General chair: 

Eva Blomqvist (Linköping University, Sweden​)
eva.blomqvist@liu.se

 

Program chairs: 

Diana Maynard (University of Sheffield, UK)
d.maynard@sheffield.ac.uk

Aldo Gangemi (Paris Nord University, France and ISTC-CNR, Italy)
gangemi@lipn.univ-paris13.fr

 

ESWC is one of the key academic conferences to present research results and new developments in the area of the Semantic Web. For its 14th edition, ESWC will be back in Portotoz, Slovenia from Sunday 28th May to Thursday 1st June 2017.

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 its meaning are manipulated by processes, services and applications. This endeavour naturally draws from and impacts on many disciplines of computing (and connected areas), related to data and information management, knowledge engineering, machine intelligence, human knowledge and languages, software 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 applications and in industry, through the “In Use and Industrial” Track.

In addition to the main focus on advances in Semantic Web research and technologies, ESWC 2017 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 Multilinguality and Transparency. The conference therefore includes 2 additional research tracks focusing on these specific aspects.

In order to manage data available in multiple languages, a semantic normalization and reconciliation of content across languages and cultures is needed, enabling the Semantic Web to reach beyond the borders of the traditional Web and into the everyday life of people around the world. Semantic technologies provide a means to improve the management of multilingual content. The introduction of this theme integrates well with and builds upon last year’s special track on Smart Cities, Urban and Geospatial Data, integrating data from all kinds of aspects of the life of a city.

Also building on both that track and last year’s other special track on trust and privacy, transparency is a key part of Open Government Data initiatives. Transparency is concerned with relationships between members of the public and both commercial and authoritative entities, with objectives varying from enabling trust to ensuring accountability. Many aspects of Semantic Web technologies can be employed to integrate, interpret and exploit multiple pieces of information for use by the public.

Tracks:

Research Tracks:

• Vocabularies, Schemas, Ontologies - chairs: Helena Sofia Pinto and Silvio Peroni
• Reasoning - chairs: Uli Sattler and Umberto Straccia
• Linked Data - chairs: Jun Zhao and Axel Ngonga Ngomo
• Social Web and Web Science - chairs: Harith Alani and Wolfgang Nejdl
• Semantic Data Management, Big data, Scalability - chairs: Maria Esther Vidal and Juergen Umbrich
• Natural Language Processing and Information Retrieval - chairs: Claire Gardent and Udo Kruschwitz
• Machine Learning - chairs: Claudia d’Amato and Michael Cochez
• Mobile Web, Sensors and Semantic Streams - chairs: Emanuele Della Valle and Manfred Hauswirth
• Services, APIs, Processes and Cloud Computing - chairs: Peter Haase and Barry Norton

Special Tracks:

• Multilinguality - chairs: Philipp Cimiano and Roberto Navigli
• Semantic Web and Transparency - chairs: Mathieu d'Aquin and Giorgia Lodi

In Use and Industrial Track:

• chairs: Paul Groth and Paolo Bouquet

Important dates:

• Compulsory abstract submission for all papers: Wednesday 7th December 2016 - 23:59 Hawaii Time
• Compulsory full paper submission:  Wednesday 14th December 2016 - 23:59 Hawaii Time
• Authors rebuttal: Friday 27th Jan - Sunday 5th Feb 2017 - 23:59 Hawaii Time
• Acceptance notification: Monday 20th February 2017
• Camera ready: Tuesday 7th of March 2017 - 23:59 Hawaii Time
   

Submission Information

ESWC 2017 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 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 submit their papers in HTML format. Authors who are new to HTML submissions, may find the following useful:

• The Research Articles in Simplified HTML (RASH) Framework provides a terse markup language for writing scientific articles in (X)HTML. 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).
• dokieli is a client-side editor for decentralized article publishing in HTML+RDFa, annotations and social interactions, compliant with the Linked Research initiative. There are a variety of examples available online https://github.com/linkeddata/dokieli/wiki#examples-in-the-wild, where the LNCS author guidelines as a template may be particularly useful.

Accepted papers for which there is an HTML version will be made available online through the ESWC website. For HTML submission, please submit a ZIP archive containing an HTML file with all the additional stylesheets and scripts for guaranteeing a correct visualization of the document in browsers.

Please note that independent of the format used, we require submissions to abide by the permitted number of pages, font sizes, font selection, margins, etc. This is to ensure visual consistency of the proceedings as well as to have comparative page limits.

