The tremendous increase in using the Web for establishing and maintaining social interactions has transformed the Web from a technical into a socio-technological phenomenon. The social web is a prominent example for that development. Aiming to expand our understanding of social phenomena from various perspectives, Web Science has emerged as an interdisciplinary approach, joining computing, physical and social sciences together. Web Science thereby studies phenomena on the web with a special focus on the people and communities who shape it and whose lives are influenced by the Web in increasingly ubiquitous ways. 

In tandem with such studies, the rise in uptake of social platforms and the ubiquity of social functionalities across the Web has meant that the machine-readability of social data from disparate Web platforms has become increasingly important. Therefore research on the Semantic Web and Web-Science has important synergies and intersections:

1. Semantic Web research offers important technologies for making sense of and dealing with the large amount of user generated content that is inherently varied, dynamic, heterogeneous and contextual.
2. Semantic Web research can greatly benefit from an increased understanding of social dynamics on the web, and from user generated content for extending its information basis and improving its technologies, for example its reasoning mechanisms.

This track invites contributions that explore synergies between the Semantic Web and its technology on the one hand, and social and ubiquitous Web phenomena on the other hand. Topics of interest include but are not limited to the following:

• Bringing user-generated content into the Semantic Web
• Mining semantics from social data and collective intelligence from community interactions
• Semantically enabled social platforms and applications: wikis, forums, portals, blogs and microblogs, etc.
• Web users as virtual and physical sensors, crawlers, etc. for a ubiquitous Social Semantic Web
• Semantics for personalisation: recommendations, social navigation, collaborative search, social filtering, etc
• Social semantic networks, network effects, community analysis and user evolution
• Querying, mining and analysis of user generated data and dynamics
• Representing and reasoning with uncertainty, provenance and trust in social data
• Big social semantic data: scalable processing techniques, trust and compliance for social semantic data analysis
• Computational social science: understanding semantic dynamics in online social networks, semantically enabled analysis of social behavior, semantic annotation of social datasets
• Incentives, usage, and social processes around linked open data
• Semantics for open data generation, analysis, and exploitation

Research Track: Semantic Data Management, Big data, Scalability

Semantic data management is playing a crucial role for the accomplishment of the Semantic Web and Web of Data visions. Although several valuable systems are available, there is a need to address new challenges due to the emergence of the Big Data phenomenon. For instance, these systems have to face increasing volumes of rapidly changing RDF datasets as well as intensive query loads that may require some inference services. Moreover, at such scale, the qualification of the data quality and its possible curation also require designing novel methods and implementing adapted systems.

In this context, the track aims to gather researchers and system developers from the Semantic Web, Database, and Artificial Intelligence fields to discuss research issues, experiences and results in designing, implementing, deploying and evaluating theories, techniques and applications related to the Management of Semantic Web Data, especially on a large scale.

Topics of interest include, but are not limited to:

• Systems for (distributed) Semantic Data Management
• Semantic Data Management in RDF and Graph Databases
• Scalable Analysis of the Web of Data
• Query processing of Semantic Data
• Semantic access to Legacy Data
• Management of Spatial Data
• Management of Dynamic Data & Temporal Semantics
• Virtualized Semantic Stores
• Exploratory Semantic Searching and Browsing
• Security and Privacy in large datasets
• Traceability and Trustworthiness
• Ranking of Semantic Data
• Provenance in the Integration of Heterogeneous Semantic Data
• Performance, Evaluation, and Benchmarking of Semantic Data Management Techniques
• Data quality and Data curation support in Semantic data management systems
• Semantic Data Management technologies for Big Data (volume, velocity, variety).
• Semantic data management and polyglot persistence
• Integrating reasoning services within (large scale) Semantic data management
• Domain-Specific Semantic Data Management technologies for Life Sciences, eGovernment, eEnvironment, eMobility, eHealth, and within the enterprise.

Research Track: Natural Language Processing and Information Retrieval

The belief that the interaction between Natural Language Processing (NLP), Information Retrieval (IR) and Semantic Web could boost the performances of Semantic Web technologies has become an undeniable fact. NLP services have substantially contributed to the rapid development of the Semantic Web, in the same way as the Semantic Web has contributed to the enhancement of NLP systems by providing background knowledge. Similarly, IR and Web search technologies are slowly but steadily moving towards semantic-aware and semantic-rich approaches (Google’s knowledge graph being a case in point). This, in turn, provides a large scale, real-world application of semantic technologies for the Web, which are at the heart of the Semantic Web vision.

