The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings
M Färber, L Ao - Quantitative Science Studies, 2022 - direct.mit.edu
Although several large knowledge graphs have been proposed in the scholarly field, such
graphs are limited with respect to several data quality dimensions such as accuracy and …
graphs are limited with respect to several data quality dimensions such as accuracy and …
Relational data embeddings for feature enrichment with background information
For many machine-learning tasks, augmenting the data table at hand with features built from
external sources is key to improving performance. For instance, estimating housing prices …
external sources is key to improving performance. For instance, estimating housing prices …
The DLCC node classification benchmark for analyzing knowledge graph embeddings
J Portisch, H Paulheim - International semantic web conference, 2022 - Springer
Abstract Knowledge graph embedding is a representation learning technique that projects
entities and relations in a knowledge graph to continuous vector spaces. Embeddings have …
entities and relations in a knowledge graph to continuous vector spaces. Embeddings have …
Concept graph construction and applied research of agricultural remote sensing
Remote sensing technology in the new era gradually need a new scientific paradigm driven
by big data and knowledge. However, in the current research, the use of existing knowledge …
by big data and knowledge. However, in the current research, the use of existing knowledge …
Knowledge graph embeddings: open challenges and opportunities
While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …
knowledge, in recent years, KG embeddings have become a popular way of deriving …
Leveraging knowledge graphs for classifying incident situations in ICT systems
The complexity of Information and Communications Technology (ICT) systems, such as
enterprise or Internet access provider networks, entails uncertainty in causal reasoning for …
enterprise or Internet access provider networks, entails uncertainty in causal reasoning for …
pyRDF2Vec: A Python Implementation and Extension of RDF2Vec
B Steenwinckel, G Vandewiele, T Agozzino… - European Semantic …, 2023 - Springer
This paper introduces pyRDF2Vec, a Python software package that reimplements the well-
known RDF2Vec algorithm along with several of its extensions. By making the algorithm …
known RDF2Vec algorithm along with several of its extensions. By making the algorithm …
pyrdf2vec: A python implementation and extension of rdf2vec
G Vandewiele, B Steenwinckel, T Agozzino… - arXiv preprint arXiv …, 2022 - arxiv.org
This paper introduces pyRDF2Vec, a Python software package that reimplements the well-
known RDF2Vec algorithm along with several of its extensions. By making the algorithm …
known RDF2Vec algorithm along with several of its extensions. By making the algorithm …
[HTML][HTML] Construction of a fluvial facies knowledge graph and its application in sedimentary facies identification
Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and
compilation of sedimentary facies. Traditional sedimentary facies analysis is a labor …
compilation of sedimentary facies. Traditional sedimentary facies analysis is a labor …
Refining diagnosis paths for medical diagnosis based on an augmented knowledge graph
N Heilig, J Kirchhoff, F Stumpe, J Plepi, L Flek… - arXiv preprint arXiv …, 2022 - arxiv.org
Medical diagnosis is the process of making a prediction of the disease a patient is likely to
have, given a set of symptoms and observations. This requires extensive expert knowledge …
have, given a set of symptoms and observations. This requires extensive expert knowledge …