Advances in Artificial Intelligent Systems for the Scholarly Domain

Dear Colleagues,

In recent decades, we have witnessed a substantial increase in the volume of scientific papers and related research objects (e.g., data sets, pipelines, and software packages). This trend is expected to continue, making it increasingly difficult to efficiently manage scientific outcomes. This opens new fundamental challenges to producing innovative and intelligent services based on machine learning, natural language processing, semantic web best practices, etc., for scientific research outcomes. Analyzing, exploring, and interlinking the existing literature is crucial to produce new knowledge, enhance our understanding of the world, and address the fundamental challenges of our time. Furthermore, the scientific community is also exploring AI tools based on machine learning to support researchers in their daily tasks, such as reading and writing research publications, developing communities, and organizing research outcomes, making possible the reproducibility of research works. However, these tasks are more and more difficult due to the millions of heterogeneous scientific articles that include images, natural language text, and external data, which cannot be directly parsed using conventional tools. For this reason, the research community has proposed several solutions for producing structured, interlinked, machine-readable, and machine-understandable descriptions of scientific knowledge from research papers to provide new services. This Special Issue aligns with these topics and provides a forum for researchers and practitioners to describe the latest discoveries.

This Special Issue is focused on but not limited to the following topics:

Knowledge Graphs:

• Ontologies for scientific publishing;
• Interlinking of various scientific data sources;
• Scientific knowledge graph construction approaches;
• Methods to analyze scientific knowledge graphs;
• Scientific knowledge graph completion;
• Scientific knowledge graph validation approaches;
• Frameworks to manage scientific knowledge graphs.

Machine Learning and Natural Language Processing:

• Machine learning to classify scientific papers;
• Machine learning to summarize scientific papers;
• Methods for extracting data from the natural language of scientific papers;
• Methods for extraction data from images of scientific papers;
• Methods for extracting data from tables of scientific papers.

Resources:

• Dataset for studying scientific outcomes;
• Benchmarks to evaluate machine learning models.

Methodologies:

• Research trend forecasting;
• Automatic state-of-the-art generation approaches;
• Frameworks to explore scientific outcomes;
• Citation prediction methodologies using scientific literature;
• Methods to evaluate the impact of scientific findings;
• Studies about the use of research findings in industry;
• Novel metrics to evaluate research outcomes.

Dr. Danilo Dessì 
Prof. Dr. Diego Reforgiato Recupero
Dr. Francesco Osborne
Guest Editors

Manuscript Submission Information

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