Workshops at ISWC 2019
Elena Demidova, Stefan Dietze, John Breslin and Simon Gottschalk
PROFILES’19 will gather novel works from the fields of semantic query interpretation, entity-centric and event-centric Web data search, dataset selection and discovery, as well as automated profiling of datasets using scalable data assessment and profiling techniques. PROFILES’19 will equally consider both novel scientific methods and techniques for querying, assessment, profiling, discovery of distributed datasets, as well as the application perspective, such as the innovative use of tools and methods for providing structured knowledge about datasets, their evolution, and fundamentally, the means to search and query Web Data.
VOILA 2019, 5th International Workshop on Visualization and Interaction for Ontologies and Linked Data
Valentina Ivanova, Patrick Lambrix, Steffen Lohmann, Catia Pesquita and Vitalis Wiens.
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 intuitive interaction techniques can significantly aid the exploration and understanding of the domains and knowledge represented by ontologies and Linked Data.
Ontology visualization is not a new topic and a number of approaches have become available in recent years, with some being already well-established, particularly in the field of ontology modeling. In other areas of ontology engineering, such as ontology alignment and debugging, although several tools have been developed, few provide a graphical user interface, not to mention navigational aids or comprehensive visualization and interaction techniques.
In the presence of a huge network of interconnected resources, one of the challenges faced by the Linked Data community is the visualization of multidimensional datasets to provide for efficient overview, exploration and querying tasks, to mention just a few. With the focus shifting from a Web of Documents to a Web of Data, changes in the interaction paradigms are in demand as well. Novel approaches also need to take into consideration the technological challenges and opportunities given by new interaction contexts, ranging from mobile, touch, and gesture interaction to visualizations on large displays, and encompassing highly responsive web applications.
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.
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 process mapping. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate.
The workshop has three 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) 2019 campaign. Besides real-world specific matching tasks, such as the disease-phenotype track supported by the Pistoia Alliance, will introduce the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching track, supported by IBM Research.
– To examine new uses, similarities and differences from database schema matching, which has received decades of attention but is just beginning to transition to mainstream tools
Major industries including manufacturing, transport and logistics, personal and public health, smart cities and smart energy, crisis management and many others spanning commercial, civic, and scientific operations that involve sensors and actuators exposed on the web. In potential combination with other AI techniques, the Semantic Web offers representational, analytical, and reasoning capabilities that are important for the development of advanced applications that rely on sensors and actuators, potentially geographically distributed and/or exposed on the Web of Things. SAW 2019 targets Semantic Web practitioners that represent, reason with, publish, or use knowledge related to sensors and actuators in general. This workshop gives a breath of fresh air to the Semantic Sensor Network Workshop series that started within ISWC in 2006, was complemented by special tracks at ESWC since 2010, and was resumed by a successful 9th edition at ISWC 2019 which benefited from renewed energy arising from the October 2017 W3C recommendation and OGC standard, and more importantly, increases significance due to the growth in IoT enabled applications. SAW 2019 is organized by the contributors to SOSA/SSN and particularly welcomes early adopters of this ontology, whose work may not be mature enough to get published at the main conference, or consist in migrating previous work.
Better information management is the key to a more intelligent health and social system. To this direction, many challenges must be first overcome, enabling seamless, effective and efficient access to the various health data sets and novel methods for exploiting the available information. SWH aims to bring together an interdisciplinary audience interested in the fields of semantic web, data management and health informatics to discuss the unique challenges in health-care data management and to propose novel and practical solutions for the next generation data-driven health-care systems.
As semantic technologies are currently widely exploited more and more for the management of health data, new challenges occur daily that dictate new solutions. The continuation of this workshop will allow the specific interdisciplinary audience to have a unique forum for discussing, exchanging ideas and experiences. In addition, directions like for example the incorporation of semantic technologies for healthcare decision support, and semantically enhanced AI approaches should be further explored. SWH will offer a fruitful environment for these ideas to mature leading to the ultimate goal of improving the results from the healthcare practice.
This workshop yields a room for discussions on the advancement of natural language interfaces to the Web of Data, has been organized four-times within ISWC, with a focus on soliciting discussions on the development of question answering systems. This workshop hopes to promote active collaboration, 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 this workshop is to bring together experts on the use of natural-language interfaces (NLI) for answering questions especially over the Web of Data.
