MEPDaW'20 - Managing the Evolution and Preservation of the Data Web

There is a vast and rapidly increasing quantity of scientific, corporate, government, and crowd-sourced data openly published on the Web. Open Data plays a catalyst role in the way structured information is exploited on a large scale. A traditional view of digitally preserving these datasets by “pickling and locking them away” for future use, like groceries, conflicts with their evolution. There are several approaches and frameworks (Linked Data Stack, PoolParty Suite, etc.) that manage a full life-cycle of the Data Web. More specifically, these solutions are expected to tackle major issues such as the synchronisation problem (monitoring changes), the curation problem (repairing data imperfections), the appraisal problem (assessing the quality of a dataset), the citation problem (how to cite a particular version of a dataset), the archiving problem (retrieving a specific version of a dataset), and the sustainability problem (preserving at scale, ensuring long-term access).

This workshop targets one of the emerging and fundamental problems in the Web, specifically the management and preservation of evolving knowledge graphs.

During the past five years, the MEPDaW workshop series has been gathering researchers from the community around these challenges. So far the series successfully published more than 25 research efforts allowing more than 50 individual authors to present and share their ideas.

This workshop aims at addressing challenges and issues on managing Knowledge Graph evolution and preservation by providing a forum for researchers and practitioners to discuss, exchange and disseminate their ideas and work, to network and cross-fertilise new ideas.

Topics of interest include, but are not limited to themes related to the evolution and preservation of Knowledge Graphs:

• Management and Governance of Evolution in Knowledge Graphs
• Representation, maintenance of versions & changes (change representation and detection)
• Efficient indexing and update of Knowledge Graphs
• Synchronization of distributed versions
• Federated Knowledge Graph governance
• Reasoning and Prediction over Evolving Knowledge Graphs
• Techniques for extracting and predicting evolving patterns
• Maintenance of explicit and implicit knowledge
• Trend analysis of evolving knowledge graphs
• Concept drift detection and prediction over knowledge graphs
• Visualization and Exploration of Evolving Knowledge Graphs
• Visual summarization of evolving knowledge
• User interfaces for exploring evolving knowledge graphs
• Visualisation of quality in knowledge graphs
• Preservation of Evolving Knowledge Graphs
• Preservation of context, provenance and background knowledge
• Efficient and effective solutions for preserving evolving knowledge graphs
• Models for representing provenance and evolution
• Quality of Evolving Knowledge Graphs
• Change-detection based quality assessment and validation
• Quality trends and prediction in evolving knowledge graphs
• Hybrid approaches for knowledge graph curation
• Evaluation of Knowledge Graph Evolution
• Benchmarks for managing, predicting, and curating evolution
• Real-world applications of evolving knowledge graphs
• Automatic and human-based techniques for evaluating evolving knowledge graph

We envision four types of submissions covering the entire workshop topics spectrum:

1. Research Papers (max 10 pages), presenting novel scientific research addressing topics of the workshop.
2. Position & Demo papers (max 5 pages), encouraging papers describing significant work in progress, late breaking results or ideas of the domain, as well as functional systems relevant to the community.
3. Industry & Use Case Presentations (max 5 pages), in which industry experts can present and discuss practical solutions, use case prototypes, best practices, etc. at any stage of implementation.
4. Expression of Interest (max 2 pages), presenting a research topic, a work in progress, practical applications or needs, etc.

The proceedings of the workshops will be published in the CEUR-WS. Papers must be submitted in PDF according to the LLNCS format or in HTML. The PDF files must have all non-standard fonts embedded. Workshop submissions must be self-contained and in English.

All papers should be submitted to https://easychair.org/conferences/?conf=mepdaw2020.

