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Found 10 papers, 1 papers with code
Artificial Intelligence for Literature Reviews: Opportunities and Challenges
This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs).
Knowledge Graphs: Opportunities and Challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately.
Characterising Research Areas in the field of AI
Interest in Artificial Intelligence (AI) continues to grow rapidly, hence it is crucial to support researchers and organisations in understanding where AI research is heading.
Trans4E: Link Prediction on Scholarly Knowledge Graphs
This is typical for KGs that categorize a large number of entities (e. g., research articles, patents, persons) according to a relatively small set of categories.
Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs
Analysing research trends and predicting their impact on academia and industry is crucial to gain a deeper understanding of the advances in a research field and to inform critical decisions about research funding and technology adoption.
The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment.
Improving Editorial Workflow and Metadata Quality at Springer Nature
For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors.
Ontology-Based Recommendation of Editorial Products
SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products.
Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain
As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph.
Ontology Extraction and Usage in the Scholarly Knowledge Domain
Ontologies of research areas have been proven to be useful in many application for analysing and making sense of scholarly data.