In recent years, the explainability of complex systems such as decision support systems, automatic decision systems, machine learning-based/trained systems, and artificial intelligence in general has been expressed not only as a desired property, but also as a property that is required by law. For example, the General Data Protection Regulation’s (GDPR) „right to explanation“ demands that the results of ML/AI-based decisions are explained. The explainability of complex systems, especially of ML-based and AI-based 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. However, none of these workshops has a focus on semantic technologies such as ontologies and reasoning. We believe that semantic technologies and explainability coalesce in two ways. First, systems that are based on semantic technologies must be explainable like all other AI systems. In addition, semantic technology seems predestined to support in rendering explainable those systems that are not themselves based on semantic technologies.
Turning a system that already makes use of ontologies into an explainable system could be supported by the ontologies, as ideally the ontologies capture some aspects of the users‘ conceptualizations of a problem domain. However, how can such systems make use of these ontologies to generate explanations of actions they performed and decisions they took? Which criteria must an ontology fulfill so that it supports the generation of explanations? Do we have adequate ontologies that enable to express explanations and enable to model and reason about what is understandable or comprehensible for a certain user? What kind of lexicographic information is necessary to generate linguistic utterances? How to evaluate a system‘s understandability? How to design ontologies for system understandability? What are models of human-machine interaction where the system enables to interact with the system until the user understood a certain action or decision? How can explanatory components be reused with other systems that they have not been designed for?
Turning systems that are not yet based on ontologies but on sub-symbolic representations/distributed semantics such as deep learning-based approaches into explainable systems might be supported by the use of ontologies. Some efforts in this field have been referred to as neural-symbolic integration.
This workshop aims to bring together international experts interested in the application of semantic technologies for explainability of artificial intelligence/machine learning 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. However, this field has so far only been only rarely explored.
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.
This workshop at the International Semantic Web Conference 2019 (ISWC 2019) seeks original articles describing theoretical and practical methods and techniques for fostering, querying, and consuming the Data Web. Topics relevant to this workshop include — but are not limited to — the following:
– SPARQL query processing
— Centralized, decentralized, federated, and distributed
— Demos and applications
— Optimization and source selection
— Benchmarks, ranking, measures, and performance evaluation
— Lightweight Linked Data interfaces
— Big Data techniques
— Entailment regimes
— Caching and replication
– Integrating public and private Linked Data
– Querying personal Linked Data stores
– Domain-specific query languages (e.g., temporal and spatial queries)
– Querying embedded Linked Data
– Query relaxation and rewriting
– Alternative languages for representing and querying the Web of Data
Mari Carmen Suárez-Figueroa, Deborah McGuinness, K. Krasnow Waterman and Markus Luczak-Roesch
The 2030 Agenda for Sustainable Development is the plan for transforming our world. This agenda focuses on 17 Sustainable Development Goals (SDGs) . These goals try to realize the human rights of all, to achieve gender equality and quality education, and to combat climate change, among other issues. In order to successfully implement and monitor such an Agenda, it is crucial to have available, accessible, high-quality and reliable data generated by governments, organizations and citizens.
Nowadays, datasets coming from different agents are emerging. This data, needed to fully understand how the SDGs are being achieved and could be reached in the future, are inherently complex, often inconsistent, and dynamic. In this context, ontologies and semantic technologies are a good way to ‘understand’ the meaning of data and information holdings. Thus, knowledge acquisition and modelling, ontologies, vocabularies, reasoning, and linking, among other topics related to the Semantic Web are key for supporting the implementation and monitoring of the 2030 Agenda.
Zhe He, Jiang Bian, Cui Tao and Rui Zhang
Biomedical ontologies and controlled terminologies provide structured domain knowledge to a variety of health information systems. The rich thesaurus with concepts linked by semantic relationships has been widely used in natural language processing, data mining, machine learning, semantic annotation, and automated reasoning. The dramatically increasing amount of health-related data poses unprecedented opportunities for mining previously unknown knowledge with semantics-powered data mining and analytics methods. However, due to the heterogeneity of different data sources, it is a challenging problem to exploit multiple sources to solve real-world problems such as designing cost-effective treatment plan for patients, designing generalizable clinical trials, drug repurposing, and clinical phenotyping. The goal of this workshop is to bring people in the field of ontologies, data mining, knowledge representation, knowledge management, and data analytics to discuss innovative semantic methods, applications, and data analytics to address problems in healthcare, biomedicine, public health, and clinical research with biomedical, clinical, behavioral, and social web data.
