posted by user: mgns || 502 views || tracked by 4 users: [display]

COLD 2016 : Seventh International Workshop on Consuming Linked Data


When Oct 17, 2016 - Oct 17, 2016
Where Kobe, Japan
Abstract Registration Due Jun 30, 2016
Submission Deadline Jul 7, 2016
Notification Due Jul 31, 2016
Final Version Due Aug 18, 2016
Categories    linked data   web   dataset   data management

Call For Papers

The quantity of published Linked Data continues to increase. However, applications that consume Linked Data are not yet widespread. Reasons may include a lack of suitable methods for a number of open problems, including the seamless integration of Linked Data from multiple sources, dynamic discovery of available data and data sources, provenance and information quality assessment, application development environments, and appropriate end user interfaces. Addressing these issues requires well-founded research, including the development and investigation of concepts that can be applied in systems which consume Linked Data from the Web. Our main objective is to provide a venue for scientific discourse (including systematic analysis and rigorous evaluation) of concepts, algorithms and approaches for consuming Linked Data.

The workshop will be co-located with the 15th International Semantic Web Conference (ISWC) in Kobe, Japan.


2016-05-24: First Call for Papers published / submission system open.
2016-04-06: The workshop has been accepted for ISWC 2016.
Important Dates

Abstract deadline: June 30th
Full paper deadline: July 7th
Author notification: July 31st
Camera-ready deadline: August 18th
Proceedings published: August 21st

The term Linked Data refers to a set of foundational principles for publishing and interlinking structured data on the Web. After Linked Data was first proposed in 2006, a grass-roots movement, led by the Linking Open Data project, started to publish and to interlink multiple open databases on the Web following the proposed principles. Due to conference workshops, tutorials and general evangelism, an increasing number of data publishers – such as the BBC, Thomson Reuters, The New York Times, the Library of Congress, BestBuy, Getty, the US and UK government – have since adopted this practice. This ongoing effort resulted in bootstrapping the “Web of Linked Data” which, today, comprises of billions of RDF triples and millions of RDF links between datasets. The published datasets now include data about books, movies, music, radio and television programs, reviews, scientific publications, genes, proteins, diseases, medicine and clinical trials, geographic locations, people, statistical and census data, companies, and many more topics besides.

All of these published datasets are openly available on the Web in standardised interoperable formats, which presents novel opportunities for the next generation of Web-based applications: data from different providers can be aggregated, allowing fragmentary information from multiple sources to be integrated so as to achieve a complementary and more complete view. While a few applications, such as the BBC music guide have used Linked Data to significant benefit, the deployment methodology has been to harvest the data of interest from the Web to create a private, disconnected repository for each specific application. Such “harvesting approaches” are typically only feasible for vertical applications tied to specific datasets, incur a high up-front cost, and are insensitive to updates in the original data-sources. New concepts for consuming Linked Data – that do not require up-front harvesting of all sources – are required to lead the Web of Linked Data to its fullest and most general potential. The concepts, patterns, and tools necessary are very different from situations where relevant resource identifiers are known a priori, where queries can be run over complete local repositories, where access to the repository is reliable and cheap, and where relevant data sources are known to be trustworthy.

Open issues include (but are not limited to) a lack of approaches for seamless integration of Linked Data from multiple sources, for dynamic, on-the-fly discovery of available data, for information quality assessment, for querying and caching dynamic remote sources, and for implementing appropriate end-user interfaces.

These open issues can only be addressed appropriately when they are conceived as research problems that require the development and systematic investigation of novel approaches. The 7th International Workshop on Consuming Linked Data (COLD 2016) aims to provide a platform for the presentation and discussion of such approaches. Our main objective is to attract submissions that present scientific discussion (including systematic evaluation and/or formal results) of broadly-applicable concepts and approaches.

Topics of Interest

While previous editions of the workshop have attracted a number of submissions that addressed topics related to (RDF and) Linked Data management in general, with COLD 2016 we aim to continue steering the workshop back towards the aforementioned core goals. To this end, we explicitly seek submissions that address research problems related to at least one of the following two aspects of Linked Data consumption:

Makes use of Linked Data principles, including dereferencing
Involves direct use of multiple, real-world Linked Datasets
In the context of these two aspects of Linked Data consumption, relevant topics for COLD 2016 include but are not limited to:
Live Linked Data (i.e., algorithms and applications that make use of Linked Data at runtime)
Architectures for consuming Linked Data (e.g., Dataspaces, Cloud, NoSQL)
Integration of Linked Data sources (e.g., entity resolution, sameas, vocabulary mapping, etc.)
Handling additional Web data (e.g., Deep Web, APIs, Microdata, JSON, Atom, tables, etc.)
Web-scale data management (e.g., crawling, indexing, parallel processing, etc.)
Novel languages for navigating and consuming Linked Data (e.g., nSPARQL, NautiLOD, etc)
Linked Data summarisation, guides and schema learning
Query processing over multiple Linked Datasets
Search over the Web of Linked Data
Auto-discovery of URIs and data
Caching and replication
Dataset dynamics
Reasoning on Linked Data from multiple sources
Information quality and trustworthiness of Linked Data
User-interface research for interacting with the Web of Linked Data

We seek novel technical research papers in the context of consuming Linked Data with a length of up to 12 pages.

All submissions must be in English. Paper submissions must be formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS).

We accept submissions in PDF but also encourage submissions in HTML. In the latter case, you should submit a ZIP archive containing all of the necessary files. If you are new to HTML submissions, you may find the following useful:

dokieli is a client-side editor for publishing HTML articles, compliant with the Linked Research initiative. There are a variety of examples available online, where the LNCS template and example paper may be particularly useful.
The Research Articles in Simplified HTML (RASH) Framework provides a terse markup language for writing scientific articles in (X)HTML+RDFa.
Please note that independently of the format used, we require articles to be submitted in LNCS format and to abide by the permitted font sizes, font selection, margins, etc., irrespective of the format used. This is to ensure visual consistency of the proceedings as well as to have comparative page limits. Submissions not conforming to the LNCS format or papers that are exceed the page limit will be rejected without review.

Please submit your paper via EasyChair at

We note that the author list does not need to be anonymised, as we do not have a double-blind review process in place.

Submissions will be peer reviewed by three independent reviewers. Accepted papers have to be presented at the workshop to be published in the proceedings. Proceedings will be published online at CEUR-WS.

Related Resources

PODS 2017   36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems
MLDM 2017   Machine Learning and Data Mining in Pattern Recognition
WEBIST 2017   13th International Conference on Web Information Systems and Technologies
ESWC 2017   14th Extended Semantic Web Conference
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
JWS-VOILA 2016   Special Issue on Visualization and Interaction for Ontologies and Linked Data
ALLDATA 2017   The Third International Conference on Big Data, Small Data, Linked Data and Open Data
ICBDA 2017   The 2017 IEEE International Conference on Big Data Analysis (ICBDA 2017) - Ei Compendex
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
IJDKP 2016   International Journal of Data Mining & Knowledge Management Process