Document-level relation extraction (DocRE) aims to extract relations between entities in a
whole document. One of the pivotal challenges of DocRE is to capture the intricate …

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

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 …

Rule learning approaches for knowledge graph completion are efficient, interpretable and
competitive to purely neural models. The rule aggregation problem is concerned with finding …

Graph data is omnipresent and has a wide variety of applications, such as in natural
science, social networks, or the semantic web. However, while being rich in information …

The task of inductive knowledge graph completion requires models to learn inference
patterns from a training graph, which can then be used to make predictions on a disjoint test …

Knowledge Representation and Reasoning is a central, longstanding, and active area of
Artificial Intelligence. Over the years it has evolved significantly; more recently it has been …

Knowledge graph completion (KGC) aims to infer missing facts from existing facts. Learning
logical rules plays a pivotal role in KGC, as logical rules excel in explaining why a missing …

The standard evaluation protocol for measuring the quality of Knowledge Graph Completion
methods-the task of inferring new links to be added to a graph-typically involves a step …

Numerous large knowledge graphs, such as DBpedia, Wikidata, Yago and Freebase, have
been developed in the last decade, which contain millions of facts about various entities in …