Ontology design patterns and other methods for modular ontology engineering have recently experienced a revival, and several new promising tools and techniques have been presented. The use of methods for modular ontology development and these newly developed tools and technologies promise simpler ontology/schema development and management, in turn furthering increased adoption of ontologies and ontology-based tech, both within and outside of the semantic web academic environment. This tutorial targets ontology designers, data publishers, and software developers interested in employing semantic technologies and ontologies. We present the state-of-the-art in terms of methods and tools, exemplifying their usage in several real-world cases. We then tutor the attendees on the use of three sets of related tooling for modular ontology development, giving them an opportunity to try out some leading-edge software that they might otherwise have missed, under the supervision of the tools’ main developers.

In this tutorial, we introduce the CrowdTruth methodology for crowdsourcing ground truth by harnessing and interpreting inter- annotator disagreement. It is a widely used methodology adopted by industrial partners and public organizations such as Google, IBM, New York Times, The Cleveland Clinic, Crowdynews, Sound and Vision Museum, the Rijksmuseum, and in a multitude of domains such as AI, news, medicine, social media, cultural heritage, social sciences. The central characteristic of CrowdTruth is harnessing the diversity in human interpretation to capture the wide range of opinions and perspectives, and thus provide more reliable and realistic real-world annotated data for training and evaluating machine learning components. Unlike other methods, we do not discard dissenting votes, but incorporate them into a richer and more continuous representation of truth. The tutorial is hands-on and exercise-centric and is divided in four parts: [1_Introduction]: basic rationale and idea behind CrowdTruth, [2_Task_Design]: elements of task design that enable the collection of diverse opinions and perspectives; [3_Data_Processing]: discusses how to project heterogeneous annotations from the crowd into an interpretable vector space, that can then be used to facilitate result aggregation; [4_Disagreement_aware Metrics]: we will exercise with the CrowdTruth disagreement-aware quality metrics for aggregating crowdsourcing responses.

Different artificial intelligence techniques can be used to explore and exploit large document corpora that are available inside organizations and on the Web. While natural language is symbolic in nature and the first approaches in the field were based on symbolic and rule-based methods, like ontologies and semantic networks, most widely used methods are currently based on statistical approaches, including linear methods, such as support vectors machines or probabilistic topic models, and non-linear ones such as neural networks. Each of these two main schools of thought in natural language processing, knowledge-based and statistical, have their limitations and strengths and there is an increasing trend that seeks to combine them in complementary ways to get the best of both worlds. This tutorial will cover the foundations and modern practical applications of knowledge-based and statistical methods, techniques and models and their combination for exploiting large document corpora. The tutorial will first focus on the foundations of many of the techniques that can be used for this purpose, including knowledge graphs, word embeddings, neural network methods, and probabilistic topic models, and will then show how these techniques are being effectively combined in practical applications, including commercial projects where the instructors are currently involved.

The goal of this workshop is to explore and strengthen the relationship between the Semantic Web and statistical communities, to provide better access to data held by statistical offices. It will focus on ways in which statisticians can use Semantic Web technologies to formalize, publish, document and link their data and metadata, and also on how statistical methods can be applied on linked data. The statistical community shows increasing interest in the Semantic Web. In particular, initiatives have been launched to develop semantic vocabularies representing statistical classifications and discovery metadata. Tools are also being created by statistical organizations to support the publication of dimensional data conforming to the Data Cube W3C Recommendation. But statisticians 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 analysis? How can we preserve data confidentiality? 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 grow in importance in the near future.

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.

The Semantic Web is increasingly becoming a centralized story: we rely on large-scale server-side infrastructures to perform intense reasoning, data mining, and query execution. This kind of centralization leads to a number of problems, including lock-in effects, lack of users' control of their data, limited incentives for interoperability and openness, and the resulting detrimental effects on privacy and innovation. Therefore, we urgently need research and engineering to bring back the “Web” to the Semantic Web, aiming for intelligent clients—instead of intelligent servers—as sketched in the original Semantic Web vision. Following the success of last year’s workshop at ISWC2017, DeSemWeb2018 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. This proposal details the topics of the workshop, as well as the organisational aspects. We believe this proposal will help put different topics on the Semantic Web community’s research agenda, which should lead to new inspiration and initiatives to build future Semantic Web and Linked Data applications.

