|Tutorial: Linked Data and Music Encodings|
|Tutorial: Knowledge Graph Construction|
Tutorial: Tutorial on Linked Data and Music Encodings
Organizers: David M. Weigl (University of Music and Performing Arts, Vienna, Austria), Stefan Münnich (University of Basel, Switzerland)
Music encoding, the representation of symbolic music information in a machine-accessible form, is critical to a variety of fields and areas of study, including computational or digital musicology, digital editions, symbolic music information retrieval, and digital libraries. The Linked Data Interest Group of the Music Encoding Initiative (MEI) brings together music encoding specialists with experts in Web science and knowledge organization and regularly organizes training events focusing on applications of Linked Data to music encodings.
We propose a full-day tutorial for ESWC 2022 describing the application of semantic technologies to the domain of music encoding. The tutorial is aimed at members of the Semantic Web and Linked Data community with an interest in music, and does not require participants to have previous knowledge of music encoding. During the session, we will provide a high-level overview of music encoding technologies, briefly covering their history and purpose, some terminology, and relevant applications of Linked Data and Semantic Web approaches in this context. Real-life examples and hands-on experience with exercises in interlinking, querying, and annotating various music-related datasets (e.g., RISM, DOREMUS, JazzCats) will guide participants through the sessions.
Tutorial: Knowledge Graph Construction
Organizers: DKG Cost Action and W3C Community Group on Knowledge Graph Construction
Despite the emergence of knowledge graphs, exposed via 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, R2RML, etc) and many of those are equipped with associated RDF generators (e.g. RMLMapper, SDM-RDFizer, FunMap, 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. The aim of 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. From the basic features of mapping languages to the most complex and optimized engines that parse those rules, we take a trip through the most recent history on declarative construction of knolwedge graphs from heterogeneous data.
Workshop: 1st International Workshop on Semantic Industrial Information Modelling (SemIIM)
Organizers: Arild Waaler, Evgeny Kharlamov, Baifan Zhou, Dongzhuoran Zhou
Information Modelling (IM) has been under the spotlight of both academia and industry for decades. Important aspects of IM include methods and practices of representing concepts, relationships, constraints, rules and operations to specify data semantics for a chosen domain of interest. As a response to the IM challenge a number of modelling paradigms and languages arose and they range from ERM, UML, ORM to OWL and Knowledge Graphs and come with a wide range of systems to support the life cycle of information models.
Despite the past success, existing approaches and systems for IM fail to cope with new challenges of overwhelming global industrial digitalization that requires advanced information models and aims at fully computerized, software-driven, automation of production processes and enterprise-wide integration of software components. Such trend and the technological and industrial developments that come with it are an important part of Industry 4.0 and industrial Internet of Things. It requires IM that, for example, allows to capture the functionality of and information flow between different assets in a plant, such as equipment and production processes. Moreover, it requires IM and models that are based on ISA and IEC standards and have a number of desirable properties, e.g., reusable, explainable, scalable, simulatable etc.
These new challenges require new theory, methodology, best practice, systems and this should be developed, shared, and discussed by a wire range of stakeholders. In this workshop we aim at gathering researchers and practitioners who work on addressing these challenges with the help of semantic technologies. We in particular invite IM experts who are excited and committed to push the frontiers of IM further and support modern industry in its current technological transformation. In our workshop we welcome novel methods, systems, solutions, experience, and practice for semantic industrial information modelling.
Workshop: Third International Workshop On Knowledge Graph Construction (KGCW)
Organizers: Anastasia Dimou (Assistant Professor, KU Leuven), David Chaves Fraga (Senior Researcher, KU Leuven & UPM), Freddy Priyatna (Knowledge Graph Engineer, Olive AI), Juan Sequeda (Principal Scientist, data.world), Pieter Heyvaert (Senior Researcher, imec – IDLab (UGent))
More and more knowledge graphs are constructed for private use, e.g., Siri, Alexa, or public use, e.g., DBpedia, Wikidata. While techniques to automatically construct KGs from existing Web objects exist (e.g., semantic labeling of Web tables), there is still room for improvement. Initially, constructing KGs from existing datasets was considered an engineering task with some ad-hoc approaches, however, scientific methods with more declarative-oriented techniques have recently emerged. In particular, several mapping languages for describing rules to construct knowledge graphs and processors to execute those rules emerged. Addressing the challenges related to KG construction requires both the investigation of theoretical concepts and the development of tools and methods for their evaluation
KGCW22 has a special focus this time on the automatization of knowledge graph construction methods, analyzing their alignment with previous standard but declarative approaches (i.e., mapping rules).
The workshop includes a keynote and a panel, as well as (research, in-use, experience, position, tools) paper presentations, demo jam and break-out discussions.
Our goal is to provide a venue for scientific discourse, systematic analysis and rigorous evaluation of languages, techniques and tools, as well as practical and applied experiences and lessons-learned for constructing knowledge graphs from academia and industry. The workshop complements and aligns with the activities of the W3C CG on KG construction.
