General chair: 

Harald Sack (Hasso Plattner Institute (HPI), Germany​)

 

Program chairs: 

Mathieu D'Aquin (Knowledge Media Institute KMI, UK)

Eva Blomqvist (Linköping University, Sweden)

 

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ESWC is one of the key academic conferences to present research results and new developments in the area of the Semantic Web. For its 13th edition, ESWC will be back in Hersonissou, Crete, between Sunday May 29th  and Thursday June 2nd 2016.

The goal of the Semantic Web is to create a network of data and knowledge that interconnect across the Web and where both content and the meaning of content are manipulated by processes, services and applications. This endeavor naturally draws from and impact on many disciplines of computing (and connected areas), related to data and information management, knowledge engineering, machine intelligence, human knowledge and languages, softwares services and applications. We are therefore seeking contribution to research at the intersection of the Semantic Web and these areas, as described in the 9 core research tracks of the conferences, as well as demonstration of the impact of Semantic Web Technologies in concrete application and the industry, through the “In Use and Industrial” Track.

In addition to the main focus on advances in Semantic Web research and technologies, ESWC 2016 is looking to broaden the Semantic Web research community’s understanding and focus on current key areas directly affecting the development of the Semantic Web, namely: Trust, Privacy, Smart Cities and GeoSpatial Data. The conference therefore also includes 2 additional research tracks focusing on these specific aspects.

Indeed, through the interaction between Semantic Web technologies the Internet of Things and the Smart Cities the Semantic Web has the potential to reach beyond the borders of the traditional Web into the everyday life of people around the world. This puts a special attention on urban and geographical information, and on applications that can benefit from the meaningful exploitation of such data, as interconnected with other, heterogeneous and distributed data regarding all aspects of the life of a city (transport, health, education, energy, water, etc).

Such a broadening of the application and impact of Semantic Web technologies also emphasises the challenges posed to privacy and the trust relationship between agents (humans or machines) on the Web. While it is becoming crucial for such technologies, often employed to share, disseminate and integrate data from various sources, to become more privacy aware, we are also looking for research and concrete development in which semantics and the ability to interconnect information from across the web can support users in managing, assessing and enforcing privacy and trust in their activities.

Tracks:

Research Tracks:

• Vocabularies, Schemas, Ontologies - chairs: Krzysztof Janowicz and Rinke Hoekstra
• Reasoning - chairs: Uli Sattler and Thomas Schneider
• Linked Data - chairs: Monika Solanki and Aidan Hogan
• Social Web and Web Science - chairs: Claudia Müller-Birn and Steffen Staab
• Semantic Data Management, Big data, Scalability - chairs: Philippe Cudré-Mauroux and Katja Hose
• Natural Language Processing and Information Retrieval - chairs: Nathalie Aussenac-Gilles and Pablo N. Mendes
• Machine Learning - chairs: Claudia d'Amato and Jens Lehmann
• Mobile Web, Sensors and Semantic Streams - chairs: Raúl García Castro and Jean-Paul Calbimonte
• Services, APIs, Processes and Cloud Computing - chairs: Maria Maleshkova and Karthik Gomadam

Special Tracks:

• Trust and Privacy - chairs: Sabrina Kirrane and Pompeu Casanovas
• Smart Cities, Urban and Geospatial Data - chairs: Carsten Kessler and Vanessa Lopez

In Use and Industrial Track:

• chairs: Mike Lauruhn and Jacco van Ossenbruggen

Important dates:

• Compulsory abstract submission for all papers: Friday 11th December 2015 - 23:59 Hawaii Time
• Compulsory full paper submission:  Friday 18th December 2015 - 23:59 Hawaii Time
• Authors rebuttal: Friday 29th Jan - Friday 5th Feb 2016 - 23:59 Hawaii Time
• Acceptance notification: Monday 22nd February 2016
• Camera ready: Monday 7th of March 2016 - 23:59 Hawaii Time

