enabled IoT frameworks (e.g. HIGHTS, CityPulse, FIESTA-IoT).
Manolis Koubarakis and Konstantina Bereta
The web of data has recently been populated with linked geospatial data as various geospatial data sources have been transformed into RDF and added to the linked data cloud (e.g., Geonames, Open Street Map, CORINE land cover etc.). Therefore, it is important to study how to represent geospatial data in RDF and how to query it using SPARQL. Researchers from the areas of Semantic Web and Linked Data have studied theses problems recently. The results of this research has been the development of geospatial extensions of RDF and SPARQL, and the implementation of geospatial RDF stores. In this tutorial, we present a comparative survey of current research in this area and point to directions for future work.