In the Big Data era, the Linked Open Data (LOD) initiative has opened up new perspectives for NLP research by making available a giant knowledge base that contains both general and very specific domain knowledge for use in language processing tasks. The NLP community in turn has provided lexical and linguistic resources that, in combination with this data, can help make sense of the structured knowledge on the Web.

Multilingualism and multiculturalism are also highly relevant: since more and more data and linguistic resources are published in the LOD cloud in languages other than English. Web-scale NLP and IR technologies such as Semantic Search, Information Extraction, Question Answering and Social Network Analysis have to account for these languages. Finally, user- or community-generated data and annotations have become extremely relevant for businesses and market studies.

The main goal of the NLP&IR track is to foster a closer interaction between the NLP, IR and Semantic Web communities, which could potentially lead to novel technologies effectively combining vast amounts of background knowledge and reasoning with statistical deep language understanding and Web-scale search. Specific topics include, but are not limited to:

• Distant supervision from Semantic Web data for Information Extraction
• Entity/event coreference and linking
• Evaluating NLP and IR systems using Semantic Web data
• Evaluating the quality of Semantic Web data extrinsically using NLP and IR systems
• Exploiting lexical resources for the Semantic Web
• Integrating ontologies / Linked Open Data with Language Resources
• Information Extraction for Social Media Mining
• Language processing of social network data
• Linguistic Linked Data
• Natural Language Processing services using Linked Open Data
• Natural Language Search and Question Answering over linked data
• Ontology learning and ontology population based on NLP technologies
• Ontology-based information extraction
• Ontology lexicalization and localization
• Opinion mining exploiting semantic information
• Semantic annotation exploiting linked data
• Semantic Search
• Deep semantic techniques for NLP applications (e.g., question answering, summarization)
• Standards for meaning and/or linguistic representation on the Semantic Web
• Web-scale structured knowledge acquisition 

Research Track: Machine Learning

In the perspective of the Semantic Web (SW) as a Web of Data, Machine Learning (ML)  and Data Mining (DM) approaches are gaining an increasing relevance. ML/DM can deal with the intrinsic uncertainty in Web data containing incomplete and/or contradictory information. ML/DM is also very well suited to cope with a large scale of Web data and provides tools for big data analytics. The prospect is that innovative solutions based on specific application of ML/DM techniques to information sources such as Linked Data, tagged data, social networks, and ontologies will increasingly support standard SW tasks and enable new ones. We invite high quality contributions from all areas of research that address the emerging data challenges.

Topics of interest include but are not limited to the following:

• Machine learning, Data mining and knowledge discovery in the Web of data
  • Statistical relational learning
  • Discovery of associations, patterns, and events
  • Data quality assessment: modeling and maintenance of provenance information
  • Feature extraction, pre-processing and transformation of SW data
  • Web usage mining, ranking methods, recommendation on the Web of Data
  • Evaluation and benchmarking of ML/DM models
• Big Data analytics involving Linked Data
  • Scalable ML/DM algorithms for the web of data
  • Distributed architectures for mining the web of data
• Inductive reasoning on uncertain knowledge
  • Combination of logic reasoning and ILP
  • Approaches adopting alternative theories of evidence
  • Inductive techniques based on semantic similarity
• Knowledge base creation and maintenance using ML/DM
  • Machine learning for construction, refinement, interlinking, debugging, and repair of knowledge bases
  • Efficient indexing, search and retrieval
  • Ontology learning and enrichment
  • Ontology and instance matching
• Applications of ML/DM on the Web of Data, including:
  • Social network analysis
  • Semantic sentiment analysis
  • Applications in Life Science and Social Science
• Semantic data mining
  • Using ontologies for the data mining process
  • Using Linked Open Data as background knowledge
• Cognitively-inspired learning approaches and exploratory search in the SW
• Ethics of SW and Big Data, analytics and ML/DM on these data, including: 
  • Privacy-preserving data mining on the SW
  • Discrimination/fairness-aware data mining on the SW

Research Track: Mobile Web, Internet of Things and Semantic Streams

Today large amounts of valuable data and sensor information still remain unused or are limited to specific application domains due to the wide variety of specific technologies and formats used. Hence, an aggregation of information from various sources is typically done manually and is often outdated or just static. This phenomenon is even more acute in the Internet of Things (IoT) – networks of devices with sensors and actuators – which brings in real-time information from the physical world that must be processed immediately.

