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Call for Papers

The mission of the Extended Semantic Web Conference is to bring together researchers and practitioners dealing with different aspects of semantic technologies. Following a successful re-launch in 2010 as a multi-track conference, ESWC 2011 builds on the success of the former European Semantic Web Conference series, and seeks to extend its focus by collaborating with other communities and research areas, in which Web semantics play an important role, within and outside ICT, and in a truly international, not just ‘European’ context.

The semantics of content, enriched with domain ontologies, data about systems’ usage, natural language processing, and many other aspects, will enable a qualitatively new level of functionality on the Internet, or in any other application environment relying thereupon. It will weave together a large network of human knowledge, and make this knowledge machine-processable. A variety of automated services, based on reasoning over metadata and ontologies, will help the users to achieve their goals by accessing and processing information in machine-understandable form. This network of knowledge-based functionality will ultimately lead to truly intelligent behavior, which will be employed for a variety of complex decision-making tasks. Research on semantic technologies can benefit from ideas and cross-fertilization with many other areas, including Artificial Intelligence, Natural Language Processing, Database and Information Systems, Information Retrieval, Multimedia, Distributed Systems, Social Networks, Web Engineering, and Web Science. These complementarities are reflected in the outline of the technical program of the ESWC 2011; in addition to the research and in-use tracks, we have furthermore introduced two special tracks this year, putting particular emphasis on inter-disciplinary research topics and areas that show the potential of exciting synergies for the future. In 2011, these special tracks focus on data-driven, inductive and probabilistic approaches to managing content, and on Digital Libraries, respectively.

Important Dates

EXTENDED Abstract submission:December 13th, 2010 expired
Full-paper submissionDecember 13th, 2010 expired
Notification of acceptance/rejectionFebruary 21st, 2011 expired
Camera-ready papersMarch 7th, 2011 expired

Additional Information

ESWC2011 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. The proceedings of this conference will be published in Springer's Lecture Notes in Computer Science series. Paper submission and reviewing for ESWC2011 will be electronic via the conference submissions site. Each paper must be submitted to the most appropriate of the twelve research tracks.  The program committee might decide to forward a paper to another track if it better fits the topics there.

Research tracks

In-use track

Special tracks 2011

Papers should not exceed fifteen (15) pages in length and must be formatted according to the information for LNCS authors. Papers must be submitted in PDF (Adobe's Portable Document Format) format and will not be accepted in any other format. Papers that exceed 15 pages or do not follow the LNCS guidelines risk being rejected automatically without a review. Authors of accepted papers will be required to provide semantic annotations for the abstract of their submission - details of this process will be provided 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.

Call for Papers ESWC 2011 Tracks

In-Use Tracks

Research Tracks

Special Tracks

 

In-Use Track Semantic Web In-Use

Bringing the research results down to exploitation by the final users as well as demonstrating the beneficial use of these results in real world settings is a major challenge. Semantic technologies are among transversal enabling technologies, and, hence, can be applied in various domains, ranging from eGovernment to manufacturing. The Semantic Web In-Use track is particularly devoted to showcase implemented applications, learned best practices as well as assessments and evaluations of semantic technologies in real world settings. Submissions to this track should substantially contribute to the knowledge transfer from research labs into mainstream adoption. Special interest for this year's ESWC in-use track includes linking open (e.g., eGovernment) data, sentiment analysis (e.g., over social networks and blogs) and scalable show cases (e.g., scenarios with large volumes of data and/or near real-time response requirements). In this track we invite original submissions conforming to generally accepted practices for scientific papers covering, but not limited to, one or more of the following topics

  • Description of the concrete problems in specific application domains, for which the semantic technologies can provide a solution.
  • Description of an implemented application of the semantic technologies in a specific domain.
  • Assessment of the pros and cons of using the semantic technologies to solve a particular business problem or other practical problems in a specific domain.
  • Comparison with alternative or competing approaches using conventional or competing technologies.
  • Assessment of the costs and benefits of the application of the semantic technologies, e.g., time spent on implementation and deployment, efforts involved, final user acceptance, returns on investment.
  • Evidence of deployment of the application, and assessment/evaluation of usage/uptake.
  • Application of the semantic technologies to problems where their scalability to large amounts of data and/or short response times are demonstrated.
  • Domains of interest include, but are not limited to
    • Enterprise applications
    • eGovernment
    • eParticipation
    • eEnvironment
    • eMobility and smart cities
    • eHealth
    • eInclusion
    • Life Sciences
    • Media and entertainment
    • Telecommunications
    • Cultural heritage
    • Financial services
    • Energy and utilities
    • Manufacturing.

