Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

知识推理(KR) 作为知识图谱构建的一个重要环节, 一直是该领域研究的焦点问题.
随着知识图谱应用研究的不断深入和范围的不断扩大, 将图神经网络(GNN) …

Abstract Knowledge graph (KG) reasoning utilizes two primary techniques, ie, rule-based
and KG-embedding based. The former provides precise inferences, but inferring via …

Humankind's understanding of the world is fundamentally linked to our perception and
cognition, with\emph {human languages} serving as one of the major carriers of\emph {world …

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to
provide seamless coverage of the vehicular network. The presence of uncovered areas …

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were
constructed to store large-scale, real-world facts as (subject, predicate, object) triples-that …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …

Relation Extraction (RE) is the task of extracting semantic relationships between entities in a
sentence and aligning them to relations defined in a vocabulary, which is generally in the …

Abstract Knowledge Graph Embedding (KGE) has proven to be an effective approach to
solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to …

Quantitative investment (``quant'') is an interdisciplinary field combining financial
engineering, computer science, mathematics, statistics, etc. Quant has become one of the …