 

Submission Information

ESWC 2016 welcomes the submission of original research and application papers dealing with all aspects of representing and using semantics on the Web. We encourage theoretical, methodological, empirical, and applications papers. Submitted papers should describe original work, present significant results, and provide rigorous, principled, and repeatable evaluation. We strongly encourage and appreciate the submission of papers incorporating links to data sets and other material used for evaluation as well as to live demos and software source code. ESWC will not accept research papers that, at the time of submission, are under review for or have already been published in or accepted for publication in a journal or another conference. The proceedings of this conference will be published in Springer's Lecture Notes in Computer Science series.

Papers should not exceed fifteen (15) pages in length and must be formatted according to the guidelines for LNCS authors. Papers must be submitted in PDF (Adobe's Portable Document Format) format. Papers that exceed 15 pages or do not follow the LNCS guidelines will be automatically rejected without a review. Each paper will be submitted in two steps: an abstract first and the full paper one week later. The abstract submission is compulsory for every full paper submitted. Abstracts alone will not be reviewed and only fully submitted papers will be considered. Authors of accepted papers will be required to provide semantic annotations for the abstract of their submission - details of this process will be given on the conference Web page at the time of acceptance. At least one author of each accepted paper must register for the conference. More information about the Springer's Lecture Notes in Computer Science (LNCS) are available on the Springer LNCS Web site.

In addition to PDF, authors have the option to also submit a version of their paper in the RASH format.
RASH (https://github.com/essepuntato/rash) is a markup language that restricts the use of HTML for writing academic research articles. It is possible to include also RDF statements as RDFa annotations and/or as Turtle, JSON-LD and RDF/XML triples by using the tag "script". Tools exist for converting RASH/compliant ODT to latex (see http://dasplab.cs.unibo.it/rocs).
Accepted papers for which there is a RASH version will be made available online through the ESWC website. For RASH submission, please submit a zip archive containing an HTML file compliant with the RASH format with all the additional stylesheets and scripts for guaranteeing a correct visualisation of the document on browsers.

Submissions and reviewing will be supported by the EasyChair system: https://easychair.org/conferences/?conf=eswc2016

 

 

ESWC 2016 Tracks

Vocabularies, Schemas, Ontologies

Krzysztof Janowicz - University of California, Santa Barbara, USA

Rinke Hoekstra - VU University Amsterdam, Netherlands

 

Reasoning

Uli Sattler - University of Manchester, UK

Thomas Schneider - Universität Bremen, Germany

 

 

Linked Data

Monika Solanki - University of Oxford, UK

Aidan Hogan - Universidad de Chile, Santiago de Chile, Chile

 

 

Social Web and Web Science

Claudia Müller-Birn - Freie Universität Berlin, Germany

Steffen Staab - Universität Koblenz, Germany

 

 

Semantic Data Management, Big data, Scalability

Phillipe Cudre-Mauroux - University of Fribourg, Switzerland

Katja Hose - Aalborg University, Denmark

 

 

Natural Language Processing and Information Retrieval

Nathalie Aussenac Gilles - IRIT - Université Toulouse, France

Pablo N. Mendes - IBM, USA

 

 

Machine Learning

Claudia d'Amato - University of Bari, Italy

Jens Lehmann - Universität Leipzig, Germany

 

 

Mobile Web, Sensors and Semantic Streams

Raúl García Castro - Universidad Politécnica de Madrid, Spain

Jean-Paul Calbimonte - École Polytechnique Fédérale de Lausanne, Switzerland

 

 

Services, APIs, Processes and Cloud Computing

Maria Maleshkova - AIFB, Karlsruhe Institute of Technology, Germany

Karthik Gomadam - Accenture Technology Labs, USA

 

 

In-use & Industrial Track

Mike Lauruhn - Elsevier Labs, Netherlands

Jacco van Ossenbruggen - Centrum Wiskunde & Informatica (CWI), Amsterdam, Netherlands