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Research Track Social Web and Web Science

The success of Social Web applications (often referred to as ‘Web 2.0’ applications) is manifested through the fast growth of social networks and sites with user-generated content, like Facebook, Twitter, YouTube, Wikipedia, Flickr, and many more. Many Social Web applications have simplified the data publishing process using user-friendly and interactive tools and practices (such as Wikis, tagging, and microblogging) and have decreased the cost and increased the incentive to contribute data. In addition, some trends such as ubiquitous computing lead to new ways and means to share content in real-time within social communities.

The combination of Social Web principles and Semantic Web technologies allows end-users to massively produce and use semantic data through social applications, which in turn enables smarter Web-based applications in various domains. This includes the Social Web itself, where it becomes possible to mine Semantic Web data and discover relationships that were not obvious, whether it is in social network identification or for information retrieval purposes. These can be exploited for various purposes: to personalize applications, recommend content, generate new knowledge, and more. But besides the technical aspect, there is also a need to understand the behaviors and patterns of users on the Web, and in particular on the Social Web. Web Science aims to address these issues, also considering other aspects that are important to realize a Social Semantic Web, such as governance, law, policies and decision-making, etc.

This track on Social Web and Web Science aims at bringing together researchers from these communities to address various challenges from improving Social Web user experiences with Semantic Web technologies in order to build novel semantic applications using Social Web data, as well as understanding the various patterns of the Web. Successful submissions will address at least some aspect of both areas. Topics of interest include, but are not limited to:

  • Collaborative and collective semantic data generation and publishing
  • Social and semantic bookmarking, tagging and annotation
  • Enriching the Social Web with semantic data: RDFa, micro formats and other approaches
  • Linked data on the Social Web
  • Semantically-enabled social platforms and applications
    • Semantic wikis
    • Semantic desktops
    • Semantic portals
    • Semantic blogs
    • Semantic calendars
    • Semantic email
    • Semantic news, etc.
  • Querying, mining and analysis of social semantic data
  • User profile construction based on tagging and annotations
  • Reasoning and personalization based on semantics
    • Recommendations
    • Social navigation
    • Social search, etc.
  • Privacy, policy and access control on Social Semantic Web
  • Provenance, reputation and trust on Social Semantic Web
  • Formation, management and understanding of semantically interlinked online communities
  • Citizen sensing and ubiquitous Social Semantics
  • Social Semantic Web and Internet of Things

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Research Track Ontologies

Ontologies, and related formal representations of conceptual knowledge, are at the heart of the Semantic Web, with research on ontology languages, the construction of ontologies and ontology-based applications representing a core part of the research since the early days of the Semantic Web. This track is intended to new developments, and especially innovative techniques for building and maintaining ontologies, as well as novel applications exploiting ontologies in challenging settings, including the open Web. Papers describing and/or evaluating models, methods and systems for supporting all tasks around the lifecycle of ontologies are welcome, including ones with a strong relation to other tracks but a clear focus on ontologies. The PC might decide, to forward a paper to another track if it better fits the topics there. Topics of interest include, but are not limited to