Knowledge Graphs, Graph Embeddings, Deep Learning, Cognition, Brain Computer Interfaces, Blockchain technology, the entire Semantic Web on a Raspberry Pi… What will be the next big topics in the Semantic Web Community? What are the most urgent challenges to be solved? THE Workshop aims to support the Semantic Web Community to find an answer to this question by offering a platform to present, discuss, exchange, revise, refine, and complement emerging ideas, ongoing and open challenges, as well as preliminary research results of potential impact. Instead of simply repeating the traditional mini conference format, THE workshop enables the ad hoc development of solution ideas for research challenges, to foster the matchmaking of researchers and practitioners working on common or complementary problems, to set up a forum for PhD students to find interesting new topics, and - in the long run - to develop a roadmap for future research.

Semantic Web (SW) Technologies and Deep Learning (DL) share the goal of creating intelligent artifacts. Both disciplines have had a remarkable impact in data and knowledge analysis, as well as knowledge representation, and in fact constitute two complementary directions for modeling expressible linguistic phenomena and solving semantically complex problems. In this context, and following the main foundations set in past editions, SemDeep-4 aims to bring together SW and DL research as well as industrial communities. SemDeep is interested in contributions of DL to classic problems in semantic applications, such as: (semi-automated) ontology learning, ontology alignment, ontology annotation, duplicate recognition, ontology prediction, knowledge base completion, relation extraction, and semantically grounded inference, among many others. At the same time, we invite contributions that analyse the interaction of SW technologies and resources with DL architectures, such as knowledge-based embeddings, collocation discovery and classification, or lexical entailment, to name only a few. This workshop seeks to provide an invigorating environment where semantically challenging problems which appeal to both Semantic Web and Computational Linguistic communities are addressed and discussed.

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 or data interlinking. Thus, matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate. We expect that ISWC 2018 technical program will have several papers presenting different methods for ontology matching (just like previous ISWCs did). Therefore, we do not plan to solicit presentations on matching methods per se. Rather, the workshop has the following goals: 1) To bring together leaders from academia, industry and user institutions to assess how academic advances are addressing real-world requirements. 2) To conduct an extensive and rigorous evaluation of ontology matching and instance matching approaches through the OAEI 2018 campaign. 3) 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.

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.

A 9th edition of the Workshop on Ontology Design and Patterns (WOP). The series addresses quality in ontology design as well as ontology design patterns (ODP) in Semantic Web data, knowledge graphs and ontology engineering. ODPs have seen a sharp rise in attention in the past few years, both pertaining to this workshop series and other related events. Patterns can benefit knowledge engineers and Semantic Web developers with a direct link to requirements, reuse, guidance, and better communication. They need to be shared by a community in order to provide a common language, hence the aim of this workshop is twofold: 1) providing an arena for discussing patterns, pattern-based ontologies, systems, datasets, and 2) broadening the pattern community by developing its own "discourse" for discussing and describing relevant problems and their solutions. Related to the latter aim we see that it is a time to open up the workshop to other approaches focusing on high quality ontology design, e.g. other methods and tools, with the intention to cross-fertilise these with the ODP idea. We propose a full-day workshop consisting of three parts: presentations, posters/demos, and an interactive session of breakout groups working on and discussing ontology design issues and patterns.

This workshop is a joint event of two active communities in the area of interaction paradigms to Linked Data: NLIWOD4 and QALD-9. NLIWOD, a workshop for discussions on the advancement of natural language interfaces to the Web of Data, has been organized three-times within ISWC, with a focus on soliciting discussions on the development of question answering systems. QALD is a benchmarking campaign powered by the H2020 project HOBBIT (project-hobbit.eu) including question answering over (Big) linked data, has been organized as a challenge within CLEF, ESWC and ISWC. We propose a joint workshop to attract people from the two communities in order to promote active collaboration, to extend the scope of currently addressed topics, and to foster the reuse of resources developed so far. Furthermore, we offer an challenge - QALD-9 - where users are free to demonstrate the capabilities of their systems using the provided online benchmark platform. Furthermore, we want to broaden the scope of this workshop series to dialogue systems and chatbots as increasingly important business intelligence factors.

