Research & Development
As a science publishing house operating in an era of digital transformation, it is imperative for us to apply best practices to all aspects of our portfolio. We develop and offer relevant content, workflow tools, and services that nurture collaboration, and we do that by supporting innovation through research and development (R&D) via our network of researchers at institutions across the globe. Covering numerous fields and subject areas, we have a significant interest in addressing forward-looking topics. Current investigations include transparency and reproducibility, the incorporation of semantic entities for published metadata, and the development and application of proprietary software.
Open science research
We are collaborating with Pascal Hitzler, director of the Center for Artificial Intelligence and Data Science at Kansas State University, and Krzysztof Janowicz, director of the Center for Spatial Studies at the University of California, in relation to the project "Open Science Data in Semantic Web Research." The team hopes to demonstrate that the open sharing of data underlying publications can be done without significant overhead and will provide significant added value. The approach can then serve as a model for other journal editors who seek a blueprint for adopting similar open science data practices. Dr. Hitzler and Dr. Janowicz are co-Editors-in-Chief of the open access Semantic Web journal.
We are collaborating with Tobias Kuhn from the Department of Computer Science at VU University Amsterdam, who is also the co-Editor-in-Chief of Data Science (DS), and Cristina Bucur, researcher in semantic web and artificial intelligence at Vrije Universiteit Amsterdam, on a project relating to linkflow and nanopublications. It highlights the need for semantically-enriched articles to make publishing more transparent and machine-interpretable. The focus is on a unified and semantic publication model based on nanopublications that can make scientific communication more effective and user-friendly.
Linked data research
We publish hundreds of journal articles and book chapters every month. How can we ensure all of this content stands out and is discoverable? The answer is that we make sure it is machine readable, with the data being structured so that it can be interlinked with other data – making it useful through semantic queries. Using this linked data, we are developing tools for users to dive in and gain insights from all the published content. We do this through our knowledge graph. Our linked data portal LD Connect builds a powerful knowledge graph using links between the data known as “triples” in the form of subject–predicate–object expressions. By enriching and fostering the interlinking of data, contextual relationships among authors, institutions, and research areas can be visualized and interpreted and new links uncovered. A full suite of powerful semantic search tools to query and visualize data is under development.
LD Connect was developed in collaboration with STKO Lab at UC Santa Barbara and the co-reference resolution with DaSe Lab at Wright State University and Kansas State University.
"It is exciting to see the growing interest in improving machine actionability in the data space. Initiatives such as IOS Press’ LD Connect, which makes available a significant amount of publications-related metadata in RDF, greatly contributes to this movement and improves the FAIRness of these resources. I hope this sets a new trend in the publishing sector."
Luiz Bonino, PhD, associate professor at Leiden University Medical Center and University of Twente
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