  • Creation of knowledge models
  • Languages, tools, and methodologies for building ontologies
  • Collaborative ontology building
  • Knowledge acquisition from various sources
  • Knowledge patterns
  • Ontology learning
  • New formalisms
  • Ontology management and maintenance
  • Ontology reuse
  • Ontology selection
  • Ontology matching, alignment and merging
  • Ontology versioning and change management
  • Ontology evolution
  • Collaborative management of ontologies
  • Ontology quality and evaluation
  • Ontology repositories and ontology search
  • Ontology-based applications
  • Ontologies for large-scale applications
  • Ontologies for eGovernment, life science, multimedia, software, engineering, eBusiness, eCommerce, mobile applications, social applications and many others
  • Ontologies for science and innovation
  • Ontology-based information retrieval
  • Ontology-based data integration
  • Ontologies for privacy
  • Human-ontology interaction

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Research Track Reasoning

The Reasoning track invites submissions on all topics related to reasoning with ontologies and rules, to reasoning for the World Wide Web, to reasoning using Semantic Web technologies, and reasoning on highly dynamic data streams. Contributions can range from theoretical advances to usage-driven developments. Particularly encouraged are future-oriented contributions concerning topics such as stream reasoning, reasoning on the Web of Data, and the application-driven development of reasoning methods. We also welcome paper with a strong relation to other tracks, but a clear focus on reasoning. The range of topics of interest includes, but is not limited to, the following

  • Approximate reasoning techniques
  • Scalable reasoning
  • Reasoning with inconsistency
  • Reasoning under uncertainty
  • Reasoning with large, expressive or distributed ontologies
  • Commonsense Reasoning
  • Non-deductive approaches to reasoning
  • Reasoning on the Web of Data
  • Declarative rule-based reasoning techniques
  • Rule languages, standards, and rule systems
  • RDF- and OWL-based reasoning
  • Distributed and parallel reasoning
  • Implementation and evaluation of reasoners
  • Applications of reasoning
  • Stream reasoning (as focus topic), including
    • Model theory
    • Inference problems and their formal properties
    • Definitions for soundness and completeness
    • Processing highly dynamic relational data streams at semantic level
    • Techniques for continuous query answering
    • Inductive reasoning
    • Stream reasoning algorithms and incremental reasoning techniques
    • Exploiting the parallel nature of streams by splitting/synchronization/pipelining
    • Cognitively-inspired approaches to deal with large and dynamic information
    • Applications of reasoning on large and noisy streams

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Research Track Semantic Data Management

During last years we have witnessed a tremendous increase in the amount of semantic data that is available on the Web in almost every field of human activity. Billions of RDF triples from Wikipedia, U.S. Census, CIA World Factbook, open government sites in the US and the UK, news and entertainment sources, as well as various ontologies (especially in eScience) have been created and published online. For the successful discovery, sharing, distribution and organization of this emerging information universe, the ability to understand and manage the semantics of the data is of paramount importance. Semantic data management refers to a range of techniques that can be employed for storing, querying, manipulating and integrating data based on its meaning. It essentially enables sustainable solutions for a range of IT environments, where the usage of today's mainstream semantic technology is either inefficient or entirely unfeasible, namely, enterprise data integration, life science research, and collaborative data sharing in SaaS architectures. In a nutshell, semantic data management aims to support a more comprehensive usage of larger scale and more complex semantic datasets at lower cost. To achieve this vision, interdisciplinary synergies are required among researchers in the Semantic Web, data management systems as well as information retrieval communities. To this end, this track will be organized along the following key themes

  • Semantic repositories and databases
    • Storage schemas optimized for RDF data
    • Reasoning supported by data management infrastructures
    • Indexing structures for schema-less or schema-relaxed semantic data, storage
    • Density and performance improvements
    • Efficient query processing
    • Embedded semantic data processing (stored procedures and storage engine extension APIs)
  • Semantic access to legacy data
    • Efficient publishing from and to other data formats (e.g. XML, relational data) from RDF and ontologies
    • Semantic query optimization techniques;
  • Virtualized semantic stores and scalability
    • Identification and composition of (fragments of) data sets by abstracting the applications from the specific set-up of the data management service (e.g., local vs. remote and distribution)
    • Semantic data partitioning
    • Replication
    • Federation on the cloud
  • Exploratory semantic searching and browsing
    • Dataspaces for the Semantic Web
    • Semantic data analytics
    • Data dynamics
    • Emergent data semantics
    • Data- and query- specific strategies for dynamic data materialization
    • Adaptive, multi-query optimization
    • Multi-modal retrieval (quantitative and statistical) and ranking algorithms (FTS, co-occurrence, concordance, temporal, spatial);
  • Security and privacy
    • Access control specification languages and enforcement strategies
    • Consistency checking of access control policies
    • Incremental maintenance of security annotations
    • Privacy aware access control models
  • Traceability and trustworthiness
    • Probabilistic RDF data and query answering
    • Provenance models for SPARQL queries and RDFS/OWL programs
    • Provenance models of dataflows and mash-ups
    • Automated reasoning over abstract provenance information
    • Efficient storage and querying of provenance data
  • Benchmarking
    • Foundations, methods and tools for semantic systems benchmarking
    • Performance evaluation of existing semantic query, update and reasoning services
    • Analysis of synthetic and real large-scale semantic data repositories.

