Call for Papers

General chair: Fabien Gandon (Inria, France)

Program chairs: 

* Marta Sabou (Vienna University of Technology, Austria)

* Harald Sack (Hasso-Plattner-Institut, Universität Potsdam, Germany)

ESWC is a major venue for discussing the latest scientific results and technology innovations related to the Semantic Web. The 12th edition of ESWC will take place from May 31st to June 4th 2015 in Portoroz, Slovenia. Besides a main focus on advances in Semantic Web research and technologies, ESWC 2015 is seeking to broaden its attention to span other relevant research areas. In 2015, ESWC will complement its Semantic Web topics with new tracks that focus on linking machine and human computation at Web scale.  

The goal of the Semantic Web is to create a Web of knowledge and services in which the semantics of content is made explicit and content is linked to both other content and services. This arrangement of knowledge-based functionalities is weaving together a large network of human knowledge, and making this knowledge machine-processable to support intelligent behaviour by machines. Additionally, it supports novel applications allowing content from heterogeneous sources to be combined in unforeseen ways and support enhanced matching between users’ needs, software functionalities and online content.

Human-centered aspects play an important role in Semantic Web research. Human cognition, for example, has inspired several Semantic Web techniques and many Semantic Web tools aim to support cognitive processes. Moreover, although much can be achieved with intelligent algorithms, humans still play a key role in the entire lifecycle of the Semantic Web. To support research activities, humans produce training data, they test the output of Semantic Web algorithms and evaluate the usability of the created tools. During the deployment of Semantic Web technologies knowledge engineers and domain experts build ontologies and vocabularies while annotators create semantic annotations and links between datasets, etc. Human Computation and Crowdsourcing methods provide convenient approaches for gathering research data, testing, as well as supporting Semantic Web deployment in practice. As such, they are increasingly used not only to ease the human contributor bottleneck in research and deployment, but also to design a new kind of applications where human capabilities are part of computational process.

Creating an interlinked Web of knowledge which bridges between heterogeneous content and services requires collaboration between several computer science domains. Also, the hybrid space that the Web has become, where humans and software interact in a complex manner, fundamentally requires an interdisciplinary approach to find novel solutions to the problems generated. 

ESWC 2015 will feature eleven thematic research tracks (see below) and an in-use and industrial track. Submissions of interdisciplinary research papers, covering more than one thematic track, are also encouraged. In addition, the in-use and industrial track will provide an opportunity for dialogue and discussion on industrial applications, tools, deployment experiences, case studies and usage analysis. We therefore encourage submissions addressing several conference research topics. However, each paper should be associated with at least one of the topics of the conference. The main research topics this year are:

* Vocabularies, Schemas, Ontologies;
* Reasoning;
* Linked Open Data;
* Social Web and Web Science;
* Data Management, Big data, Scalability;
* Natural Language Processing and Information Retrieval;
* Machine Learning;
* Mobile Web, Sensors and Semantic Streams;
* Services, APIs, Processes, and Cloud Computing.

In line with this year’s special theme on Human-centered Aspects of the Semantic Web, we particularly encourage submissions to two special tracks:

* Cognition and Semantic Web;
* Human Computation and Crowdsourcing.

Important Dates

Compulsory abstract submission for all papers: 17th of January, 2015 (sharp) - 23:59 Hawaii Time

Compulsory full paper submission: 19th of January, 2015 (sharp) - 23:59 Hawaii Time

Authors rebuttal: 18th-24th February, 2015 - 23:59 Hawaii Time

Acceptance notification: 9th of March, 2015 - 23:59 Hawaii Time

Camera ready: 18th of March, 2015 - 23:59 Hawaii Time

Submission Information

ESWC 2015 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 two days later. The abstract submission is compulsory for every full paper submitted. Abstracts alone will not be reviewed and only fully submitted papers will be considered. Authors of accepted papers will be required to provide semantic annotations for the abstract of their submission - details of this process will be given on the conference Web page at the time of acceptance. At least one author of each accepted paper must register for the conference. More information about the Springer's Lecture Notes in Computer Science (LNCS) are available on the Springer LNCS Web site.

