Drift-a-LOD'18

Description

The continuous growth of the Linked Open Data (LOD) cloud is extending to various new domains. In many of these, knowledge changes continuously: political landscapes evolve, medical discoveries lead to new cures, artists establish new collaborations. In terms of knowledge representation, we observe that some concepts change their meaning, instances change their roles, new relations appear, old ones become invalid, and classes change both their definition and member-instances.

The evolution of LOD poses concrete new challenges to stakeholders: data publishers need to detect changes in the real world and capture them in their datasets; users and applications need automated tools to adapt querying over diachronic datasets; knowledge engineers want to understand modelling practices behind ontology changes; linguists study drift in the meaning of words.

As a continuation of the last two successful Drift-a-LOD workshops, this workshop seeks to form a community of researchers and practitioners working on detecting, representing and managing concept drift in and for LOD. Drift-a-LOD’18 will bring together different communities that define, identify and manage the dynamics of concepts in their knowledge bases using various domain-specific methods (statistical inference, symbolic reasoning, natural language processing, etc.), leveraging the LOD cloud as a data source or as a result publishing platform.

Topics of interest include, but are not restricted to:

• Detecting and predicting concept drift (using any method, including reasoning, data mining, word embeddings, machine learning and natural language processing);
• Representation of concept drift;
• Reasoning, querying, machine learning in the presence of evolving knowledge and drifting concepts;
• Theoretical explanations of drift dynamics;
• Empirical studies of how concepts drift;
• Visualization and presentation of evolving knowledge;
• Ontology evolution and concept drift;
• Frameworks addressing concept identity over time;
• Benchmarks for concept drift and evolving knowledge;
• Efficient storage and querying of diachronic semantic content;
• Tools, infrastructures, datasets and observatories of evolving semantic Web data;
• Evaluation of concept drift detection methods;
• Applications working in the presence of concept drift;
• Frameworks for monitoring/recording concept drift on a large scale (‘drift observatories’).

Submission Guidelines

We invite full papers and short papers. Submissions must be in PDF, formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). For details on the LNCS style, see Springer’s Author Instructions page with Latex and Word Templates and sample files. Contributions should not exceed 8 pages in length for full papers and 5 pages for short papers, including references and optional appendices. Papers can be submitted through the EasyChair submission system. Contributions may be accepted as either long of short presentations depending on quality, novelty, and potential to stimulate a discussion at the workshop.

Accepted contributions will be published on the CEUR-WS website (or equivalent). CEUR is an open access publisher, and copy-rights of the papers stay with the authors. We encourage authors to follow the recommendations of the Linked Research initiative, and to make their work as open as possible. This could be done, for example, by making papers easily accessible through home pages or other repositories, by making datasets and other resources available on the Web, or by publishing (parts of) the contribution in Web formats (possibly with a reference to the ceur-ws volume for citations).

At least one author needs to register for the workshop. Registration for the ESWC conference is mandatory for registration for the Drift-a-LOD workshop.