Workshops

This established workshop provides a forum for discussing application-oriented issues of Semantic Technologies, with the focus on the development and deployment of systems that turn large volumes of real-world data into actionable knowledge in various domains. This imposes significant scalability requirements on storage and processing systems and demands for reliable workflows to curate and validate data from various sources. SSWS furthermore invites contributions that integrate methods and results from research on Property Graphs found in Graph Databases for instance as well as approaches that combine Knowledge Graphs with machine learning. SSWS welcomes submissions that address relevant research results, report on real-world deployments as well as describe benchmarks and capable back ends or system architectures.

This is the eleventh edition in a series of workshops addressing the topic of ontology and semantic web patterns as best practices, related to the ontologydesignpatterns.org initiative. The workshop series covers issues related to quality in ontology design and ontology design patterns (ODPs) for data and knowledge engineering in Semantic Web. The increased attention to ODPs in recent years through their interaction with emerging trends of Semantic Web such as knowledge graphs can be attributed to their benefit for knowledge engineers and Semantic Web developers. Such benefits come in the form of direct link to requirements, reuse, guidance, and better communication. The workshop’s aim is thus not just: 1) providing an arena for discussing patterns, pattern-based ontologies, systems, datasets, but also 2) broadening the pattern community by developing its own “discourse” for discussing and describing relevant problems and their solutions.

The advancements in health-care have brought to the foreground the need for flexible access to health-related information and created an ever-growing demand for efficient data management infrastructures. To this direction, many challenges must be first overcome, enabling seamless, effective and efficient access to several health data sets and novel methods for exploiting the existing information. This workshop aims at putting together an interdisciplinary audience that is interested in the fields of semantic web, data management and health informatics to discuss the challenges in health-care data management and to propose new solutions for the next generation data-driven health-care systems.

Wikidata is an open knowledge base hosted by the Wikimedia Foundation that can be read and edited by both humans and machines. Wikidata acts as the central source of common, open structured data used by Wikipedia, Wiktionary, Wikisource, and others. It is used in a variety of academic and industrial applications.

In recent years, we have seen an increase in the number of scientific publications around Wikidata. While there are a number of venues for the Wikidata community to exchange, none of those publish original research. We want to bridge the gap between these communities and the research events and give the research-focused part of the Wikidata community a venue to meet and exchange information and knowledge.

The Wikidata Workshop 2020 focuses on the challenges and opportunities of working on a collaborative open-domain knowledge graph such as Wikidata, which is edited by an international and multilingual community. We encourage submissions that observe the influence such a knowledge graph has on the web of data, as well as those working on improving this knowledge graph itself. This workshop brings together everyone working around Wikidata in both the scientific field and industry to discuss trends and topics around this collaborative knowledge graph.

The constant growth of Linked Data on the Web raises new challenges for querying and integrating massive amounts of data across multiple datasets. Such datasets are available through various interfaces, such as data dumps, Linked Data Platform, SPARQL endpoints and Triple Pattern Fragments. In addition, various sources produce streaming data. Efficiently querying these sources is of central importance for the scalability of Linked Data and Semantic Web technologies. To exploit the massive amount of data to its full potential, users should be able to query and combine this data easily and effectively. This workshop at the International Semantic Web Conference (ISWC) seeks original articles describing theoretical and practical methods and techniques for fostering, querying, consuming, and benchmarking the Web of Data.

The NLIWOD 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. With NLIWOD, we attract people from academia as well as from industry to promote active collaboration, to extend the scope of currently addressed topics, and to foster the reuse of resources developed so far.

In each installation, we keep the main tenor of the workshop but include current topics from natural language interface. This year, we broaden the scope of the workshop series to truly conversational systems and chatbots (inspired by the Amazon Alexa challenge and the advances in personal assistants) as well as focus on the combination of recently successful Deep Learning techniques and Knowledge Graphs (inspired by the last Dagstuhl workshop on the future of Knowledge Graphs) which are both increasingly important business intelligence factors.

The Web of Data has seen tremendous growth recently. New forms of structured data have emerged in the form of knowledge graphs, Web markup, such as schema.org, as well as entity-centric data in Web tables. Considering these rich, heterogeneous and evolving data sources which cover a wide variety of domains, exploitation of Web Data becomes increasingly important in the context of various applications, including dataset search, question answering and fact verification. These applications require reliable information on dataset characteristics, including general metadata, quality features, statistical information, dynamics, licensing, and provenance. Lack of a thorough understanding of the nature, scope and characteristics of data from particular sources limits their take-up and reuse, such that applications are often limited and focused on well-known reference datasets.