Workshop: 5th International Workshop on Geospatial Linked Data (GeoLD)
Organizers: Timo Homburg (i3mainz — Institute for Spatial Information Surveying Technology, Mainz University Of Applied Sciences, Germany), Dr. Beyza Yaman (ADAPT Centre, Trinity College Dublin, Ireland), Dr. Mohamed Ahmed Sherif (University of Paderborn, Germany), Assoc. Prof. Dr. Armin Haller (Australian National University, Australia)
Geospatial data is vital for both traditional applications like navigation, logistics, and tourism and emerging areas like autonomous vehicles, smart buildings and GIS on demand. Spatial linked data has recently transitioned from experimental prototypes to national infrastructure. However the next generation of spatial knowledge graphs will integrate multiple spatial datasets with the large number of general datasets that contain some geospatial references (e.g., DBpedia, Wikidata). This integration, either on the public Web or within organizations has immense socio-economic as well as academic benefits. The upsurge in Linked data related presentations in the recent Eurogeographics data quality workshop shows the deep interest in Geospatial Linked Data (GLD) in national mapping agencies. GLD enables a web-based, interoperable geospatial infrastructure. This is especially relevant for delivering the INSPIRE directive in Europe. Moreover, geospatial information systems benefit from Linked Data principles in building the next generation of spatial data applications e.g., federated smart buildings, self-piloted vehicles, delivery drones or automated local authority services.
This workshop invites papers covering the challenges and solutions for handling with GLD, especially for building high quality, adaptable, geospatial infrastructures and next-generation spatial applications. We aim to demonstrate the latest approaches and implementations and to discuss the solutions to challenges and issues arising from research and industrial organizations.
Workshop: 1st Workshop on Modular Knowledge (ModularK)
Organizers: Loris Bozzato (Fondazione Bruno Kessler, Italy, Valentina Anita Carriero (University of Bologna, Italy, Torsten Hahmann (University of Maine, USA), Antoine Zimmermann (École des Mines de Saint-Étienne, France)
The dramatic increase in the amount of open and linked data and the increasing semantification of such data make clear that knowledge is not monolithic, static or uniform, and that there is a need of methods and tools for dealing with heterogeneous and distributed knowledge as a constellation of modules.
The Modular Knowledge workshop offers an interdisciplinary venue for discussing and developing solutions for modularity of knowledge.
The workshop combines the efforts of previous experiences (like WoMO, ARCOE-Logic and WOMoCoE workshops) into an interdisciplinary venue for discussing and developing solutions for modularity of knowledge.
Modular Knowledge 2022 aims to cover and establish connections between various approaches (ranging from rich semantic representations, like Knowledge Graphs and formal ontology, to simpler schemas, like RDF and database schemas) for representing knowledge, its context, its evolution, and for making it accessible to automatic reasoning and knowledge management tasks. We welcome approaches that make use of logic-based, subsymbolic, or numerical representations.
Workshop: 10th Linked Data in Architecture and Construction Workshop (LDAC)
Organizers: Mehwish Alam (FIZ Karlsruhe – Leibniz Institute for Information Infrastructure (DE)), Anastasia Dimou (KU Leuven (BE)), Pieter Pauwels (Eindhoven University of Technology), María Poveda Villalón (Universidad Politécnica de Madrid), Walter Terkaj (Consiglio Nazionale delle Ricerche (CNR))
The LDAC workshop series provides a focused overview on technical and applied research on the usage of semantic web, linked data and web of data technologies for architecture and construction (design, engineering, construction, operation, etc.). The workshop aims at gathering researchers, industry stakeholders, and standardization bodies of the broader Linked Building Data (LBD) community. The aim of the workshop is to present current developments, coordinate efforts, gather stakeholders, and elaborate practical insights from industry.
Workshop: 7th Natural Language Interfaces for the Web of Data (NLIWOD+QALD)
Organizers: Ricardo Usbeck (Universität Hamburg, Germany (Chair)), Meriem Beloucif (Universität Hamburg, Germany (Chair)), Chris Biemann (Universität Hamburg, Germany (Chair)), Axel-Cyrille Ngonga Ngomo (DICE research group, Paderborn University, Germany)
The NLIWOD workshop focuses on the advancement of Natural Language (NL) Interfaces to the Web of Data. The workshop has been organized four times within ISWC, with a focus on soliciting discussions on the development of question answering systems, chatbots, and other NL techniques. It is a yearly highlight for the active NL interface community centered around semantic technologies.
The NLIWOD workshop attracts people from academia as well as from industry to promote active collaboration and discussion, to extend the scope of currently addressed topics, and to foster the reuse of resources developed so far. We want to broaden the scope of this workshop series to dialogue systems and chatbots as increasingly important business intelligence factors.
The primary goal of the NLIWOD workshop is to bring together experts on the use of natural-language interfaces (NLI) for answering questions, especially over the Web of Data.