RDF promises a distributed database of repurposable, machine-readable data. Although the benefits of RDF for data representation and integration are indisputable, it has not been embraced by everyday programmers and software architects who care about safely creating and accessing well-structured data. Semantic web projects still lack some common tools and methodologies that are available in more conventional settings to describe and validate data. In particular, relational databases and XML have popular technologies for defining data schemas and validating data which had no analog in RDF. Two technologies have been proposed for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). ShEx was designed as an intuitive and human-friendly high level language for RDF validation in 2014. ShEx 2.0 has recently been proposed by the W3C ShEx community group. SHACL was proposed by the Data Shapes Working Group and accepted as a W3C Recommendation in July 2017. In this tutorial we will present both ShEx and SHACL using examples, presenting the rationales for their designs, a comparison of the two, and some example applications.

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. This workshop 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.

The Linked Data Principles defined by Tim-Berners Lee promise that a large portion of Web Data will be usable as one big interlinked RDF database. Today, we are assisting at a staggering growth in the production and consumption of Linked Open Data (LOD) and the generation of increasingly large datasets. In this scenario, it is crucial to provide intuitive tools for researchers, domain experts, but also businessmen and citizens to view and interact with LOD resources. This talk aims to identify the challenges and opportunities in big linked data visualization and review some current approaches for exploring and visualizing LOD sources. First, we introduce the problem of finding relevant datasets in the catalogue of thousands of datasets, we present the issues related to the understanding and exploration of unknown datasets. We list the difficulties to visualize these growing datasets in static or dynamic form. We focus on the practical use of LOD/ RDF browsers and visualization toolkits and examine the support at big scale. In particular, we experience the exploration of some LOD datasets by performing searches of growing complexity. At last, we sketch the main open research challenges with big linked data visualization. By the end of the talk, the audience will be able to get started with their own experiments on the LOD Cloud, select the most appropriate tool for a defined type of analysis and be aware of the open issues that remain unsolved in the scenario of the exploration of Big Linked Data.

Rapid growth in IoTs means connected sensors and actuators will be inundating the Web infrastructure with data. Semantics is increasingly seen as key enabler for integration of sensor data and the broader Web ecosystem. The W3C and Open Geospatial Consortium standards organisations have taken a second look at W3C SSN and published a new ontology standard for Sensors, Observations, Sampling, Actuation and Sensor Networks. Analytical and reasoning capabilities afforded by Semantic Web standards and technologies are considered important for developing advanced applications that go from capturing observations to recognition of events, deeper insights and actions. Furthermore, the contribution of semantics to sensing and actuation patterns is being explored. 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, web, services and semantics. This workshop will continue the activity started within ISWC in 2006, complemented by special tracks at ESWC since 2010. This 2018 edition benefits 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.

This workshop is related to Contextualized Knowledge Graphs (CKGs), i.e., Knowledge Graphs where every fact can be associated with values in different contexts (e.g., provenance, time, location, or confidence). CKGs have been gaining importance in the recent years, from research initiatives in contextualized and Distributed Description Logics, annotation of statements in the Semantic Web, or Distributed Knowledge Repositories, to being a need in 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 solutions, while at the same time raising awareness about this emerging topic to a more broader community. It addresses topics that range from foundational to practical topics: from logical models to encode the contextual annotations in the graph, or reasoning and querying with CKGs, to exploiting CKGs in applications such as query answering, data mining, or machine learning, as well as techniques to benchmark or improve the performance of contextual graphs management. The workshop will potentially serve as a kickstarter for the creation of a W3C working group on this topic.