The Semantic Web community has come a long way to ease integration of heterogeneous data, but the dynamic nature of sensing data poses a challenge for designing efficient methods for data representation, storage and analysis. In particular, sensory information is known to be faulty, and guaranteeing the accuracy of the results of the data analysis is also a hard problem. Another challenge is encoding and interpreting the geolocation information in the data.

In this track we welcome new ideas and results that combine stream data – available on the Web or coming from sensors and/or mobile devices – and semantic technologies for effective data description, representation (including geo-semantics), interpretation, integration, and development of novel applications (for example in future cities or the smart home). We invite high-quality submissions related to (but not limited to) one or more of the following topics:

• Architectures, middleware and data management for semantic streams, geo-semantics, and semantic sensor networks
• Application of semantic technologies, sensors and semantic streams, as e.g. environmental monitoring, scientific research or smart cities
• Context- and location-aware applications based on semantic technologies and geo-semantics
• Intelligent data processing and large sensor and mobile Web data analytics
• Real-time data and resource discovery with quality-aware information search and retrieval
• Using semantic enrichment and large-scale data analytics for processing or interpreting dynamic Internet of Things data
• Linked data and mashups over stream data
• Ontologies and rules for a dynamic Web
• Provenance of semantic data on the sensor and mobile Web
• Modelling and processing of uncertain and imprecise sensory data
• Modelling and processing of geolocations
• Scalability and performance of semantic technologies on sensor and mobile Web
• Semantic-based security, privacy and trust in mobile devices and applications
• Semantic event detection and response
• Semantics for the factory of the future, smart home, or future cities

Research Track: Services, Web APIs, and the Web of Things

The volume and diversity of data, devices, and services across the internet is rapidly increasing. Most Web sites provide programmatic access to their data and services over Web APIs that allow third parties to conveniently provide and consume data over the Web. Likewise low-cost personal devices for the home or personal health are currently some of the fastest growing sources and consumers of services and data. Increasing numbers of applications on personal devices share data through bespoke APIs, and recipe-based services such as IFTTT are facilitating novel yet unconventional integration between such services that are tailored to user needs and enhance the user experience.  The Web of Services is thus witnessing an unprecedented proliferation of highly heterogeneous, distributed, multi-tenant services. The current landscape offers a wide range of opportunities for the creation of valuable applications but it carries as well an outstanding number of challenges ranging from low-level integration concerns to higher-level issues around the management of highly distributed systems.  Thus, the vision encapsulated by the fact that anything may become a service (XaaS) to be provided as, or accessible through an API or Web Service is now a reality; yet the fundamental problems of semantic interoperability, discovery and autonomous, real-time exploitation are still unsolved.

This track is concerned with latest advances in semantic technologies that are suitable to address the challenges and opportunities raised. Topics of interest include but are not limited to:

• Solutions for bridging the gap between Web of Data and the Web of Services
• Semantic technologies for streamlining the creation of applications out of distributed, heterogeneous services
• Semantics for supporting the discovery, integration and composition of services and data APIs
• Semantics in support of Service Science
• Semantic technologies for the smart deployment and management of service-based applications
• Ontologies and Vocabularies for capturing the semantics of 
  • Web APIs and RESTful services
  • Business Services and Processes and
  • The services exposed by Things
• Controlled exposition of Linked Data through semantic Web APIs
• Privacy-aware service description, representation, processing and reasoning
• Semantics and services for trusted and secure cloud computing
• Semantics for cloud interoperability and management
• Context-aware elicitation of service semantics
• Agent-based use of semantics for services
• Personalized interaction with Internet-accessible things, services and business processes
• Semantics for supporting scientific workflows, business processes, and mash-ups
• Systems, tools and use cases exploiting semantics within service-oriented applications
• Automated mining and derivation of service semantics

In-Use & Industrial Track

Especially in their extended sense, Semantic Technologies are daily powering the world of information management, discovery and reuse,  bringing relevance, and opening up a range of new opportunities. Despite this, it is a fact that these techniques often remain difficult to apply, with organizations reaping the benefits often only after extensive investments.  With the very most of the world’s information today still handled in very primitive ways, an enormous potential for optimization of information handling seems to exists making search and reuse much easier and serendipitous. 

