Combining machine learning and semantic web: A systematic map** study
In line with the general trend in artificial intelligence research to create intelligent systems
that combine learning and symbolic components, a new sub-area has emerged that focuses …
that combine learning and symbolic components, a new sub-area has emerged that focuses …
Knowledge graph embedding for data mining vs. knowledge graph embedding for link prediction–two sides of the same coin?
Abstract Knowledge Graph Embeddings, ie, projections of entities and relations to lower
dimensional spaces, have been proposed for two purposes:(1) providing an encoding for …
dimensional spaces, have been proposed for two purposes:(1) providing an encoding for …
The DLCC node classification benchmark for analyzing knowledge graph embeddings
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 …
A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering
Attributed graph clustering aims to partition the nodes in a graph into groups such that the
nodes in the same group are close in terms of graph proximity and also have similar attribute …
nodes in the same group are close in terms of graph proximity and also have similar attribute …
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 …
Putting rdf2vec in order
The RDF2vec method for creating node embeddings on knowledge graphs is based on
word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we …
word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we …
KGrEaT: a framework to evaluate knowledge graphs via downstream tasks
In recent years, countless research papers have addressed the topics of knowledge graph
creation, extension, or completion in order to create knowledge graphs that are larger, more …
creation, extension, or completion in order to create knowledge graphs that are larger, more …
Walk this way! entity walks and property walks for rdf2vec
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from
knowledge graphs by performing random walks, then feeds those into the word embedding …
knowledge graphs by performing random walks, then feeds those into the word embedding …
FeaBI: A Feature Selection-Based Framework for Interpreting KG Embeddings
Abstract Knowledge Graph (KG) embedding methods represent KG entities as vectors in an
embedding space, and they have been successfully used for a variety of tasks, including link …
embedding space, and they have been successfully used for a variety of tasks, including link …
Learning embedded representation of the stock correlation matrix using graph machine learning
Understanding non-linear relationships among financial instruments has various
applications in investment processes ranging from risk management, portfolio construction …
applications in investment processes ranging from risk management, portfolio construction …