"Human-in-the-loop is a model of interaction where a machine process and one or more humans have an iterative interaction. In this paradigm the user has the ability to heavily influence the outcome of the process by providing feedback to the system as well as the opportunity to grab different perspectives about the underlying domain and understand the step by step machine process leading to a certain outcome. Amongst the current major concerns in Artificial Intelligence research are being able to explain and understand the results as well as avoiding bias in the underlying data that might lead to unfair or unethical conclusions. Typically, computers are fast and accurate in processing vast amounts of data. People, however, are creative and bring in their perspectives and interpretation power. Bringing humans and machines together creates a natural symbiosis for accurate interpretation of structured data at scale. The goal of this workshop is to bring together researchers and practitioners in various areas of AI (i.e., Machine Learning, NLP, Computational Advertising, etc.) to explore new pathways of the human-in-the-loop paradigm."

Music provides a fascinating and challenging field for the application of Semantic Web technologies. Music is culture. Yet as knowledge, music takes fundamentally different forms: as digital audio waveforms recording a performance (e.g. MP3); symbolic notation prescribing a work (scores, Music Encoding Initiative); instructions for synthesising or manipulating sounds (MIDI, Digital Audio Workstations); catalogues of performance or thematic aggregations (playlists, setlists); psychological responses to listening; and as an experienced and interpretable artform. How can these heterogeneous structures be linked to each other? To what end? How do we study these materials? Can computational and knowledge management analyses yield insight within and across musics? Semantic Web technologies have been applied to these challenges across industry, memory institutions and academia, but with results reported to conferences representing the communities of different disciplines of musical study. SAAM is a venue for implementers of technical solutions underpinning these achievements to join in dissemination and discussion, identifying intersections in the challenges and solutions which cut across musical areas. In finding common approaches and coordination, SAAM will set the research agenda for advancing the development of semantic applications for audio and music. SAAM also invites the wider ISWC community to discover “what makes music interesting!”.

Knowledge Graphs are a powerful tool that changes the way we do data integration, search, analytics, and context-sensitive recommendations. Consisting of large networks of entities and their semantic relationships, they have been successfully utilized by the large tech companies, with prominent examples like the Google Knowledge Graph and Wikidata, which makes community-created knowledge freely accessible. Cloud computing has fundamentally changed the way that organizations build and consume IT resources, enabling services to be provisioned on-demand in a pay-as-you-go model. Building Knowledge Graphs in the cloud makes it easy to leverage their powerful capabilities quickly and cost effectively. In this tutorial, we cover the fundamentals of building Knowledge Graphs in the cloud. In comprehensive hands-on exercises we will cover the end-to-end process of building and utilizing an open Knowledge Graph based on high-quality Linked Open Data sets, covering all aspects of the Knowledge Graph life cycle including enterprise-ready data management, integration and interlinking of sources, authoring, exploration, querying, and search. The hands-on examples will be performed using prepared individual student accounts set up in the AWS cloud, backed by an RDF/SPARQL graph database service with an enterprise Knowledge Graph application platform deployed on top.

Many Social Good topics can benefit from the sort of unplanned, cross-domain, rapid-prototyping analysis that Linked Data makes possible. We’ve learned that measuring topics like poverty, health, economic opportunity and equality can be substantially more nuanced by considering broader categories of data than organizations traditionally collect for their own use. There have been individual past presentations on topics that involve Semantic Web for Social Good. This workshop is intended to present multiple topics, to better identify intersections, overlaps, opportunities for accelerated advancement, and opportunities for shared abstractions.

In the past few years, a push for open reproducible research has led to community efforts for publishing datasets, software and methods described in scientific publications. These efforts underpin research outcomes more explicitly accessible. However, the time and effort required to achieve this form of scientific communication remains a barrier to reproducibility. Furthermore, scientific experiments are becoming increasingly complex, and ensuring that research outcomes become understandable, reusable and reproducible is still a challenge. The goal of this workshop is to incentivize practical solutions and fundamental thinking to bridge the gap between existing scientific communication methods and the vision of a reproducible and accountable open science. Semantic Web technologies provide a promising means for achieving this goal, enabling more transparent descriptions for all scientific objects required for this envisioned form of science and communication.