Submissions and reviewing will be supported by the EasyChair system:

ESWC 2015 Tracks

Vocabularies, Schemas, Ontologies

Silvio Peroni - University of Bologna, Italy; National Research Council, Italy
Pavel Shvaiko - Informatica Trentina SpA, Italy


Pascal Hitzler - Wright State University, USA
Stefan Schlobach - Vrije Universiteit Amsterdam, Netherlands

Linked Data

Sören Auer - University of Bonn, Germany​
Stefan Dietze - L3S Research Center, Germany

Semantic Web and Web Science

Miriam Fernandez - Knowledge Media Institute, UK
Markus Strohmaier - GESIS & University of Koblenz, Germany

Semantic Data Management, Big data, Scalability

Olivier Curé - Université Pierre et Marie Curie, France
Axel Polleres - Vienna University of Economics & Business, Vienna, Austria

Natural Language Processing and Information Retrieval

Kalina Bontcheva - University of Sheffield, UK
Simone Paolo Ponzetto - University of Mannheim, Germany

Machine Learning

Bettina Berendt - KU Leuven, Belgium
Heiko Paulheim - University of Mannheim, Germany

Mobile Web, Internet of Things and Semantic Streams

Alasdair Gray - Heriot-Watt University, Scotland, UK
Josiane Xavier Parreira - Siemens AG, Austria

Services, Web APIs, and the Web of Things

Terry Payne - University of Liverpool, UK
Carlos Pedrinaci - Knowledge Media Institute, The Open University, UK

In-Use & Industrial Track

Vanessa Lopez - IBM Research, Ireland
Giovanni Tumarello - Sindicetech/Fondazione Bruno Kessler, Italy

Cognition and Semantic Web

Aba-Sah Dadzie - The HCI Centre, The University of Birmingham, UK
Andreas Nürnberger - Otto von Guericke University Magdeburg, Germany

Human Computation and Crowdsourcing

Lora Aroyo - VU University Amsterdam, Netherlands
Gianluca Demartini - Information School, University of Sheffield, UK

Research Track: Vocabularies, Schemas, Ontologies

Ontologies and related artifacts, such as schemas and vocabularies, play a central role in the Semantic Web by enabling the design of robust applications needing intelligence, e.g., through querying and reasoning over large-scale linked data and documents. The Semantic Web has also established empirical perspectives on ontologies and their development, e.g., ontologies can be learned from text, extracted from legacy representations, the Web, as well as can be studied experimentally with experts and end-users, and against realistic tasks, making design patterns for ontologies emerge from good practices. Finally, the management and dynamics of ontologies are addressed by matching, versioning, evolution, modularization, and other ontology engineering tasks.

This track aims at addressing innovative top-down and/or bottom-up research on ontologies, vocabularies, and schemas for the Semantic Web, Linked Data, and semantic technologies in general. Both theoretical and practical research papers are welcome. Topics of interest include, but are not limited to, the following:

  • Languages, tools, programming paradigms and methodologies for (collaborative) ontology engineering
  • Ontology matching, alignment, and merging
  • Evolution of vocabularies, schemas, and ontologies on the Web
  • Ontology repositories and ontology search
  • Knowledge patterns
  • Ontology- and schema-based data integration and curation
  • Knowledge acquisition (extraction, learning)
  • Ontology management, maintenance, and reuse
  • Ontology and schema quality and evaluation
  • Ontology-driven applications
  • Ontologies and vocabularies for specific domains (e.g., publishing, legal domain, bioinformatics)
  • Ontology-/schema-/vocabulary-based information retrieval
  • Semantic Web (e.g., schema-centric) programming

Research Track: Reasoning

The Reasoning track invites submissions on all topics related to reasoning with ontologies and rules, to reasoning for the Web, to reasoning using Semantic Web technologies, reasoning and learning, and reasoning on highly dynamic and noisy data streams. Contributions can range from theoretical advances to usage-driven developments. Papers with a strong relation to other tracks, but a clear focus on reasoning are also welcome. The range of topics of interest includes, but is not limited to, the following:

  • RDF- and OWL-based reasoning
  • Declarative rule-based reasoning techniques, rule languages, standards, and rule systems
  • Mixing logical and statistical reasoning
  • Approximate reasoning techniques and anytime reasoning
  • Combining learning and reasoning
  • Non-deductive approaches to reasoning, non-standard reasoning over ontologies
  • Scalable reasoning, reasoning with large, expressive or distributed ontologies
  • Distributed and parallel reasoning
  • Stream reasoning
  • Ontology-based data access
  • Reasoning with context dependent knowledge and commonsense reasoning
  • Reasoning with inconsistency, reasoning under uncertainty, reasoning about vagueness
  • Reasoning about user preferences
  • Reasoning on the Web of data
  • Reasoning for knowledge engineering, for data integration, for data and knowledge extraction from the Web
  • Applications of reasoning
  • Implementation and evaluation of reasoners

