ESWC 2011 - European Semantic Web Symposium (ESWS)

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 submission December 13th, 2010 expired
Notification of acceptance/rejection February 21st, 2011
Camera-ready papers March 7th, 2011
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

Social Web and Web Science
http://www.easychair.org/conferences/?conf=eswc201...
Ontologies
http://www.easychair.org/conferences/?conf=eswc201...
Reasoning
http://www.easychair.org/conferences/?conf=eswc201...
Semantic Data Management
http://www.easychair.org/conferences/?conf=eswc201...
Linked Open Data
http://www.easychair.org/conferences/?conf=eswc201...
Software, Services, Processes and Cloud Computing
http://www.easychair.org/conferences/?conf=eswc201...
Natural Language Processing
http://www.easychair.org/conferences/?conf=eswc201...
Sensor Web
http://www.easychair.org/conferences/?conf=eswc201...
Mobile Web
http://www.easychair.org/conferences/?conf=eswc201...
In-use track

Semantic Web In-Use
http://www.easychair.org/conferences/?conf=eswc201...
Special tracks 2011

Inductive and probabilistic approaches
http://www.easychair.org/conferences/?conf=eswc201...
Digital Libraries
http://www.easychair.org/conferences/?conf=eswc201...
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

Semantic Web In-Use (Olmedilla Daniel, Telefonica I+D, Spain-ES; Shvaiko Pavel, TasLab - Informatica Trentina S.p.A., Italy-IT)
Research Tracks

Social Web and Web Science (Vrandecic Denny, KIT, Germany-DE; Passant Alexandre, DERI, Ireland-IE)
Ontologies (D´Aquin Mathieu, Open University, United Kingdom-UK; Stuckenschmidt Heiner, University of Mannheim, Germany-DE)
Reasoning (Hitzler Pascal, Kno.e.sis Center, Wright State University, Dayton, Ohio, United States-US; Della Valle Emanuele, Politecnico di Milano, Italy-IT)
Semantic Data Management (Polleres Axel, DERI, Ireland-IE; Christophides Vassilis, FORTH-ICS and University of Crete, Greece-GR)
Linked Open Data (Consens Mariano, University of Toronto, Canada-CA; Groth Paul, Free University of Amsterdam, Netherlands-NL; Lehmann Jens, University of Leipzig, Germany-DE)
Software, Services, Processes and Cloud Computing (Norton Barry, KIT, Germany-DE; Stollberg Michael, SAP Research, Germany-DE)
Natural Language Processing (Cimiano Philipp, University of Bielefeld, Germany-DE; Witbrock Michael, Cycorp, Slovenia-SI)
Sensor Web (Alani Harith, KMI, Open University; Mottola Luca, Swedish Insitute of Computer Science, Sweden-SE)
Mobile Web (Lassila Ora, Nokia, Finland-FI; Toninelli Alessandra, INRIA, France-FR)
Special Tracks

Inductive and Probabilistic Approaches (Ghani Rayid, Accenture, United States-US; Lawrynowicz Agnieszka, Poznan University, Poland-PL)
Digital Libraries (Meghini Carlo, CNR-ISTI, Pisa, Italy-IT; Doerr Martin, FORTH ICS, Greece-GR; Renear Allen, University of Illinois at Urbana-Champaign, United States-US)

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.
top ↑

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
top ↑

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
top ↑

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
top ↑

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.
top ↑

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.
top ↑

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
top ↑

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
top ↑

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
top ↑

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
top ↑

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)
top ↑

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.