 

 

Trust and Privacy

Sabrina Kirrane - Wirtschaftsuniversität Wien, Austria

Pompeu Casanovas - Universidad Autónoma de Barcelona, Spain

 

 

Smart Cities, Urban and Geospatial Data

Carsten Kessler - Hunter College, CUNY, New York, USA

Vanessa Lopez - IBM, Ireland

 

Research Track: Vocabularies, Schemas, Ontologies

Description:

Ontologies, schemas, and vocabularies play a central role for the Semantic Web. They ensure reusability of (linked) data and knowledge and enable the design and implementation of robust and intelligent applications. Key object of study is the effective construction of ontologies. They can be learned from linked data or text, extracted from legacy datasets, be re-implementations of existing data models, or developed from scratch. Ontology engineering emphasises a knowledge acquisition perspective and studies the way in which ontologies can be designed in collaboration with domain experts and end users. This gives rise to ontology engineering methodologies, best practices and design patterns. A third strain of research develops theories, methods and algorithms for ontology matching and alignment, versioning, evolution and modularisation.

This track aims to address innovative research on ontologies, vocabularies and schemas for the Semantic Web, Linked Data and semantic technologies in general. We welcome both theoretical and more practical research papers.

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

•  Languages, tools, programming paradigms and methodologies for (collaborative) ontology engineering
• Ontology matching, alignment, and merging
• Evolution of vocabularies, schemas, and ontologies
• Ontology repositories and ontology search
• Knowledge patterns
• Ontology design patterns
• Pattern mining and extraction from (linked) data
• Ontology- and schema-based data integration and curation
• Knowledge acquisition (extraction, learning)
• Ontology management, maintenance, and reuse
• Evaluation of ontology and schema quality
• Ontology-driven applications
• Ontologies, schemas, and vocabularies a specific domain (publishing, law, bio-informatics, medicine, geosciences, etc.)
• Ontology-/schema-/vocabulary-based information retrieval
• Semantic Web (e.g., schema-centric) programming
• The role of ontology in cyber-infrastructure

 

Research Track: Reasoning

Description:

The Reasoning track invites submissions on all topics concerning reasoning related to ontologies, rules and the Web. Contributions can range from theoretical advances to empirical evaluations. Papers with a strong relation to other tracks, in particular to the In-Use & Industrial Track but a clear focus on reasoning are also welcome. 

We are inviting the submission of papers that describe 

• algorithms, 
• implementations, 
• optimisation, or 
• evaluations​ of web reasoning systems, i.e., of procedures that take, as an input, 
• ontologies (usually in RDF, RDFS, OWL, RIF) and 
• test entailments or answer queries. 

We are interested in relevant properties of these systems, for example 

• soundness, 
• completeness, 
• computational complexity and optimality, 
• performance, 
• robustness, 
• scalability, 
• effectiveness. 

The following variations are clearly within the scope of this track: 

• extensions to the input of existing formalisms and modifications to the usual semantics, e.g., to deal with noise, exceptions,
• user preferences, vagueness, probabilities. 
• different settings for the input of the system, e.g., to deal with data streams, distributed ontologies, incremental reasoning. 
• non-standard reasoning tasks, e.g., modularisation, explanation, learning, abduction, induction. 

