However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially.
To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning.
The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets.
Large language models (LLMs) have achieved great success in general domains of natural language processing.
The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.
We focus on the case where the underlying joint distribution of complete features and label is invariant, but the missing pattern, i. e., mask distribution may shift agnostically between training and testing.
In this work, we propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
In the research of end-to-end dialogue systems, using real-world knowledge to generate natural, fluent, and human-like utterances with correct answers is crucial.
In this work, we first collect a large-scale institution name normalization dataset LoT-insts1, which contains over 25k classes that exhibit a naturally long-tailed distribution.
Ranked #1 on Long-tail Learning on Lot-insts
This is achieved by aligning the hierarchy of the rooted-tree of a central node with the ordered neurons in its node representation.
Ranked #2 on Node Classification on Actor
Constructing a comprehensive, accurate, and useful scientific knowledge base is crucial for human researchers synthesizing scientific knowledge and for enabling Al-driven scientific discovery.
Understanding the origin and influence of the publication's idea is critical to conducting scientific research.
In review-based recommendation methods, review data is considered as auxiliary information that can improve the quality of learned user/item or interaction representations for the user rating prediction task.
Our model can incorporate almost all types of existing relations in the literature, and in addition, we propose introducing co-reference relations for the multi-turn scenario.
Ranked #1 on Dialogue State Tracking on CoSQL
Our method provides an automatic process that maps the raw data to the classification results.