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 …

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent
entity pairs. While existing methods heavily rely on human-generated labels, it is …

Generating recommendation reasons for recommendation results is a long-standing
problem because it is challenging to explain the underlying reasons for recommending an …

Knowledge-based visual question answering (KVQA) has been extensively studied to
answer visual questions with external knowledge, eg, knowledge graphs (KGs). While …

Knowledge-based question answering (KBQA) is widely used in many scenarios that
necessitate domain knowledge. Large language models (LLMs) bring opportunities to …

Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of
newly-coming entities that never appeared in the training set. Such a setting is more in line …

Neural symbolic knowledge graph (KG) reasoning offers a promising approach that
combines the expressive power of symbolic reasoning with the learning capabilities inherent …

Collaborative filtering on user-item interaction graphs has achieved success in the industrial
recommendation. However, recommending users' truly fascinated items poses a seesaw …

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that
combines symbolic reasoning methods with deep learning to leverage their complementary …