Submissions and reviewing will be supported by the EasyChair system: https://easychair.org/conferences/?conf=eswc2017

ESWC 2017 Tracks

Vocabularies, Schemas, Ontologies

Helena Sofia Pinto - Universidade de Lisboa

Silvio Peroni - University of Bologna, Italy

 

 

Reasoning

Uli Sattler - University of Manchester, UK
Umberto Straccia - ISTI-CNR, Italy

 

 

Linked Data​

Jun Zhao - University of Oxford, UK
Axel Ngonga Ngomo - Universität Leipzig, Germany

 

 

Social Web and Web Science​

Harith Alani - The Open University, UK
Wolfgang Nejdl - Leibniz Universität Hannover, Germany

 

 

Semantic Data Management, Big data, Scalability​

Maria Esther Vidal - University of Bonn, Germany and Universidad Simón Bolívar, Venezuela
Juergen Umbrich - Vienna University of Economics and Business, Austria

 

 

Natural Language Processing and Information Retrieval​

Claire Gardent - CNRS, France
Udo Kruschwitz - University of Essex, UK

 

 

Machine Learning​

Claudia d’Amato - University of Bari, Italy
Michael Cochez  - Fraunhofer Institute for Applied Information Technology FIT, Germany

 

 

Mobile Web, Sensors and Semantic Streams​

Emanuele Della Valle - Politecnico di Milano, Italy
Manfred Hauswirth - TU Berlin, Germany

 

 

Services, APIs, Processes and Cloud Computing​

Peter Haase - metaphacts GmbH, Germany
Barry Norton - Elsevier, UK

 

 

Multilinguality​

Philipp Cimiano - Universität Bielefeld, Germany
Roberto Navigli - Sapienza University of Rome, Italy

 

 

Semantic Web and Transparency​

Mathieu d'Aquin - The Open University, UK
Giorgia Lodi - CNR, Italy

 

 

In-use & Industrial Track

Paul Groth - Elsevier Labs, Netherlands
Paolo Bouquet - Trento University, Italy ​

Research Track: Vocabularies, Schemas, Ontologies

Description:

Ontologies, schemas, and vocabularies play a central role in the Semantic Web. They ensure reusability of (linked) data and knowledge, and enable the design and implementation of robust and intelligent applications. A 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 and anti-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 in a specific domain (publishing, law, bio-informatics, medicine, geosciences, cultural heritage, digital humanities, food and agriculture, Internet of things, media and entertainment, and other commercial and industrial sectors)
• Ontology/schema/vocabulary-based information retrieval
• Semantic Web (e.g., schema-centric) programming
• The role of ontologies in cyber-infrastructure

We welcome papers describing ontologies. In particular, the ontology described must be available for reviewing and the paper must comply with at least one of the following types:

1. Systematic and clear description of the ontology, including its construction process, describing a particular complex and challenging domain (e.g. formalising 150 pages of a particular standard). Focus: how the ontology has been developed, which new techniques have been adopted to deal with the complexity of the domain, how inconsistencies (if any) have been solved, etc.
2. Introduction of an (even simple) ontology in the context of its applications worldwide. Focus: in which applications it has been used, what are the advantages of adopting such ontology, outcomes of a comparative evaluation of adopting such an ontology for addressing particular tasks (which should at least be compared with a run of the same tasks without the use of such an ontology), etc.

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, 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 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, uncertainty
• 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, non-monotonic reasoning, paraconsistency. 

Research Track: Linked Data

Description:

Linked Data is defined as a set of best practices for the sharing and publication of structured data on the Web. This paradigm is now being used in an increasing number of applications. Nevertheless, various challenges related to the extraction, storage,  evolution, preservation, and discovery of Linked Data (including links and provenance information) still remain. This track invites research submissions pertaining to the generation/extraction of Linked Data from other types of data sources, the generation, (long-term) maintenance and curation 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 of Linked Data. The research submissions submitted to this track should describe significant advancement over the state-of-the-art in the Linked Data field, related to the following non-exhaustive list of topics of interest:

Topics:

• Consumption and publication of Linked Data (LD)
• Extraction, linking and integration of LD
• Creation, storage and management of LD and LD vocabularies
• Searching, querying, and reasoning over decentralized LD
• 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:

The tremendous increase in using the Web for establishing and maintaining social interactions has transformed the Web from a technical platform into a socio-technological phenomenon. The social web is a prominent example for that development, and now constitutes the major part of global Web activities. Web Science has recently emerged as an interdisciplinary approach that aims to expand our understanding of this social phenomena from various perspectives, by studying the Web as a vast information network of people and communities. This track invites contributions that explore the use of semantic techniques in the study of the Web as a socio-technical platform and phenomenon, as well as the role that socio-technical issues play in influencing semantic web evolution, adoption, and technologies. Topics of interest include but are not limited to the investigation of the following:

• Collaborative creation of semantic knowledge (e.g. Linked Open Data, schema.org or wikidata)
• Crowdsourcing semantics; methods, dynamics, and challenges
• User consumption patterns of Semantic Web data, languages, and ontologies
• Inter-influences of users and technologies in the Semantic Web
• Incentives, usage, and social processes around Linked Open Data
• Ubiquitous Social Semantic Web
• Social media semantic processing and analysis
• Analysis, evaluation, and management of online communities using semantic data and techniques 
• Semantic social network analysis, representation, and management
• Semantic modelling and understanding of users
• Mining semantics from social data 
• Semantically enabled social platforms and applications: 
• Querying, mining and analysis of user generated data and dynamics
• Semantic computational social science 

 

Research Track: Semantic Data Management, Big data, Scalability

Description:

Semantic data management comprises approaches that exploit the meaning of data in different data-driven tasks, e.g. query processing, data storage, or integration. Although there exist effective solutions for semantic data management, Big Data characteristics like volume, variety, velocity and veracity  prevent semantic data management from being used on a large scale. In particular, data-driven tasks that require inference services may be negatively affected whenever Big Data dimensions rapidly change, and novel methods are required to address these issues efficiently. The main goal of this track is to gather experts from the Semantic Web, Databases, and Artificial Intelligence communities to discuss open problems as well as innovative solutions for semantic data management on Big Data sources.   

Topics of interest include, but are not limited to:

• Distributed infrastructures for Semantic Data Management over Big Data sources
• Semantic Data Management Techniques for Big Data
• Query processing of Semantic Data
• Access Control and Privacy in Semantic Data
• Synchronization Models 
• Semantic Data Integration and Quality Assessment 
• Traceability and Trustworthiness
• Ranking of Semantic Data
• Semantic Data Analytics
• Storage Models for Semantic Data
• Semantic Searching and Browsing
• Management of Provenance of Semantic Data 
• Semantic Data Management for Dynamic and Temporal Data
• Semantic Data Management and Polyglot Persistence
• Empirical Evaluation of Semantic Data Management Techniques
• Benchmarks for Semantic Data Management Techniques
• Innovative applications for Semantic Data Management problems on a large scale, e.g. healthcare, biomedical, financial, and industrial manufacturing domains 

Research Track: Natural Language Processing and Information Retrieval

Description:

Natural Language Processing (NLP), Information Retrieval (IR) and Semantic Web (SW) developments and technologies are driven and represented by three fairly distinct research communities which until recently have had little overlap. This has started to change in recent years, e.g. through the development of large-scale knowledge graphs, the move towards Natural Language interfaces in search, and the rise of large-scale linked data stores, including a lot of linguistic data. There are many ways in which each of these three communities could contribute to each other.The main goal of the NLP&IR track is to foster a closer interaction between the NLP, IR and SW communities, which has the potential to lead to on one hand to major advances in combining vast amounts of structured background knowledge and reasoning with statistical approaches applied to realistic applications and user needs, on the other hand to accurately populate (or interact with) the SW with data and ontologies extracted by means of NLP or IR techniques.

Topics include, but are not limited to:

• Combining knowledge-driven and data-driven approaches to NLP and IR
• Dialogue systems and intelligent personal assistants exploiting Semantic Web Data
• Information Extraction from Semantic Web Data
• Entity/event coreference and linking
• Evaluation of NLP and IR systems using Semantic Web Data and vice versa
• Exploiting/creating lexical resources for the Semantic Web
• Integrating ontologies / Linked Open Data with Language Resources
• Knowledge-driven NLP/IR applications for user-generated content and social media
• Knowledge Extraction from Text
• Natural Language Generation from Semantic Web Data
• Natural Language Interfaces for the Semantic Web
• Natural Language Processing services using Linked Open Data
• Natural Language Search and Question Answering
• Ontology learning and ontology population
• Ontology lexicalization and localization
• Opinion mining using Semantic Web technologies
• Querying Semantic Web Data using natural language
• Representation of meaning and/or linguistic data on the Semantic Web
• Semantic annotation of texts exploiting Linked Data
• Semantic search using knowledge graphs
• Summarization approaches using structured knowledge

Papers must demonstrate clear and explicit relevance to the Semantic Web (e.g. not only using Web as a platform or as a corpus, but also using SW data or ontologies, KR languages, formal representation or linking of resources). Contributions that do not meet these criteria will be rejected.

