Open-domain Question Answering (OpenQA) is an important task in Natural Language
Processing (NLP), which aims to answer a question in the form of natural language based …

Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

The dominant paradigm for relation prediction in knowledge graphs involves learning and
operating on latent representations (ie, embeddings) of entities and relations. However …

In traditional RAG framework, the basic retrieval units are normally short. The common
retrievers like DPR normally work with 100-word Wikipedia paragraphs. Such a design …

In open question answering (QA), the answer to a question is produced by retrieving and
then analyzing documents that might contain answers to the question. Most open QA …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

Large Transformer models have achieved impressive performance in many natural
language tasks. In particular, Transformer based language models have been shown to …

This paper shows how to construct knowledge graphs (KGs) from pre-trained language
models (eg, BERT, GPT-2/3), without human supervision. Popular KGs (eg, Wikidata, NELL) …