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Research Track Linked Open Data

The Linked Open Data (LOD) movement has gained remarkable momentum over the past years. Hundreds of datasets (including governmental, reference, geographic, media, scientific, and social data) have been published, providing tens of billions of RDF triples interlinked by hundreds of millions of RDF links. LOD, as well as complementary open data initiatives, are becoming significant contributors to the information landscape of the Web. The recent advances in both the publication and the consumption of Web open data have increased the potential impact of research contributions in this area. In this track, we look for research contributions in the area of Web open data and the LOD initiative. Topics include, but are not limited to, the following

  • Linked Open Data publication
    • Entity resolution and interlinking
    • Managing the storage and publication of data, interlinks, and embedded LOD
    • Linked data and metadata integration/fusion/consolidation
    • Dataset curation
  • Linked Open Data consumption
    • Linked data applications (e.g., open government data consumption)
    • Searching, querying, analyzing, and mining linked data; reasoning with LOD
    • Dataset description and discovery
    • User interfaces and user/social interactions for LOD
  • Architecture and infrastructure
    • Provenance, privacy, and rights management
    • Assessing data quality and data trustworthiness
    • Dataset dynamics
    • Crawling and caching
    • Scalability in the linked data cloud.

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Research Track Software, Services, Processes and Cloud Computing

The software industry in Europe and beyond is preparing for the Future Internet of Services, which is commonly is considered to become a multi-billion market within the next years. Despite the substantial innovations and research results on service engineering throughout the last decade, several challenges need to be solved in order to make the vision of the Internet of Services (IoS) become a reality. To address this, we are particularly interested in scientific contributions for the profitable employment of semantic technologies for (a) the modeling, handling, and management of business-relevant aspects of services and service-based systems such as SLAs, pricing models, security and trust; (b) novel techniques for automating the complete service provision and consumption life cycle in an efficient and large-scale manner, esp. around light-weight RESTful services in addition to the traditional WS-* stack as well as the integration with Web-of-Data technologies; (c) innovative techniques for easy, light-weight, and efficient service-based application development such as Web 2.0 inspired service mash-up techniques, scalable service composition and the integration with business process and workflow technologies, or service customization and on-device support. We also welcome contributions on other techniques or insights that can help to overcome the burden for potential service providers to publish their offers as services. This track invites high-quality submissions related, but not limited to the following topics:

  • Semantic description models for business-relevant aspects of services and processes
  • Semantics for service governance and quality-of-service
  • Service Science: business needs, challenges, and case studies on the adoption of services and semantically enabled service engineering techniques in industry
  • Profitable use of semantics in the service engineering process
  • 'Mash-up' approaches and/or the combination of data, services, and processes
  • Semantic resource-oriented architectures using services and processes
  • Scalable and efficient automation of the service life cycle (matchmaking, discovery, composition, ranking, selection, federation, data & process mediation, etc.)
  • Extraction of semantic service descriptions from un-/semi-structured sources

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Research Track Natural Language Processing

