The forthcoming volume “Knowledge Graphs and RDF Data Provenance: AI Actions with Machine-Interpretable Data” of the Springer book series “Advanced Information & Knowledge Processing” now invites chapters.

Aim and Scope
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Formal knowledge representation is proven to be efficient in information fusion of heterogeneous data derived from diverse data sources, however, implicit information generated via automated reasoning quite often cannot be accepted on an “as is” basis. Explainable AI requires additional information, such as the source, software and version, responsible person, platform, and similar properties of data generation so that decision-makers can justify their decisions. These can be useful in a wide range of applications from data processing to privacy preserving, from knowledge graphs to entity resolution.

With the emergence of data science, the importance of several Semantic Web standards became apparent. However, while the RDF data model has many benefits that make it more appealing for many areas of data science than other representations, standard RDF triples have limitations in terms of metadata, and provenance in particular. This is, however, crucial when it comes to data processing in applications that require and/or feature dynamic data, such as in communication networks.

Machine-interpretable statements in the form of subject-predicate-object triples became ubiquitous in AI applications that rely on formal knowledge representation and automated reasoning. The power of RDF lies in its simplicity, but the elements of RDF statements do not inherently hold information about statement context, making it impossible to use groups of statements in a different file or at a different web address, or to draw conclusions about statements inferred from explicit knowledge. RDF lacks a built-in mechanism to attach metadata, such as provenance data, crucial to make automatically generated/processed data authoritative. Therefore, several data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras have been proposed. This volume is a collection of state-of-the-art RDF provenance approaches that capture provenance for RDF triples at various levels, including syntax, semantics, and implementation.

Submission Guidelines
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Submissions are expected from, but are not limited to, the following topics:

Knowledge graphs with provenance
RDF provenance approaches, mechanisms to capture provenance-aware RDF statements
Extensions of the standard RDF data model to capture provenance
Formal knowledge representation with RDF quadruples
Automated reasoning over context-aware RDF statements
RDF provenance in data science
Information fusion based on RDF provenance
Applications of RDF representations complemented by provenance data
Alternate structured representations
Inception and evolution of RDF provenance
Standardization efforts for provenance
Commercial applications of provenance-aware RDF datasets
Future outlook for provenance-aware RDF formalisms

Proposal
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Chapter proposals should be 100–150 words and should be submitted to l [dot] sikos [at] ecu [dot] edu [dot] au.

Submission
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If your proposed chapter is accepted, you can use the proposal as your chapter abstract. The purpose of this book is to provide a concise introduction to a specific topic from the standpoint of a newcomer. As such, each chapter must be self-contained. The average length of each chapter is 25–30 pages. Co-authored chapters are welcome. The manuscripts are expected to be prepared in LaTeX using the corresponding Springer template. U.S. spelling is preferred.

Note: in case you consider submitting an extended version of a previously published material as a chapter, you are responsible for requesting the required reuse permissions from the publisher (e.g., request from IEEE here, from Springer here).

Important Dates
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Abstract submission: 1 November 2019
Author notification: 15 November 2019
Chapter draft due: 13 April 2020
Peer review due: 15 June 2020
Revised/final chapter due: 17 August 2020