In the past three years, SEPDA has been established as a key venue for disseminating research on health data analytics using semantic web technologies such as ontologies. In the past few years, we have seen an increasing interest in using semantic web technologies for health data analysis with more and more submissions that present novel methods and applications for linked open data, information extraction, semantic-web-based knowledge bases, and deep learning. The NIH Data Science Strategic Plan released in June 2018 explicitly commits to ensuring that all data-science activities and products supported by the agency adhere to the FAIR principles, meaning that data be Findable, Accessible, Interoperable, and Reusable. Semantic web technologies play a crucial role to address the FAIR principles. With the infrastructure support such as NCBO’s BioPortal for ontology maintenance, the CEDAR software for metadata creation and validation, more and more researchers are using ontologies and semantic web technologies for knowledge representation, semantic inference, natural language processing, and data analytics. Meanwhile, we received submissions that use semantic-based methods to tackle critical problems in biomedical informatics such as extracting drug-drug interaction, drug repurposing, adverse drug reaction, detecting early signals for cognitive impairment, and visualizing dietary supplement knowledge. It is thus critical for SEPDA to continue our momentum and allow researchers to present and discuss novel methods and applications in this fast growing field. This year, we will hold SEPDA in ISWC, the premier conference in semantic web.
Reza Samavi, Shahan Khatchadourian and Mariano Consens
Blockchain and artificial intelligence (AI) are two emerging technologies with the potential to contribute to semantic web efforts. Blockchains have been used for applications such decentralized finance in such a way that establishes trust without a central authority. Artificial Intelligence has been used for decision making, requiring vast amounts of data affected by privacy and trust requirements. To address these overlapping themes, the BlockSW workshop is open to submissions at the intersection of blockchain, semantic web and AI.
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.
Ali Hasnain, Vit Novacek, Michel Dumontier and Dietrich Rebholz-Schuhmann.
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 workshop at the International Semantic Web Conference (ISWC) seeks original contributions describing theoretical and practical methods and techniques that present the anatomy of large scale linked data infrastructure, which covers: the distributed infrastructure to consume, store and query large volumes of heterogeneous linked data; using indexes and graph aggregation to better understand large linked data graphs, query federation to mix internal and external data-sources, and linked data visualisation tools for health care and life sciences. It will further cover topics around data integration, data profiling, data curation, querying, knowledge discovery, ontology mapping / matching / reconciliation and data / ontology visualisation, applications / tools / technologies / techniques for life sciences and biomedical domain. SeWeBMeDA aims to provide researchers in biomedical and life science, an insight and awareness about large scale data technologies for linked data, which are becoming increasingly important for knowledge discovery in the life sciences domain.
Topics of interest include, but are not limited to Semantic Web and Linked Data technologies in the following areas:
– Techniques for analyzing semantic data in the life sciences, medicine and health care
– The description, integration, analysis and use of data in pursuit of challenges in the life sciences, medicine and health
– Tools and applications for biomedical and life sciences
– Large scale biomedical data curation and integration
– Processing biomedical data at scale
– Knowledge representation and knowledge discovery for biomedical data
– Data and metadata publishing, profiling and new datasets in biomedical and life sciences
– FAIR (Findable, Accessible, Interoperable and Reusable) publishing, usage and analysis of biomedical/ life science data
– Scalable integration and reproducible analysis of FAIR (Findable, Accessible, Interoperable and Reusable) data
– Querying and federating data over heterogeneous datasources
– Biomedical ontology creation, mapping/ matching/ translation and reconciliation
– Biomedical Ontology and data visualisation
– Building and maintaining biomedical knowledge graphs
– Machine learning with biomedical knowledge graphs
– Knowledge Graphs and Relational Learning for Life Sciences
– Intelligent Visualisations of Linked Life Science Data
– Biomedical data quality assessment and improvement
– From Semantics to Explanations in biomedicine and life science
– Text analysis, text mining and reasoning using semantic technologies
– New technologies and exploitation of existing ones in Linked Data and Semantic Web
– Social, ethical and moral issues publishing and consuming biomedical and life sciences data.
International Workshop on Artificial Intelligence and Big Data Technologies for Legal Documents (AI4LEGAL)
Manolis Koubarakis, Elena Montiel Ponsoda, Grigoris Antoniou, Guido Governatori and Yoshinobu Kano
AI4LEGAL aims to bring together Artificial Intelligence and Big Data researchers and practitioners to discuss issues related to the digitization of legislation and other legal documents in today’s interconnected world.
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 and Big Data researchers and practitioners to work on the problem of digitization of legislation and legal documents in today’s interconnected world. As can be seen from the above topics, both research areas have a lot to contribute.