The objective of this workshop is to bring together researchers and practitioners interested in the development of techniques for dataset profiling and deriving quality analytics, as well as performing dataset search and dataset retrieval on the Web while taking dataset profiles into account. We are interested in approaches to analyse, characterise and discover data sources. We aim to discuss technologies addressing data profiling and search – including semantics, information retrieval for Web Data (ranking algorithms and indexing), in particular in the context of decentralised and distributed systems, such as the Web. We want to facilitate a discussion around data search across formats and domain-specific applications.

‘A picture is worth a thousand words’, we often say, yet many areas are in demand of sophisticated visualization techniques, and the Semantic Web is not an exception. The size and complexity of ontologies and Linked Data in the Semantic Web constantly grows and the diverse backgrounds of the users and application areas multiply at the same time. Providing users with visual representations and sophisticated interaction techniques can significantly aid the exploration and understanding of the domains and knowledge represented by ontologies and Linked Data. There is no one-size-fits-all solution but different use cases demand different visualization and interaction techniques. Ultimately, providing better user interfaces, visual representations and interaction techniques will foster user engagement and likely lead to higher quality results in different applications employing ontologies and proliferate the consumption of Linked Data.

Legislation applies to every aspect of people’s living and evolves continuously building a huge network of interlinked legal documents. Therefore, it is important for a government to offer services that make legislation easily accessible to the citizens aiming at informing them, enabling them to defend their rights, or to use legislation as part of their job. It is equally important to have law professionals (lawyers, judges, etc.) access legislation in ways that allow them to do their job easily (e.g., they might need to be able to see the evolution of a law over time). Finally, in the age of the Web, it is important to enable software developers to develop applications for citizens and law professionals easily, by connecting the available laws with other kinds of government or private sector information. Towards this direction, there are already many countries in Europe and elsewhere that have computerized the legislative process by developing platforms for archiving legislation documents and offering on-line access to them using standards such as Akoma Ntoso (aka LegalDocML) which is an OASIS standard, the European standard CEN-MetaLex, the European Legislation Identifier, the European Case Law Identifier etc. There also private companies (e.g., ROSS Intelligence, LexisNexis, RAVEL, LexMachina etc.) that specialize on providing digital services for law, case law, compliance, contracts, etc. The vision of the AI4LEGAL international workshop is to bring together Artificial Intelligence researchers and practitioners to work on the problem of digitization of legislation and legal documents in today’s interconnected world.

Ontology matching is a key interoperability enabler for the Semantic Web, as well as a useful technique in some classical data integration tasks dealing with the semantic heterogeneity problem. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. These correspondences can be used for various tasks, such as ontology merging, data interlinking, query answering or navigation over knowledge graphs. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate.

The workshop has the following goals:

• To bring together leaders from academia, industry and user institutions to assess how academic advances are addressing real-world requirements. The workshop will strive to improve academic awareness of industrial and final user needs, and therefore, direct research towards those needs. Simultaneously, the workshop will serve to inform industry and user representatives about existing research efforts that may meet their requirements. The workshop will also investigate how the ontology matching technology is going to evolve, especially with respect to data interlinking, process mapping and web table matching tasks.
• To conduct an extensive and rigorous evaluation of ontology matching and instance matching (link discovery) approaches through the OAEI (Ontology Alignment Evaluation Initiative) 2020 campaign.
• To examine similarities and differences from other, old, new and emerging, techniques and usages, such as process matching, web table matching or knowledge embeddings.

As knowledge graphs become increasingly important resources in the public and private sectors, there is an increasing need for expertise in developing and maintaining these resources. The workshop’s aim is twofold: practical tools and effective pedagogy. That is, solicit and understand the tooling infrastructure needs of both industry and academia. In doing so, we identify points in workflows to focus future research endeavors while providing a critical need to the community. And secondly, determine effective ways for teaching Semantic Web concepts (and their importance) to existing industry professionals, as well as to students in an academic setting. Of course, there is strong overlap, as these points are deeply entwined and inform each other.

Due to the impact of COVID-19 and the virtual format, we have opted to make some small changes to the design and execution of PRAXIS 2020.

• The workshop will be conducted virtually and synchronously.
• Reduced to 3 hours.
• Time TBD, and dependent on timezone decision made by ISWC.
• The workshop will be conducted un-conference style.
• The workshops will be hosted through Zoom and run through ISWC’s infrastructure.
• Authors of submissions will be invited to give 5-10 minute lightning talks on their submission, and these submissions will act as seed topics for our un-conference.
• We intend to brainstorm and discuss tools and education along the dimensions outlined in the description.
• The results of our brainstorming and discussion, alongside submissions from our authors, will be placed into a GitHub repository and given a Zenodo DOI, per the original Call for Submissions for PRAXIS2020.

