Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …

Abstract Background Question Answering (QA) systems enable users to retrieve exact
answers for questions posed in natural language. Objective This study aims at identifying …

Knowledge graphs are important resources for many artificial intelligence tasks but often
suffer from incompleteness. In this work, we propose to use pre-trained language models for …

Recently, considerable literature has grown up around the theme of few-shot named entity
recognition (NER), but little published benchmark data specifically focused on the practical …

Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they
frequently face the issue of incompleteness. In this study, we explore utilizing Large …

Existing studies on question answering on knowledge bases (KBQA) mainly operate with
the standard iid assumption, ie, training distribution over questions is the same as the test …

In recent years, pre-trained language models (PLMs) have been shown to capture factual
knowledge from massive texts, which encourages the proposal of PLM-based knowledge …

Reasoning is essential for the development of large knowledge graphs, especially for
completion, which aims to infer new triples based on existing ones. Both rules and …

Over the last few years, natural language interfaces (NLI) for databases have gained
significant traction both in academia and industry. These systems use very different …

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different
sources or languages, which may benefit many knowledge-driven applications. It is …