In this article, we provide a comprehensive introduction to knowledge graphs, which have
recently garnered significant attention from both industry and academia in scenarios that …

Equip** machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Many online applications, such as online social networks or knowledge bases, are often
attacked by malicious users who commit different types of actions such as vandalism on …

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …

Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related
downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are …

The Natural Language Processing (NLP) community has significantly contributed to the
solutions for entity and relation recognition from a natural language text, and possibly linking …

Knowledge graphs (KGs) are of great importance in various artificial intelligence systems,
such as question answering, relation extraction, and recommendation. Nevertheless, most …

Knowledge graphs (KGs) can structurally organize large-scale information in the form of
triples and significantly support many real-world applications. While most KG embedding …

The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has
been widely used in intelligent analysis and applications. However, possible noises and …

Abstract Knowledge graphs (KGs), which could provide essential relational information
between entities, have been widely utilized in various knowledge-driven applications. Since …