With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

Knowledge graph completion (KGC) aims to reason over known facts and infer the missing
links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations …

Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Abstract Knowledge Graphs (KG) are multi-relational graphs consisting of entities as nodes
and relations among them as typed edges. Goal of the Question Answering over KG (KGQA) …

Contextual word representations, typically trained on unstructured, unlabeled text, do not
contain any explicit grounding to real world entities and are often unable to remember facts …

Pre-trained language representation models (PLMs) cannot well capture factual knowledge
from text. In contrast, knowledge embedding (KE) methods can effectively represent the …

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 …