 

Research Track: Linked Data

Description:

Linked Data (LD), as a set of best practices for sharing and publication of structured data on the Web, has gained significant momentum over the past years. From a research perspective, Linked Data still faces several challenges related to, for instance, evolution and preservation, discovery and long-term maintenance of links within and across datasets, scalable query and storage mechanisms, quality assessment and management, effective publishing methodologies, efficient consumption, access-restricted querying, as well as inferencing. In order to improve take-up and reuse of Linked Data, novel solutions and approaches addressing the aforementioned issues have to be investigated. The track calls for research submissions advancing the state-of-the-art in the Linked Data field, in particular related to the following, non-exhaustive list of topics of interest:

 

Topics:

• Consumption and publication of Linked Data
• Extraction, linking and integration of Linked Data
• Creation, storage and management of LD and LD vocabularies
• Searching, querying, and reasoning over decentralized Linked (Open) Data
• Dataset profiling and description
• Data quality, validation and data trustworthiness
• Dynamics and evolution of LD
• Analyzing, mining, and visualizing LD
• LD and the Social Web
• Scalability issues relating to Linked Data
• Provenance, privacy, and rights management
• Leveraging RDFa, JSON-LD and Microdata
• Database, IR, NLP and AI technologies for LD

 

Research Track: Social Web and Web Science

Description:

Over the last decade, the initial idea of the Web as an instrument for information sharing was replaced by the Web as a social space. In this social space people collaborate, share, create data and content, and interact. With Semantic Web technologies, people can even immerse into the Web since the needed knowledge is provided instantly. Flagship initiatives such as LOD, Dbpedia, or Wikidata show how semantic technologies are on a large scale. Thus, the Web is changing from a socio-technical into a socio-semantic space.  

In this track, we want to make this transition visible in two regards. First, we want to show how semantic web technologies help us to understand the social space. Second, we want to reveal how the semantic web emerges in the different corners of the web. Such emergence can be the usage of semantic web standards, it can relate to created tools that use semantic web technologies, or communities that create semantic data.

We invite a broad range of papers that allow us to unfold the different perspectives on the Web as a socio-semantic place including, but not limited to:

-    Semantic Web data perspectives

• Investigating collaboratively and/or distributedly created data such as LOD, schema.org or wikidata
• Tracing the usage of ontologies (e.g. FOAF), languages (e.g. OWL dialects) or other Semantic Web specifications
• Focussing on the dynamics of data creation 

-    Semantic tools and communities perspectives

• Studying semantic data usage
• Describing collective action in the Semantic Web
• Evaluating the dynamics of participation in the Semantic Web
• Analysing contributions in communities that create structured knowledge bases, e.g. Semantic MediaWiki communities or​ ICD-11

-    Perspectives of observing and managing the Social Web

• Revealing community characteristics (e.g. from Twitter and Facebook) 
• Understanding and describing users (e.g. motivation, social roles)
• Characterizing the structural and group properties of social platforms
• Evaluating content in online communities or crowdsourcing systems (e.g. quality, trust)
• Examining interdependencies of user, content and/or structure ​

 

Research Track: Semantic Data Management, Big data, Scalability

Description:

Semantic data management is playing a crucial role in the realization of the Semantic Web vision. 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, systems have to face increasing volumes of rapidly changing RDF datasets as well as complex workloads that may require inference services. Moreover, at such scale, data analysis and curation also require designing novel methods and implementing dedicated systems.

In this context, this track aims at gathering 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 very large datasets.

Topics of interest include, but are not limited to:

• Systems for (distributed) Semantic Data Management
• Scalable Analysis of the Web of Data
• Query processing of Semantic Data
• Semantic access to Legacy Data
• Management of Big 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 and 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

Description:

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 involving 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; word sense disambiguation
• 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 (e.g., entity / relationship / event extraction)
• Language processing of social network / social media 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 (e.g. finding entities by description, interpreting query keywords, etc.)
• 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
• Distributional Representations of Semantics (e.g. Distributional Semantics, Vector Space Models, Word Embeddings)
• Graph-based models of semantics
• Semantic similarity, relatedness, entailment, paraphrasing and analogies

 

Research Track: Machine Learning

Description:

In the perspective of the Semantic Web (SW) as a Web of Data, Machine Learning (ML)  and Data Mining (DM) methods become increasingly important. 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 the large scale of Web data and provides tools for big data analytics. The prospect is that innovative solutions, based on the development of ML/DM methods 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:

