MEPDaW'23 - 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 eight years, the MEPDaW workshop series has been gathering researchers from the community around these challenges. So far the series successfully published more than 50 research efforts allowing more than 60 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 CEURART 1-column format. The PDF files must have all non-standard fonts embedded. Workshop submissions must be self-contained and in English. Note: The review process is single-blind, no need for authors to submit anonymous articles.

All papers should be submitted to https://easychair.org/conferences/?conf=mepdaw2023.

• Submission: August 8^th, 2023
• Notification: September 4^th, 2023
• Camera-ready: September 25^th, 2023
• Presentation: November 6^th, 2023

Attending the workshop

MEPDaW will take place on Monday 6^th of November afternoon between 2pm and 5:20pm. All hours are Athens time. (Please, don't forget to register and attend... ☺)

Academic Keynote

Title: Challenges in Data Management for Evolving Knowledge Graphs
David Chaves-Fraga, assistant professor, University of Santiago de Compostela, Spain
https://citius.gal/team/david-chaves-fraga

Abstract: KGs are playing an increasingly significant role in scientific and industrial domains as they have demonstrated their potential to represent the convergence between data and knowledge using a graph data model. KGs provide a solution to data heterogeneity and enable the representation of fine-grained domain knowledge. Despite the enormous efforts made by researchers and practitioners, the reality is that many data management challenges still appear in the development of KG-driven ecosystems. The construction and maintenance of these ecosystems suffer in key aspects such as maintainability, sustainability, scalability, and transparency, and they become even more complex when data and knowledge evolve. In this talk, we will analyze what are the main challenges that need to be addressed to ensure scalable, transparent, and maintainable data management techniques for the construction of evolving knowledge graphs. We will exemplify all these problems through the EU Public Procurement Data Space, a real-world project where a decentralized KG ecosystem is being built to enhance the transparency of public procurement across Europe.

Bio: David Chaves-Fraga is an assistant professor at the University of Santiago de Compostela (USC, Spain), a senior researcher at the Center for Research in Intelligent Technologies (CiTIUS@USC), and also a research collaborator at the Declarative Languages and Artificial Intelligence Group (DTAI) at KU Leuven, Belgium. His work is mostly focused on automating and optimizing the construction of knowledge graphs from heterogeneous data on the web using declarative mapping rules. He currently co-chairs the W3C Community Group Knowledge Graph Construction, where they pursue the standardization of the RDF Mapping Language (RML), and he is the main researcher of the EU Public Procurement Data Space. He received his PhD in Artificial Intelligence at Ontology Engineering Group (Universidad Politécnica de Madrid) in 2021.

Agenda

Organisers

• Damien Graux (Huawei Ltd., The UK) is a principal research scientist at the Huawei Research Center. He has been contributing to research efforts in Semantic Web technologies: focusing on query evaluation and designing complex pipelines for heterogeneous Big Data. Prior, he had research positions at Inria (France), Trinity College Dublin (Ireland) and Fraunhofer IAIS (Germany).
• 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.
• Emetis Niazmand (Leibniz Information Centre for Science and Technology (TIB) & Leibniz Universität Hannover, Germany) is a research assistant at the Scientific Data Management research group at TIB and Computer Science PhD student at Leibniz Universität Hannover. Her research interests include knowledge management, semantic web, and query processing over knowledge graphs.
• Gabriela Ydler (L3S Forshungszentrum, Research Center, Germany) is a student assistant at the Scientific Data Management research group at TIB. She studied at the Univ. Santa Maria (Venezuela) and majored in Social Communications. She is currently supporting the group in the area of external communications and finishing her bachelor degree in Anthropology and Geography at Universität Bonn.
• Maria-Esther Vidal (Leibniz Information Centre for Science and Technology (TIB) & Leibniz University, Hannover, Germany) is the head of the Scientific Data Management group at TIB and a full professor at the Leibniz University of Hannover. Her interests include data and knowledge management, knowledge representation, Big Data, and Semantic Web.

Advisory Board

• Philippe Cudré-Mauroux, eXascale Infolab, University of Fribourg, Switzerland
• Jeremy Debattista, TopQuadrant Inc
• Javier D. Fernández, Information Architect at Roche, Switzerland

Program Committee

Important Dates

• Submission (EasyChair): August 8^th, 2023
• Notification: September 4^th, 2023
• Camera-ready: September 25^th, 2023
• Presentation: November 6^th, 2023