There are already established conferences on AI for legislation such as the International Conference on Artificial Intelligence and Law (ICAIL) or the International Conference on Legal Knowledge and Information Systems (JURIX). However, this area has not attracted so far the attention it deserves, given its importance in people’s lives and its economic importance, from researchers and practitioners from the Semantic Web area, especially in comparison to other application areas targeted by the conference (e.g., social media). The vision of the AI4LEGAL workshop organizers is to change this.
Ruben Verborgh, Tobias Kuhn and Tim Berners-Lee
Like the Web, the Semantic Web is often reduced to a centralized story: we rely on large-scale server-side infrastructures to perform intense reasoning, data mining, query execution, etc. Therefore, we urgently need research and engineering to put the “Web” back in the “Semantic Web”, aiming for intelligent clients instead of intelligent servers. The DeSemWeb2019 workshop focuses on decentralized and client-side applications to counterbalance the centralized discourse of other tracks. While we recognize the value in all subfields of the Semantic Web, we see an urgent need to revalue the role of clients, continuing last year’s edition.
Contribute to this workshop and help put different topics on the Semantic Web community’s research agenda, which will lead to new inspiration and initiatives to build future Semantic Web and Linked Data applications.
Raphaël Troncy, Franck Cotton, Sarven Capadisli, Evangelos Kalampokis and Armin Haller.
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 seventh workshop in a series that started at the International Semantic Web Conference in 2013 (SemStats 2013) and run since every year at ISWC (2014, 2015, 2016, 2017 and 2018).
The statistical community shows more and more interest in the Semantic Web. In particular, initiatives have been launched to develop semantic vocabularies representing statistical classifications, discovery metadata, business models, etc. Tools have been created by statistical organizations to support the publication of dimensional data conforming to the Data Cube W3C Recommendation. But statisticians still see challenges in the Semantic Web: how can data and concepts be linked in a statistically rigorous fashion? How can we avoid fuzzy semantics leading to wrong analyses? How can we preserve data confidentiality? How can we use linked statistical data in machine learning models?
The workshop will also cover the question of how to apply statistical methods or treatments to linked data, and how to develop new methods and tools for this purpose. Except for visualization techniques and tools, this question is relatively unexplored, but the subject will obviously grow in importance in the near future.
This year, Statistic New Zealand (SNZ) has offered a Keynote Talk on “Semantically Linking the Knowledge and Statistical worlds” and the chairing of a panel session about challenges, questions, ideas etc. raised out of the workshop. SNZ has also proposed various contributions like a debate on “GLAM (Galleries, Libraries, Archives and Museums) versus Stats” from a linked data perspective.
Mohammed Hasanuzzaman, Rejwanul Haque, Yalemisew Abgaz
Historical, legacy and indigenous language resources are becoming available on the web in response to open access policies adopted by public and private research institutions. Legacy data of historical, cultural and linguistic importance has a significant role in supporting the research endeavour of fellow scientists across different domains. This emerging trend presents a new challenge for the institutions on how to efficiently open up their legacy data to deliver semantically rich, interlinked and interoperable dataset. Ontologies provide semantics to enrich such resources and serve the basis for publishing such data using Linked Open Data(LOD) platforms. However, existing ontologies and platforms do not provide comprehensive coverage to domain-specific requirements of the institutions and the target users. This trend further provides a new opportunity to bridge the among domain digital humanity researchers, linguists, NLP practitioners and computer scientists by making rich domain-specific models to semantically uplift the collections, efficiently interlink the resources using LOD principles and enhance the discovery of the resources by human users and computer agents.
To this end, it becomes very crucial for both institutional and individual users to come together to discuss and share the experiences on the semantic publishing of legacy data on the LOD platforms. The proposed workshop is focused on the challenges and opportunities of data-driven humanities and brings together scientists and scholars at the forefront of this emerging field, at the interface between the digital humanities, history, anthropology, lexicography, linguistics, as well as computer science.
Many historical, socio-cultural and linguistic research centres, national archives and museums are adopting open access policy to promote efficient utilisation of their resources by the general public. However, much of the legacy data lacks detailed semantics to be used and exploited by non-expert users. Semantic web technologies are capable of enriching the data with required semantics, however existing ontologies and available models do not fully support the domain-specific requirements of users.
As institutions plan to make public records accessible to the general public, more and more domain-specific information will become available and linking such data in the LOD cloud will attract significant attention. Thus, opening a discussion platform that brings various stakeholders such as digital humanity experts, linguists, NLP experts, computer scientist and ontology engineers together to present their work and share their experiences is of paramount importance.