Abstract The Semantic Web contains an enormous amount of information in the form of
knowledge bases (KB). To make this information available, many question answering (QA) …

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

Existing knowledge-based question answering systems often rely on small annotated
training data. While shallow methods like relation extraction are robust to data scarcity, they …

Semantic Question Answering (SQA) removes two major access requirements to the
Semantic Web: the mastery of a formal query language like SPARQL and knowledge of a …

This article provides a comprehensive overview of the broad area of semantic search on text
and knowledge bases. In a nutshell, semantic search is “search with meaning”. This …

Many question answering systems over knowledge graphs rely on entity and relation linking
components in order to connect the natural language input to the underlying knowledge …

Question Answering (QA) systems aim at supplying precise answers to questions, posed by
users in a natural language form. They are used in a wide range of application areas, from …

With the development of the Semantic Web, a lot of new structured data has become
available on the Web in the form of knowledge bases (KBs). Making this valuable data …

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 …