Research Track: Linked Data

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

  • Extraction, linking and publication of Linked Data
  • Linked Data integration/fusion/consolidation
  • Creation and management of LD vocabularies
  • Searching, querying, and reasoning in distributed LD
  • Dataset profiling and description
  • Data quality and data trustworthiness
  • Dynamics and evolution of LD
  • Analyzing, mining, and visualizing LD
  • Usage of LD and social interactions with LD
  • Scalability issues of Linked Data
  • Provenance, privacy, and rights management; relationship between LD and linked closed data
  • Storage, publication, and validation of data, links, and embedded Microdata
  • Database, IR, NLP and AI technologies for LD
  • Linked Data applications in various domains including Life Science, eGovernment, Manufacturing, Cultural Heritage or Education?

Research Track: Semantic Web and Web Science

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

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

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

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

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

Research Track: Semantic Data Management, Big data, Scalability

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

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

Topics of interest include, but are not limited to:

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

Research Track: Natural Language Processing and Information Retrieval

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

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

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

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

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

Research Track: Machine Learning

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

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

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

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

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

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

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

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

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

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

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

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

In-Use & Industrial Track

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

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

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

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


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

Research Track: Cognition and Semantic Web

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

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

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


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

Research Track:  Human Computation and Crowdsourcing

Human Computation & Crowdsourcing (HC&C) have recently become popular approaches for data processing in tasks machines typically are not effective in. Human computation, crowdsourcing and collective intelligence are demonstrating unique combination of both human-centered studies and traditional computer science. In the context of the Semantic Web, HC&C are considered key techniques to guarantee data quality while maintaining the ability to process data at Web scale. A number of research questions remain open, such as dealing with the diverse quality of crowd answers, optimally combining machine and human-based processing of Linked Data, identifying for which Semantic Web tasks HC&C approaches are most suitable. 

The ESWC 2015 special track on Human Computation and Crowdsourcing (HC&C) is aimed at providing a forum for the community to explore the benefits and challenges of applying semantic technologies in the broad spectrum of HC&C applications. We invite submissions presenting latest scientific results, technology innovations, and exploring challenges and opportunities at the intersection of Semantic Web and Human Computation, Collective Intelligence, Crowdsourcing, as well as contributions from other fields that focus on human(crowd)-centred approaches for collecting, managing and analysis of data semantics, and address the (individual & collective) crowd aspects in their explorations, design, and implementation. 

The core question-pair for this track is: How can Semantic Web technologies contribute to the design and development of innovative, efficient and effective human computation, crowdsourcing and human collective intelligence approaches? And vice versa, how can human computation, crowdsourcing and human collective intelligence techniques help in advancing the Semantic Web field?


  • HC&C approaches for Semantic Web tasks, such as: 
    • Ontology engineering, ontology alignment
    • Search result curation, relevance feedback
    • Linked (Open) Data curation
    • Linked (Open) Data integration
    • Integrating human & social semantics with existing legacy systems
  • Applying Semantic Web technologies in HC&C tasks:
    • Semantic autocompletion and autosuggestion
    • Semantic profiling of crowd workers
    • (Social) semantics in collective intelligence systems
    • Semantics for worker analytics
  • Data Visualization in human computation, crowdsourcing, and collective intelligence (both using SemWeb technologies or applied for SemWeb tasks):
    • Micro-task templates visualization
    • Visualizing distributed collective intelligence systems
    • Visualization for micro-task result aggregation
    • Visualization for worker performance analytics
  • Quality assurance in HC&C (both using SemWeb technologies or applied for SemWeb tasks):
    • Using gold questions
    • Spam filtering
    • Experts vs. crowds
    • Machines vs. crowds
    • Trust and reputation models
    • Crowd worker analytics
  • Optimization aspects for HC&C (both using SemWeb technologies or applied for SemWeb tasks):
    • Semantic Web specific crowdsourcing tools and infrastructures
    • HC&C efficiency and scalability by means of graph-based systems
    • Crowd incentives (individual and group) 
    • Hybrid human-machine systems
    • Social Network Analysis for optimal crowdsourcing performance
    • HC&C workflow definition and management 
  • Applications, Use cases, and Experiences with human computing, crowdsourcing and collective intelligence, such as:
    • Best practices and methodologies for applying HC&C on Semantic Web problems
    • Semantic Web systems leveraging programmatic access to human intelligence
    • Social (semantic) search and Social sensing 
    • Niche-sourcing applications in different domains
    • Expert-sourcing