Natural language is the main means of communication between humans and as a result a huge amount of content on the Web is still textual or at least semi-structured, combining some markup (e.g. in the form of tags) with unstructured content. Natural language processing and text mining are therefore crucial building blocks for the semantic analysis of unstructured and textual data and thus important research areas for the Semantic Web. Natural language can represent an effective and intuitive means for querying and accessing semantic data. In this track we invite research contributions dealing with all aspects of combining natural language and semantics solving traditional as well as novel challenges. We invite papers on the following topics

  • Semantic analysis of textual data
  • Robust natural language processing for the Web
  • Question answering on the Semantic Web/Linked Data
  • Natural language generation for the Semantic Web
  • Ontology learning and knowledge acquisition from text and other unstructured resources
  • Information extraction at Web scale
  • Opinion mining/Sentiment analysis on the Web
  • Approaches to semi-automatic annotation/mark-up/authoring
  • Ontology localisation
  • Multilinguality and the Semantic Web
  • Lexicon-ontology Interface
  • Mining social media
  • Tacking information diffusion and provenance
  • Machine reading
  • Knowledge representation, ontologies and reasoning for NLP

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Research Track Sensor Web

The correct interpretation and analysis of the raw numerical values provided by the ever more pervasive sensor networks requires proper semantics support and contextual knowledge. This enables better data representation, integration, and use, and further aids in coping with the inherently unreliable nature of the observations provided by sensor networks, affected by sensor noise and faults. In this track we invite approaches dealing with combining sensor network and semantic technologies, for the purpose of management, interpretation and analysis of the observed environment. Contributions are expected to cover a wide range of related topics such as (a) identification of simple events or event streams by joining sensor data with background knowledge, (b) identification of complex events composed from several atomic sensed events based on background knowledge, (c) filtering, management, and interpretation of sensor data using contextual models, (d) creation of actuators and applications based on sensor data and background knowledge. We also particularly welcome solutions that address one or more of the above challenges by means of in-network processing techniques. We invite high-quality submissions related to (but not limited to) one or more of the following topics

  • Data models and querying solutions for semantic sensor networks
  • Programming languages and abstractions for sensor network supporting contextual and background models
  • Architectures and middleware for semantic sensor networks
  • In-network data processing and filtering techniques based on contextual and semantic knowledge
  • Ontologies and rules for semantic sensor networks
  • Annotation tools for semantic sensor networks
  • Semantic data integration and fusion of heterogeneous sensor network data streams
  • Spatio-temporal aspects of semantic sensor networks
  • Filtering techniques for sensor network data based on contextual knowledge
  • Mash-up technologies for semantic sensor networks
  • Use cases and applications demonstrating the use of semantic technologies combined with sensor networks
  • Social sensing data architectures and applications
  • Standardization efforts in semantic sensor networks
  • Visualization of semantic sensor data

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Research Track Mobile Web

With more and more mobile devices now sporting several sensors, rich multimedia support, versatile connectivity options, and significant computational resources, the vision of ubiquitous computing is becoming reality. Beyond the initial technical barriers arising from resource constraints of mobile devices, developers today must face new challenges in the design, implementation and deployment of mobile applications: collecting, producing and processing huge amounts of heterogeneous data about users' movements, activities, social interactions, physical conditions and environments. Semantic Web technologies can be successfully exploited to meet this challenge, and to address interoperability, adaptation and personalization, thus enabling a completely new class of mobile applications. This track solicits the submission of original research papers dealing with significant issues and innovative solutions for mobile Semantic Web applications. Submissions are expected to clearly present and evaluate their contribution. We encourage papers that share their data sets with the community for further reuse. Topics of interest include, but are not limited to

  • Management of semantic data in a mobile environment (including reasoning)
  • Cloud computing for semantic data processing
  • Scalability and performance of semantic technologies on mobile devices
  • Interoperability of mobile applications based on semantic technologies
  • Provenance of semantic data in mobile environments
  • Mobile technologies for (and applications of) linked data, including mobile social applications
  • Context- and location-aware mobile applications based on semantic technologies
  • Semantic technologies in smart spaces and ubiquitous computing
  • Semantic-based security, privacy and trust in mobile devices and applications
  • Toolkits, testbeds, development environments for semantic mobile applications
  • Semantic data sets for mobile applications
  • User interfaces for semantic data on mobile devices
  • Semantic middleware to support mobile applications