At the same time, a relatively “silent” revolution has happened on the World Wide Web since a few years: driven by industry, most of the Web pages which represent items for sale, or events or news are now marked up using vocabularies such as Schema.org or Opengraph, effectively implementing at huge scale at least part of the original vision of the Semantic Web. While this is bringing considerable benefits to the large web search engines, it is still to be seen if and how this markup could enable novel and useful scenarios.

The Semantic Web in Use and Industry track provides a common ground for both researchers and industry, taking the outcome of their research in the Semantic Web area to the market and adopting Semantic Web technologies in specific businesses, respectively. Submissions to the Semantic Web in Use and Industry track will provide a deeper insight on the exploitation of Semantic Web technologies in different economic sectors. Papers will be therefore evaluated on the basis of the impact of semantic technologies in the market and the society and on the extent to which  they address real-life problems. 

Expectations for papers in this track are to also evaluate or comment in a meaningful way how the Semantics based solution provides significant advantages over a solution that would otherwise be a state of the art IT, common practitioner, solution using no semantic technologies.

Topics:

• Best practices and lessons learnt from  the use of Semantic Web and Linked Data technologies in real world, industrial settings
• Industry and Business Trends related to the use of the Web of Data
• Comparison of Semantic technologies with alternative or conventional approaches for Industry and Business Analytics
• Pragmatics of deploying and using Semantic Technologies in real world scenarios
• Cloud Computing and Mobile apps based on Semantic Technologies
• Corporate Data and Knowledge Management over large, heterogeneous and diverse data
• Collaborative Content Management Systems, incl. Wikis
• Sensor Networks, Smart Cities and Open Government 
• e-Health and Life Sciences
• Sentiment Analysis and Social Networks in action
• Digital Libraries and Cultural Heritage
• Applications of Semantic Technologies in Multimedia Search, Media and Entertainment
• Security and Privacy
• Intelligent User Interfaces and Interaction Paradigms that profit from semantics and knowledge graphs
• Web of Data, Schema.org and Opengraph markup
• Case studies about the role of semantics in Machine Learning and Information Retrieval

Research Track: Cognition and Semantic Web

A key benefit of the Semantic Web is that it has greatly advanced machine understanding and interoperability, by providing methods for formalising and enriching information using ontologies and knowledge representation languages. However, the benefits of the Semantic Web will only be truly seen where this translates to effective support and augmentation of human capability in the use of Semantic Web data and technology. This holds especially for carrying out basic and more cognitively demanding tasks that involve knowledge retrieval, synthesis and modeling, in order to enable effective confident analysis, sense-making and critical decision-making.

This is getting more and more important in today's technology-rich world, where vast amounts of complex, inter-related data are generated on a daily basis, which humans must interact with in order to, e.g., in their personal lives: plan a journey, review and purchase products and services; in industry: obtain reliable, up-to-date information about markets in order to retain competitive advantage; at governmental level: capture the citizen voice for policy-making, and model and deliver improvements to public services in metropolitan and remote, rural areas; in the social arena: allow visitors to a region or an exhibition to discover and explore related artefacts in remote locations.

This track invites work at the intersection of the Semantic Web and Cognitive Science, and contributions from other fields that lead to human-centred approaches for managing the challenges encountered trying to harness the content of big data.

Topics:

• Cognitive strategies and models for user interfaces and interaction paradigms
• Cognitive strategies for reasoners
• Cultural aspects of categorization, theories of categorization, e.g., prototypes
• Framing and conceptual metaphor
• Analogy and analogical learning
• Semantic similarity
• Visualization techniques and user interfaces for exploratory, semantic search and information seeking in Linked Data
• Dialog systems
• Approximate reasoning
• Semantic heterogeneity and interoperability
• Cognitive constraints
• Symbol grounding
• Sense-making
• Emerging semantics
• Change and ontology evolution based on Cognition
• Recognition of knowledge patterns based on human reasoning
• Rational analysis and common-sense reasoning
• Personalization (of interfaces, reasoners and ontologies)
• Semantic translation, mapping, and alignment
• Spatial cognition
• Conceptual spaces
• Human competencies on the Semantic Web
• Heuristics based on human knowledge representation
• Consequences of task-switching, priming, and stopping-rules
• Neuroscience for the Semantic Web
• Linked Data Visualization

Research Track:  Human Computation and Crowdsourcing