• Submission: August 10^th, 2020 August 17^th, 2020 September 8^th, 2020 (extended)
• Notification: August 3^rd, 2020 September 11^th, 2020 September 30^th, 2020 (extended)
• Camera-ready: September 2^nd, 2020 September 21^st, 2020 October 9^th, 2020 (extended)
• Presentation: November 1^st, 2020

Attending the workshop

MEPDaW will take place on Sunday 1st of November afternoon between 2pm and 6pm CET via Zoom. All hours are Central European Time (CET). (Don't forget to register... ☺)

Keynote

Sharing, Tracking, and Enhancing Highly Dynamic Knowledge Graphs
Professor Philippe Cudré-Mauroux, eXascale Infolab, University of Fribourg, Switzerland
https://exascale.info/

Abstract: Knowledge Graphs are often seen as static and complete artifacts. However, my experience with knowledge graphs shows the exact opposite, i.e., they are in practice highly dynamic and incomplete. In this talk, I will give an overview of some of the recent techniques we developed in my lab to take this fact into account and improve the automated processing of large-scale and evolving Knowledge Graphs. I will first describe data-driven techniques to identify information gaps in Knowledge Graphs (e.g., in terms of missing classes or properties). I will then describe a series of methods to impute missing values from the graphs, before presenting two large-scale system deployments we were involved in: one for Swiss open research data, and one for knowledge tracking on Microsoft Azure.

Bio: Philippe Cudre-Mauroux is a Full Professor and the Director of the eXascale Infolab at the University of Fribourg in Switzerland. He received his Ph.D. from the Swiss Federal Institute of Technology EPFL, where he won both the Doctorate Award and the EPFL Press Mention. Before joining the University of Fribourg, he worked on information management infrastructures at IBM Watson (NY), Microsoft Research Asia and Silicon Valley, and MIT. He recently won the Verisign Internet Infrastructures Award, a Swiss National Center in Research award, a Google Faculty Research Award, as well as a 2 million Euro grant from the European Research Council. His interests are in next-generation software infrastructures for Big Data and AI.

Agenda

Organisers

• Fabrizio Orlandi (ADAPT Centre, Trinity College Dublin, Ireland) is a Senior Research Fellow at Trinity College Dublin. His research focuses on knowledge management, Linked (Open) Data, Big Data technologies, data governance and personalisation. Prior to joining ADAPT he worked as post-doctoral researcher at Fraunhofer IAIS applying his research on large EU-funded and industry projects.
• Damien Graux (ADAPT Centre, Trinity College Dublin, Ireland) is a Senior Research Fellow at the ADAPT Centre in Trinity College Dublin. He has been contributing to research efforts in Semantic Web technologies and focusing on distributed query evaluation and on designing complex transformation pipelines for heterogeneous Big Data. Prior to joining ADAPT, he was a senior researcher at Fraunhofer IAIS (Germany).
• Maria-Esther Vidal (Universidad Simon Bolivar and Technische Informationsbibliothek (TIB)) is a full professor (on-leave) at Universidad Simón Bolívar (USB) Venezuela and a Research Scientist at the Technische Informationsbibliothek (TIB). Her interests include data and knowledge management, knowledge representation, big data, and semantic web.
• Javier D. Fernández (F. Hoffmann-La Roche AG in Switzerland) is an information architect at Roche based in Basel. Before joining Switzerland, Javier was a post-doctoral research fellow at Vienna University where he focused on efficient management of Big Semantic Data, privacy-aware Big Data technologies, RDF streaming, archiving and querying dynamic Linked Data.
• Jeremy Debattista (TopQuadrant Inc.) is a Solutions Architect at TopQuadrant Inc. His research interests include Linked Data Quality, Big Linked Data, Data Governance, Knowledge Management and Machine Learning.

Advisory Board

• Laure Berti-Equille, IRD Marseille, France
• Declan O’Sullivan, ADAPT Centre, Trinity College Dublin, Ireland
• James Anderson, Dydra - Datagraph, USA
• Axel Polleres, Vienna University of Economics and Business, Austria

Program Committee

Important Dates

• Submission (EasyChair): September 8^th, 2020
• Notification: September 30^th, 2020
• Camera-ready: October 9^th, 2020
• Presentation: November 1^st, 2020