In the age of post-truth, misinformation has become too dangerously common, and is actively and successfully influencing public perception and stance towards many sensitive issues and domains, including economy, health, environment, culture, and foreign policy. Today, around half the world’s population have access to the Internet, where they can create, propagate, and consume information instantly and globally. In spite of this rising addiction to rapid consumption of online information, people and current technologies are yet to adapt to the age of misinformation, where incorrect or misleading information is intentionally or unintentionally spread across all media, and in particular across the Web and social media. This workshop aims to gather cutting edge research, expertise, and techniques generated by the Semantic Web community, to join the emerging global battle against misinformation.

There is a vast and rapidly increasing quantity of scientific, corporate, government, and crowd-sourced data openly published on the Web. Open Data plays a catalyst role in the way structured information is exploited on a large scale. A traditional view of digitally preserving these datasets by “pickling and locking them away” for future use, like groceries, conflicts with their evolution. There are several approaches and frameworks (Linked Data Stack, PoolParty Suite, etc.) that manage a full life-cycle of the Data Web. More specifically, these solutions are expected to tackle major issues such as the synchronisation problem (monitoring changes), the curation problem (repairing data imperfections), the appraisal problem (assessing the quality of a dataset), the citation problem (how to cite a particular version of a dataset), the archiving problem (retrieving a specific version of a dataset), and the sustainability problem (preserving at scale, ensuring long-term access). During the past five years, the MEPDaW workshop series has been gathering researchers from the community around these challenges. So far the series successfully published more than 25 research efforts allowing more than 50 individual authors to present and share their ideas.

Research data is the mirror of experimental work. It complements scientific publications and is core input to data driven research. Most research activities follow the research data cycle, where data is continuously used, modified and produced, transitioning from one research group to another. For this cycle to prosper, we require Research Data Management plans supporting the findable, accessible, interoperable and reusable (FAIR) principles. Despite playing an important role, data on its own is not sufficient to establish Open Science nor Linked Open Science, i.e., Open Science plus Linked Open Data (LOD) principles. LOD principles, a.k.a. LOD 5 stars, follow objectives that overlap with FAIR principles such as “openness” and the use of “non-proprietary open formats”. In this workshop we will explore what is required for RDM to effectively instantiate Linked Open Science, including effective support for LOD, automation by, e.g., machine/deep learning approaches, and innovations to include supporting data elements such as the software used to produce/consume it or the tutorials showcasing usage and fostering further developments. Furthermore, data management should be complemented by other research objects management plans, e.g., software and workflows, in order to get an integrated layer supporting all the edges of Linked Open Science. In this workshop, we will focus on data management for Linked Open Science but we will also have opportunities to discuss how other research objects, i.e., other than data objects, play an important role.

Explainability of complex systems such as those based on machine learning and artificial intelligence has been expressed not only as a desired property, but also as required by law: the GDPR “right to explanation” demands that the results of ML/AI-based decisions are explained. The explainability of complex systems becomes increasingly relevant as more and more aspects of our lives are influenced by these systems‘ actions and decisions. Several workshops address the problem of explainable AI, but none has a focus on semantic technologies. We believe semantic technologies and explainability coalesce in two ways. First, systems based on semantic technologies must be explainable like all other AI systems. Furthermore, semantic technology seems predestined to support in rendering explainable those systems that are not themselves based on semantic technologies. This workshop aims to bring together international experts interested in the application of semantic technologies for explainability of AI/ML-based approaches to stimulate research, engineering and evaluation – towards making machine decisions transparent, re-traceable, comprehensible, interpretable, explainable, and reproducible. Semantic technologies have the potential to play an important role in the field of explainability since they lend themselves very well to the task, as they enable to model users‘ conceptualizations of the problem domain.

The goal of this workshop is to explore and strengthen the relationship between the Semantic Web and statistical communities, to provide better access to the data and metadata held by statistical offices. It focuses on ways in which statisticians can use Semantic Web technologies and standards in order to formalize, publish, document and link their data and metadata, and also on how statistical methods can be applied on linked data. This is the 8th workshop in a series that started at the International Semantic Web Conference in 2013 and runs since every year at ISWC.

CKG2020 is concerned with knowledge graphs with contexts, i.e., every fact is enriched by the contexts (e.g., provenance, time, location, or confidence). Contextualized Knowledge Graphs (CKGs) have been gaining importance in the recent years. Research topics include contextualized and distributed Description Logics, annotation of statements in the Semantic Web, and Distributed Knowledge Repositories. Real-world use cases include the creation of collaborative knowledge bases, such as Wikidata, where qualifiers and references can be attached to every statement. This workshop aims to serve as a gathering point for researchers and industry interested in CKGs to discuss current challenges and future solutions, and raise awareness about this emerging topic to a more broader Semantic Web community. This workshop addresses fundamental as well as practical topics including (i) logical models to encode the contextual annotations in the graph, (ii) reasoning and querying over CKGs, (iii) using CKGs in applications such as query answering, data mining, or machine learning, (iv) techniques to benchmark or improve the performance of CKG storage and querying systems. This workshop is complemented by a W3C community on this topic.