Topics:

• (Statistical) Relational learning for the Web of Data
• Semi-supervised, Unbalanced, Inductive Learning for the Semantic Web
• Data mining and knowledge discovery in Linked data and ontologies
• Feature extraction, pre-processing and transformation of SW data
• Machine learning for ontology matching, instance matching, search and retrieval
• Semantic Web usage mining, ranking methods 
• Search, Retrieval and Recommendation on the Web of Data
• Evaluation and benchmarking of ML/DM models
• Deep Learning for the Semantic Web
• Big Data analytics involving Linked Data
• Scalable ML/DM algorithms for the web of data
• Distributed architectures for mining the web of data
• Machine learning method for handling uncertain knowledge
• Approximate inductive reasoning on ontologies
• Combination of logic reasoning and ILP
• OWA vs. CWA in learning
• Knowledge base creation and maintenance using ML/DM
• Machine learning for construction, enrichment, refinement, interlinking, debugging, and repair of Semantic Web knowledg bases
• Link Prediction and Efficient indexing in the Linked Data Cloud
• Semantic data/ontology mining
• 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, Sensors and Semantic Streams

Description:

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:

 

• Real-time data and resource discovery with quality-aware information search and retrieval
• Integration of semantic sensor networks with Internet/Web of Things
• Ontologies for sensors and IoT environments
• RDF stream processing semantics and query processing
• Using semantic enrichment and large-scale data analytics for processing or interpreting dynamic Internet of Things data
• Linked data and mashups over streaming data
• 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
• 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
• Stream reasoning algorithms and techniques

 

Research Track: Services, APIs, Processes and Cloud Computing

Description:

The volume and diversity of data, devices, and services across the internet is rapidly increasing. In this context Web Services and APIs play a crucial role. An increasing number of Web applications and platforms now provide programmatic access to developers to their data and services over Web APIs. Likewise low-cost sensors and devices for the home or personal health are currently some of the fastest growing sources and consumers of services and data. Applications built on top of personal devices share data through bespoke APIs have facilitated novel yet unconventional integration between services that are tailored to specific user needs and enhance the user experience. The Web of Services is witnessing an unprecedented proliferation of highly heterogeneous, distributed, multi-tenant services. While the current landscape offers a wide range of opportunities for the creation of valuable applications, it also faces an outstanding number of challenges ranging from low-level integration concerns to higher-level issues around the management of highly distributed systems.  We are moving closer to achieving the vision that anything may become a service to be provided as, or accessible through an API or Web Service; yet some of 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 in the context of Services on the Web.

 

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 using distributed, heterogeneous services
• Semantics for supporting the discovery, integration and composition of services and data APIs
• Semantic technologies for the deployment and management of service-based applications
• Ontologies and Vocabularies for capturing the semantics of Web APIs and RESTful services
• Automated mining and derivation of service semantics
• API management for streaming services 
• Services as the interoperability and integration point for  the Internet of Things
• Exposing and integrating 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
• Semantics for supporting scientific workflows, business processes, and mash-ups
• Systems, tools and use cases exploiting semantics within service-oriented applications
• Semantics for provisioning and managing microservices including port and adapter development
• API deployment and management including API scaling and infrastructure provisioning
• Data pipelines for asynchronous and event driven APIs

 

Research Track: In-use & Industrial Track

Description:

The Semantic Web In-Use and Industry track provides a common ground for both researchers and industry. It is a forum to report on Semantic Web research taken to the market, or on any other relevant uptake of semantic technologies outside the lab. 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 measurable impact of semantic technologies, and on the extent to which they address real-life problems. Submitted papers in this track should evaluate or reflect on the pros and cons of the approach when compared to existing solutions that do not use 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: Trust and Privacy

Description:

Semantic technology is used not only to represent knowledge in a machine-readable format but also to support the publication and interlinking of data. However, large scale publishing and interlinking of machine-readable data on the Web brings with it particular challenges with respect to trust, privacy and security. To date, in spite of all the work carried out to implement the so-called General Data Protection Reform (GDPR), very little research has been specifically conducted into the legal, ethical and societal impact/potential of semantic technologies. The aim of this track is to provide a forum for researchers to discuss both challenges and opportunities, and to unveil practical and exploratory usage of semantic technology with respect to trust, privacy and security. The track invites papers in the fields of computer science, law, humanities and social sciences, as well as other relevant fields. Multidisciplinary papers are particularly welcome.