Workshop: Third International Workshop On Semantic Digital Twins (SeDIT)
Organizers: Raúl García-Castro (Associate Professor, UPM), John Davies (Chief Researcher, BT)
The concept of digital twins, as virtual replicas of physical entities, has gained significant traction in recent years in a range of domains such as industry, construction, energy, health or transport. Digital Twins can be used to view the status of the twinned physical object, without the need to interrogate the object itself. The digital twin can be queried by other software without the need to query the device itself thus relieving pressure on devices, which typically have very limited computational capabilities. Digital twins can also be used for monitoring and diagnostics to optimize device performance without impacting on the physical device.
Digital twins require unambiguous descriptions of both the entity and its digital counterpart, as well as the ability to integrate data from heterogeneous sources of information (including real-time data) and to interact with the physical world. Given these requirements, semantic technologies can play a significant role in the real-world deployment of digital twin technology.
The aims of the SeDIT workshop are twofold. Firstly, to drive the discussion about current trends and future challenges of semantic digital twins. Secondly, to support communication and collaboration with the goal of aligning the various efforts within the community and accelerating innovation in the associated fields.
Workshop: International Workshop on Knowledge Graph Generation from Text (Text2KG)
Organizers: Sanju Tiwari (Universidad Autonoma de Tamaulipas, Mexico), Nandana Mihindukulasooriya (MIT-IBM Watson AI Lab, USA), Francesco Osborne (KMi, The Open University), Dimitris Kontokostas (Diffbot, Greece), Jennifer D’Souza (TIB, Germany), Mayank Kejriwal (University of Southern California), Amit Sheth (AIISC, University of South Carolina), Joey Yip (AIISC, University of South Carolina)
Knowledge Graphs are getting traction in both academia and in the industry as one of the key elements of AI applications. They are being recognized as an important and essential resource in many downstream tasks such as question answering, recommendation, personal assistants, business analytics, business automation, etc. Even though there are large knowledge graphs built with crowdsourcing such as Wikidata or using semi-structured data such as DBpedia or Yago or from structured data such as relational databases, building knowledge graphs from text corpora still remains an open challenge.
The workshop welcomes a broad range of papers including full research papers, negative results, position papers, dataset, and system demos examining the wide range of issues and processes related to knowledge graphs generation from text corpora including, but not limited to entity linking, relation extraction, knowledge representation, and Semantic Web. Papers on resources (methods, tools, benchmarks, libraries, datasets) are also welcomed.
Workshop: 3rd International Workshop on Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP)
Organizers: Sarra Ben Abbès (Engie, France), Rim Hantach (Engie, France), Philippe Calvez (Engie, France)
In recent years, deep learning has been applied successfully and achieved state-of-the-art performance in a variety of domains, such as image analysis. Despite this success, deep learning models remain hard to analyze data and understand what knowledge is represented in them, and how they generate decisions.
Deep Learning (DL) meets Natural Language Processing (NLP) to solve human language problems for further applications, such as information extraction, machine translation, search, and summarization. Previous works have attested the positive impact of domain knowledge on data analysis and vice versa, for example pre-processing data, searching data, redundancy and inconsistency data, knowledge engineering, domain concepts, and relationships extraction, etc. Ontology is a structured knowledge representation that facilitates data access (data sharing and reuse) and assists the DL process as well. DL meets recent ontologies and tries to model data representations with many layers of non-linear transformations.
This workshop aims at demonstrating recent and future advances in semantic rich deep learning by using Semantic Web and NLP techniques which can reduce the semantic gap between the data, applications, machine learning process, in order to obtain semantic-aware approaches. In addition, the goal of this workshop is to bring together an area for experts from industry, science, and academia to exchange ideas and discuss the results of ongoing research in natural language processing, structured knowledge, and deep learning approaches.
Workshop: 5th Workshop on Semantic Web solutions for large-scale biomedical data analytics (SeMWeBMeDA)
Organizers: Ali Hasnain, Tracy Robson, Michel Dumontier, Brian Kirby, Dietrich Rebholz-Schuhmann
The life sciences domain has been an early adopter of linked data and, a considerable portion of the Linked Open Data cloud is composed of life sciences data sets. The deluge of in flowing biomedical data, partially driven by high-throughput gene sequencing technologies, is a key contributor and motor to these developments. The available data sets require integration according to international standards, large-scale distributed infrastructures, specific techniques for data access, and offer data analytics benefits for decision support. Especially in combination with Semantic Web and Linked Data technologies, these promises to enable the processing of large as well as semantically heterogeneous data sources and the capturing of new knowledge from those.
This workshop invites papers for life sciences and biomedical data processing, as well as the amalgamation with Linked Data and Semantic Web technologies for better data analytics, knowledge discovery and user-targeted applications. This research contribution should provide useful information for the Knowledge Acquisition research community as well as the working Data Scientist. This year we have our special consideration for receiving and accepting original contributions in regards to the Data resources, tools and technologies relevant for research in ongoing Covid19 pandemic.