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 Knowledge Graphs (KGs), as a structured human knowledge, manage data in an
ease-of-store, recognizable, and understandable way for machines and provide a rich …

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 …

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 …

This work studies the use of visual semantic representations to align entities in
heterogeneous knowledge graphs (KGs). Images are natural components of many existing …

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

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 …