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 …

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and
industry. Conventional KG reasoning based on symbolic logic is deterministic, with …

Medication recommendation systems have gained significant attention in healthcare as a
means of providing tailored and effective drug combinations based on patients' clinical …

Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing
works mainly concentrate on mining rules. However, there might be several rules that could …

This paper aims to establish a generic multi-modal foundation model that has the scalable
capability to massive downstream applications in E-commerce. Recently, large-scale vision …

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences
for the items they are seeking so as to provide appropriate recommendations …

In this paper, we address multi-modal pretraining of product data in the field of E-commerce.
Current multi-modal pretraining methods proposed for image and text modalities lack …

The use of pre-training is an emerging technique to enhance a neural model's performance,
which has been shown to be effective for many neural language models such as BERT. This …

While Graph Neural Networks (GNNs) have demonstrated remarkable achievements in
knowledge graph reasoning, their computational efficiency on conventional computing …

Multi-task learning has been established as an important machine learning framework for
leveraging shared knowledge among multiple different but related tasks, with the …