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 have 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:

 

• (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 knowledge 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:

The Internet of Things (IoT) is quickly becoming a major information source for all application domains. Recently, describing, integrating and using this data has been the focus of much research in the Semantic Web community. Special interest has been devoted to dealing with dynamicity, resource constraints; different proprietary data formats and platforms; time and location dependency of data; limited validity periods of data; faulty, imprecise and contradicting information; and integration with background (slowly evolving) knowledge. The situation is similar, though a little less severe, for mobile environments. Efficient methods for data representation, storage and analysis have been proposed, taking into account these problems, along with real-time processing of spatio-temporal semantic data, specifically in such harsh and resource-constrained environments. In this track, we invite researchers to propose new, ground-breaking 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. 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 IoT data
• Linked data and mashups over streaming data
• Architectures, middleware and data management for semantic streams, geo-semantics, and semantic sensor networks
• Easy-to use platforms for processing (mobile) stream data and design of applications
• Application of semantic technologies, sensors and semantic streams, e.g. for 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
• Publishing semantic streams on the Web
• RDF stream compression techniques
• Integrating streams and large, slowly-evolving, background data
• Sensor stream and social stream data integration

Research Track: Services, APIs, Processes and Cloud Computing

Description:

Computing in the 21st Century, both in a Web context and otherwise, must deal with the classical aims of abstraction and composition, but also with the need to be resilient to hardware and network failures and to be flexibly scalable with respect to demand across the world. This concerns semantic technologies in two ways: semantics-based applications must be architected to be scalable and composable; semantic technologies can also help to build for composition, resilience and scalability. Cloud-based services now provide for a great deal of storage, management, analysis and exposure of data-driven applications. Examples of these are, respectively, the provision of: large open datasets with tiered billing and accounting; replicated and resilient database clusters that can be provisioned to scale to update/query loads by the hour; compute clusters capable of running distributed algorithms for analysis, and also provision of GPU hardware (via Amazon, Microsoft and others) for analytics; load-balanced web servers and caches, which is the means by which a large proportion of the Web is now served. Applications that make use of semantics must be just as easily and flexibly provisioned using such services if they are to compete with current cloud-backed data-driven applications. The description of services using semantics is a research area with a long pedigree, while, in restricted settings such as scientific and biomedical workflows, the semantic description of processes has been found useful. Google’s TensorFlow is just one recent development showing how the abstract description of processing can be more generally useful, and indeed provides a clear light to the future in which ‘flows’, describing very complex processing of large datasets, will be provisioned in a cloud-based IT infrastructure. The time is ripe for explicit formal semantics in the description of the components of data analysis, and their combination into processes, to show their worth. Finally, blockchain-based technologies have recently seen a great deal of interest, beyond their original applications to crypto-currency, where decentralised trust must be established. This opens up possibilities for synergies with semantic technologies in establishing trust in distributed systems built from heterogeneous services, and presents open questions in defining the behaviours expected in decentralised contracts which have much in common with the aims of semantic service descriptions. 

Topics of interest include but are not limited to:

• Solutions for bridging the gap between Web of Data and the Web of Services
• Case studies in the architecture of large-scale semantic and data-driven solutions
• Semantic enhancement of the description and discovery of (micro)services, including via lightweight virtualisation and container technologies
• Descriptions of novel services and containers of use to the semantics community
• Semantic technologies for streamlining the creation of applications using distributed, heterogeneous services
• Applications of blockchain technology with ontology-based semantics in service provision and consumption
• Semantics for supporting the discovery, integration and composition of services
• Semantic technologies for the deployment and management of service-based applications
• Ontologies and Vocabularies for capturing the semantics of services and processes
• Automated mining and derivation of service semantics and process behaviour
• Streaming data processing
• 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
• Cloud Services for semantic data processing
• GPU processing and its abstraction within cloud-based heterogenous data processing
• Semantics for cloud interoperability and management
• Semantics for supporting scientific workflows and business processes
• Semantics for provisioning and managing microservices including port and adapter development

Research Track: Multilinguality

Description:

Semantic technologies promise to improve the management of multilingual content by facilitating its analysis, interlinking, integration and generation. In order to manage data available in multiple languages, a semantic normalization and reconciliation of content across languages is needed. Ontologies and linked lexical resources play an important role here as they provide an interlingual representation that can support the normalization, harmonization and thus the integration of multilingual data. Important challenges lie in the development of algorithms that can handle, index and retrieve multilingual data as well as algorithms to align or merge data and ontologies across multiple languages.  Further challenges lie in the analysis and semantic interpretation of multilingual textual data. Cross-cultural aspects of semantic technologies, including cross-cultural ontology engineering and cross-cultural negotiation and dispute resolution, are of crucial importance in order to pave the way for a European Digital Single Market.
 