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

• Languages, tools, programming paradigms and methodologies for (collaborative) ontology engineering
• Ontology matching, alignment, and merging
• Evolution of vocabularies, schemas, and ontologies 
• Ontology repositories and ontology search
• Knowledge patterns
• Ontology design patterns
• Pattern detection and extraction from Linked Data
• Ontology- and schema-based data integration and curation
• Knowledge acquisition (extraction, learning)
• Ontology management, maintenance, and reuse
• Evaluation of ontology and schema quality
• Ontology-driven applications
• Ontologies and vocabularies a specific domain (publishing, law, bio-informatics, medicine, etc.)
• Ontology-/schema-/vocabulary-based information retrieval
• Semantic Web (e.g., schema-centric) programming

 

Research Track: Smart Cities, Urban and Geospatial Data

Description:

More than half of the world’s population is already living in urban areas today. UN projections show that this proportion will grow to 66% by 2050, adding another 2.5 billion people to our cities. Geospatial data provided by sensor networks, different remote sensing technologies, citizen scientists, social networks, as well as Open Data initiatives helps cities address these challenges and transform into smart cities.
However, in such a diversity of information, it is a fact that large amounts of valuable open data and sensor information remain unused, and aggregation of information from various sources is typically limited to specific application domains, with organizations and cities reaping the benefits often only after extensive investments. With the very most of the world’s information today still handled in siloes, there is an enormous potential for better information management, search, discovery and reuse of heterogeneous urban data using Semantic Technologies, in order to make cities more intelligent, innovative and integrated beyond the boundaries of isolated applications.
In this track, we invite submissions that address the use of Semantic Web technologies in the context of this transformation process. Submissions to this track should contain original, unpublished research that shows how urban and smart city applications can benefit from Semantic Web technologies. Authors are strongly encouraged to include concrete application examples, ideally using real data, in their papers. Papers in this track will be evaluated on the basis of the impact of semantic technologies in the society and the extent to which they address real-life problems in the context of cities. Papers are also expected to evaluate or provide a deeper insight on the significant advantages of a semantic solution over state of the art, common practitioner no semantic solutions.

Topics

• Semantic integration and processing of remotely sensed data and data from in-situ sensors
• Semantic models for spatial-temporal change
• The city as an API
• Semantics of urban sensor networks
• Semantic integration of distributed urban data
• Semantic analysis of data streams
• Semantic Web applications addressing urban topics such as transport, energy, building, safety, water, food, waste,​ or emissions
• Semantics for citizen-centric Smart cities
• Application of semantic technologies, sensors and semantic streams for e-Health, Life Sciences, e-Government,​ Environmental Monitoring, Cultural Heritage, Utility Services or Social Sensing
• Intelligent User Interfaces and Interaction Paradigms that profit from semantics and knowledge graphs over Web Data,​ open government and corporate data relating to cities
• Context- and location-aware (mobile) applications based on semantic technologies and geo-semantics
• Provenance, access control, trust and privacy-preserving issues in smart cities
• Semantic-based cloud applications for Smart Cities
• Semantic reasoning, event detection, knowledge extraction and analytics for smart city platforms
• Big data and scaling out in semantic cities. Managing real time and historical city data using knowledge​ representation models
• Semantic platforms, knowledge acquisition, publishing, consumption, evolution and maintenance of city data