Semantic embedding of knowledge graphs has been widely studied and used for prediction
and statistical analysis tasks across various domains such as Natural Language Processing …

Reasoning is essential for the development of large knowledge graphs, especially for
completion, which aims to infer new triples based on existing ones. Both rules and embeddings …

Zero-shot learning has been a tough problem since no labeled data is available for unseen
classes during training, especially for classes with low similarity. In this situation, transferring …

Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by
filling in missing connections via link prediction or relation extraction. One of the main …

Deep neural networks have achieved promising results in stock trend prediction. However,
most of these models have two common drawbacks, including (i) current methods are not …

Large Language Models (LLMs) have taken Knowledge Representation -- and the world --
by storm. This inflection point marks a shift from explicit knowledge representation to a …

Ontology alignment (aka ontology matching (OM)) plays a critical role in knowledge integration.
Owing to the success of machine learning in many domains, it has been applied in OM. …

Automatically annotating column types with knowledge base (KB) concepts is a critical task
to gain a basic understanding of web tables. Current methods rely on either table metadata …

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from
knowledge graphs (eg, Wikidata or DBpedia) to the elements of a table. This task is a …

Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared
in the training data, has arisen hot research interests. The key of implementing ZSL is to …