Topics include but are not limited to:

• Cross-lingual and multilingual taxonomy and ontology learning and alignment
• Multilingual named entity recognition, entity linking and information extraction
• Cross-lingual and multilingual word sense disambiguation and induction
• Multilingual and cross-lingual information retrieval
• Multilingual question answering over linked data
• Semantic Web and Linked Data for machine translation
• Multilingual and cross-border collaboration
• Multilingual and cross-lingual crowdsourcing
• Multilingual and cross-lingual dispute resolution
• Culture-specific aspects of interoperability and data exchange
• Cross-cultural and cross-lingual ontology engineering
• Best practices for multilingual linked data sets
• Localization of ontologies and linguistic linked data
• Cross-lingual data and resource linking
• Multilingual Natural Language Generation
• Multilingual word- and document-level similarity
• Multilingual embeddings and other neural approaches to multilinguality
• Multilingual data integration, harmonization and aggregation
• Semantic technologies supporting the creation of a digital single market

Research Track: Semantics and Transparency

Description:

Transparency is generally seen as a property of government decisions, processes and activities of being based on information, rationales and processes that are open, comprehensive, visible and understandable by citizens. It can be generalised to the relationships between members of the public and any form of organisation, whether they represent authorities (e.g. local councils, police) or commercial (e.g. large corporates, service providers) entities. The objectives of transparency vary from enabling trust to ensuring accountability. 
Semantic web technologies naturally have a key role to play in achieving and supporting transparency. This has been particularly visible as part of open government data initiatives where the data publication mechanisms offered by the Linked Open Data paradigm have enabled the linking of dispersed data that could be then navigated in order to provide a wider and more accurate view of government transparency. Many other aspects of those technologies can then be employed to integrate diverse pieces of information, interpret them semantically, and generally make them exploitable by members of the public for their own purpose. Semantic web technologies, however, are designed to automate the manipulation of information and knowledge for the benefits of users. As such, it might become difficult for those users to monitor and understand the way information has been processed when coming from multiple, automatically integrated and manipulated sources. In other words, there is a need to also address transparency in semantic web technologies and approaches. This special track of the ESWC 2017 conference aims to present the latest work on the use and fundamental development of semantic web technologies in relation to transparency. As such, we invite submissions of research papers on a broad range of topics, including but not limited to:

• Linked Open Government Data
• Provenance
• Data policies and data governance
• Data traceability 
• Data assessment 
• Fact checking
• Citizen data interpretation and visualisation
• Technology-mediated citizen engagement
• Trust and accountability 
• Data journalism
• Sousveillance, undersight and reverse surveillance
• Ethical and legal hacking for transparency
• eParticipation 

Papers should make an explicit connection between one or more of those topics and the uses of, requirements for, or challenges associated with semantic web technologies. 

 

In-use and Industrial track 

Description:

The Semantic Web In-Use and Industry track provides a forum for researchers and industry to discuss novel  research taken to the market, or on any other relevant uptake of semantic technologies outside the lab. Submissions to the 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. In addition, papers will be assessed on the novelty of the techniques applied in practice. Submitted papers in this track should evaluate or reflect on the pros and cons of the approach when compared to existing solutions. The track takes a broad view of semantic technologies including natural language processing and machine learning techniques.

Topics:

• Best practices and lessons learnt from the use of semantic technologies in real world, industrial settings
• Industry and Business Trends related to the use of the Web of Data
• Semantic Big Data 
• Ontology-based data access in large-scale and industrial systems
• 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
• Semantic technologies and corporate data governance 
• Collaborative Content Management Systems, including Wikis
• Sensor Networks, Smart Cities and Open Government 
• e-Health and Life Sciences
• Sentiment Analysis and Social Networks in action
• Natural language processing and machine reading techniques applied in practice
• 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 other open markup languages