Inductive link prediction---where entities during training and inference stages can be
different---has been shown to be promising for completing continuously evolving knowledge …

Abstract Rules over a Knowledge Graph (KG) capture interpretable patterns in data and
various methods for rule learning have been proposed. Since KGs are inherently …

Abstract Representation learning on a knowledge graph (KG) is to embed entities and
relations of a KG into low-dimensional continuous vector spaces. Early KG embedding …

Fact-checking is a crucial task for accurately populating, updating and curating knowledge
graphs. Manually validating candidate facts is time-consuming. Prior work on automating …

Abstract Recent success of Knowledge Graph has spurred widespread interests in methods
for the problem of Knowledge Graph completion. However, efforts to understand the quality …

Rule learning from large datasets has regained extensive interest as rules are useful for
developing explainable approaches to many applications in knowledge graphs. However …

Compared to black-box neural networks, logic rules express explicit knowledge, can provide
human-understandable explanations for reasoning processes, and have found their wide …

Multimodal Analytics in Big Data architectures implies compounded configurations of the
data processing tasks. Each modality in data requires specific analytics that triggers specific …

Rule-based approaches have proven to be an efficient and explainable method for
knowledge base completion. Their predictive quality is on par with classic knowledge graph …

Advances in information extraction have enabled the automatic construction of large
knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from …