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

Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark
of human intelligence, it involves a degree of explicit reading comprehension, interpretation …

Question answering (QA) has become a popular way for humans to access billion-scale
knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and …

Templates are an important asset for question answering over knowledge graphs,
simplifying the semantic parsing of input utterances and generating structured queries for …

The gap between unstructured natural language and structured data makes it challenging to
build a system that supports using natural language to query large knowledge graphs. Many …

Logical queries constitute an important subset of questions posed in knowledge graph
question answering systems. Yet, effectively answering logical queries on large knowledge …

In recent years, many knowledge bases have been constructed or populated. These
knowledge bases link real-world entities by their relationships on a large scale, serving as …

Translating natural language questions to semantic representations such as SPARQL is a
core challenge in open-domain question answering over knowledge bases (KB-QA) …

The ever-increasing knowledge graphs impose an urgent demand of providing effective and
easy-to-use query techniques for end users. Structured query languages, such as SPARQL …

Due to the increasing scale and complexity of software products, software maintenance
especially on bug resolution has become a challenging task. Generally in large-scale …