There is a vast and rapidly increasing quantity of scientific, corporate, government, and crowd-sourced data published on the emerging Data Web. Moreover, knowledge graphs have emerged as scalable knowledge models for integrating data collected from heterogeneous data sources, e.g., Data Web, and representing the meaning of these data and their properties. Knowledge graphs enable the modeling of facts that correspond to items of data, knowledge, or actionable insights, as well as the relations among these facts. The wide amount of sources of data and knowledge, offers a great potential for building innovative products and services that create new value; they are expected to foster active citizenship (e.g., around topics of journalism, greenhouse gas emissions, food supply-chains, smart mobility) and world-wide research according to the “fourth paradigm of science”.
Open access datasets are publicly available on the Web. The traditional way of digitally preserving them by “locking them away” for future use, conflicts with their evolution. There are a number of approaches and frameworks that manage a full life-cycle of the Data Web and knowledge graphs. More specifically, these techniques are expected to tackle major issues such as the synchronisation problem (how to monitor changes), the curation problem (how to repair data imperfections and add value over time), the appraisal problem (how to assess the quality of a source of data or knowledge), the citation and provenance problem (how to cite a particular version, how to keep the lineage/provenance), the archiving problem (how to retrieve the most recent or a particular version of a source), and the sustainability problem (how to support preservation 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.
This topic is of particular relevance to The Web Conference since it raises awareness of the many research challenges for preserving and managing knowledge graphs that evolve over time. Fostering active usage of such evolving knowledge graphs requires further research advances on topics such as storage, synchronisation, change representation and querying. Solutions to these problems correspond to main subjects of interests of the workshop.