LDOW-LDDL 2019: Linked Data on the Web and its Relationship with Distributed Ledgers Co-located with the Web Conference San Francisco, CA, United States, May 13-14, 2019 |
Conference website | http://events.linkeddata.org/ldow-lddl/ |
Submission link | https://easychair.org/conferences/?conf=ldow-lddl-2019 |
Submission deadline | February 3, 2019 |
The Web is developing from a medium for publishing textual documents into a medium for sharing structured data. This trend is fueled on the one hand by the adoption of the Linked Data principles by a growing number of data providers. On the other hand, large numbers of websites have started to semantically mark up the content of their HTML pages and thus also contribute to the wealth of structured data available on the Web. Recently, Distributed Ledger Technologies (DLTs) have emerged as a novel way to manage and exchange digital assets among a large number of agents in a decentralised way, leading to a rethink of consensus algorithms. Distributed Ledgers may be one answer to the problems of trust and redecentralisation of the Web, for instance in the context of Linked Data. Conversely, Linked Data and Web technologies could help Distributed Ledger technologies for solving their very own challenges, like interoperability and querying.
The workshop on Linked Data on the Web and its Relationship to Distributed Ledgers (LDOW/LDDL) aims to stimulate discussion and further research into the challenges of publishing, consuming, and integrating structured data from the Web, covering established topics of the Linked Data on the Web (LDOW) community. As this year’s edition represents the coming together of the established Workshop on Linked Data On the Web (LDOW) with Workshop on Linked Data and Distributed Ledgers we'll additionally address the question of how distributed ledgers could help towards solving some of these challenges, and how Linked Data technologies may help distributed ledgers to become more open and interoperable.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- Full scientific articles: up to 10 ‘pages’
- Short scientific and position articles: up to 5 ‘pages’
Important dates:
- Contributions deadline: 2019-01-28 (23:59 Hawaii Time)
- Notification of acceptance: 2019-02-14
- Camera-ready versions of accepted contributions: 2019-03-03
- Workshop date: 2019-05-13 or 14
List of Topics
- Integrating Web Data from Large Numbers of Data Sources
- linking algorithms and heuristics, identity resolution
- schema matching and clustering
- evaluation of linking and schema matching methods
- Linked Data Applications
- application showcases including Web data browsers and search engines
- marketplaces, aggregators and indexes for Web Data
- security, access control, and licensing issues of Linked Data
- role of Linked Data within enterprise applications (e.g. ERP, SCM, CRM)
- Linked Data applications for life-sciences, digital humanities, social sciences etc.
- Linked Data for Distributed Ledgers
- Architectures and protocols for interoperability between DLs and other Web components and architectures (non-ledger based web services, web databases, etc)
- Extensions of web data models and formats to accommodate Distributed Ledgers (JSON, HTTP, HTML, RDF, etc)
- Vocabularies and ontologies for describing DLs and Smart Contracts
- Semantification and linking of Distributed Ledgers and their contents
- Storage, querying and updating RDF data inside Distributed Ledgers
- Using Linked Data in Smart Contract languages
- Distributed Ledgers for Linked Data
- Decentralisation and disintermediation of web-based architectures with DLs
- Distributed management of identity and online identity.
- Distributed Ledger backing of general Linked Data processes: vocabulary and dataset evolution, entity naming and re-naming, etc.
- DLs for trust in the Web
- Web Data Quality Assessment and cleansing
- methods for evaluating the quality and trustworthiness of web data
- methods for cleansing web data
- tracking the provenance of web data
- data fusion and truth discovery
- profiling and change tracking of web data sources
- cost and benefits of web data quality assessment and cleansing
- web data quality assessment and cleansing benchmarks
- Mining the Web of Data
- large-scale derivation of implicit knowledge from the Web of Data
- using the Web of Data as background knowledge in data mining
- techniques and methodologies for Linked Data mining and analytics
Committees
Organizing committee (In Alphabetic order)
- Maribel Acosta, Karlsruhe Institute of Technology, Germany
- Tim Berners-Lee, W3C/MIT, USA
- Stefan Dietze, Leibniz Universität Hannover, Germany
- Anastasia Dimou, IDLab - University of Ghent, Belgium
- John Domingue, KMI - Open University, United Kingdom
- Luis-Daniel Ibáñez, ECS - University of Southampton, United Kingdom
- Krzysztof Janowicz, University of California, Santa Barbara, US
- Maria-Esther Vidal, TIB Hannover, Germany
- Amrapali Zaveri, University of Maastricht, Netherlands