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Special Track Inductive and Probabilistic Approaches

Approaches dealing with formalized knowledge fall in the spectrum between 'knowledge-driven' and 'data-driven' methods. Data-driven approaches are focused on the creation of new knowledge by extraction and mining it directly from data. They are suitable for scenarios where existing knowledge (in the form of ontologies or domain knowledge for example) is not available and is expensive to create. Data driven approaches operate on instances collected from the observed environment. In this track we invite contributions using methods from research areas such as statistical modeling, machine learning, Data/Text/Web-mining motivated by and/or applied to semantic technologies. We are interested in submissions that describe approaches tested and applied to large real-world data sets. In particular we welcome submissions on

  • Dealing with large amounts of real-world data
  • Methods for combining top-down and bottom-up techniques
  • Extraction and augmentation of ontological knowledge from data using statistical and machine learning methods
    • Ontology learning/mining
    • Ontology mapping and mediation
    • Learning semantic relations
    • Information extraction
  • Use of existing ontological knowledge for improving analytics systems
  • Web mining for the Semantic Web
    • Graph mining
    • Social network analysis
    • Link prediction
    • Statistical relational learning
    • Ranking methods and learning to rank
    • Inductive Logic Programming on the Semantic Web
  • Advances in semantic technologies using analytics approaches
    • Refinement operators for concept and rule languages
    • Probabilities formal representations
    • Probabilistic methods for concept and rule languages
    • Semantic (dis-)similarity measures
    • Kernels for structured representations
  • Applications of inductive and probabilistic methods (such as consumer applications, life sciences, semantic multimedia, search, geo-informatics, recommender systems)

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Special Track Digital Libraries

Digital Libraries are fast becoming significant resources for the world’s knowledge. Even though a lot of their content is already accessible via harvesting to the visible Web, much more could be exploited, and better exploited, through the Semantic Web. Often, Digital Libraries focus on particular disciplinary and subject areas and constitute curated knowledge. Semantic Web technologies for digital libraries will therefore take advantage of much richer assumptions on domain-specific semantics, consistency and quality of content. Digital Library research often focuses on creating ‘core’ finding aids, but content and metadata are a source of semantic relationships that can be exploited for far richer, intelligent information services. Searching information to solve a research question is much more than filling a query-by-example form for the most relevant document. Harvesting protocols currently capture only a small part of the metadata, and the collected item level metadata may miss important facts from the context that holds for the collections as wholes. Content and metadata not only refer to categorical subjects, but much more they refer and corefer to billions of things, people, places and events. The global co-reference management of entities with explicit identity(ies) only in local contexts is a major challenge for the future, which will ultimately turn object collections into integrated knowledge resources. This includes - but goes far beyond - opening Digital Libraries to Linked Data. Particular topics of relevance are

  • Digital Libraries requirements for the Semantic Web and semantic technologies in Digital Libraries
  • Ontologies for metadata integration
  • Adequacy of metadata schemata/ontologies for research questions
  • Core ontologies and community-specific extensions
  • Deductions from complex data paths in semantic metadata relationships, such as detecting co-author clusters
  • Inferences between collection-level and item-level metadata – property inheritance from wholes to parts and vice-versa
  • Effective querying of an ‘Open World’ of incomplete metadata, such as distinguishing ‘positive hits’ versus ‘possible hits’
  • Abstracting rich metadata for retrieval: how to close the recall gap between keyword search and ‘advanced search’
  • Automatic metadata generation
  • Metadata transformation, metadata enhancement
  • Digital provenance models and digital provenance metadata generation
  • Reasoning on digital provenance: property inheritance by derivatives from primary digital creation, garbage collection in derivative sets etc.
  • Authenticity, digital provenance and long-term preservation
  • Exploiting digital provenance for digital rights management
  • Automatic detection of co-reference to people, places, events, things
  • Manual, Web 2.0-style, community-driven co-reference detection
  • Large-scale distributed co-reference management and integration with authority services.

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