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Found 10 papers, 2 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).
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.
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.
Commonsense Spatial Reasoning for Visually Intelligent Agents
Differently from prior approaches to qualitative spatial reasoning, the proposed framework is robust to variations in the robot's viewpoint and object orientation.
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.
Fit to Measure: Reasoning about Sizes for Robust Object Recognition
While object recognition solutions are traditionally based on Machine Learning methods, augmenting them with knowledge based reasoners has been shown to improve their performance.
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.
Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis
A key capability required by service robots operating in real-world, dynamic environments is that of Visual Intelligence, i. e., the ability to use their vision system, reasoning components and background knowledge to make sense of their environment.
