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) …

Automatic Question Answering (QA) has been successfully applied in various domains such
as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables …

Question answering (QA) models have shown rapid progress enabled by the availability of
large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to …

CommonsenseQA (CQA)(Talmor et al., 2019) dataset was recently released to advance the
research on common-sense question answering (QA) task. Whereas the prior work has …

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

Real-world factoid or list questions often have a simple structure, yet are hard to match to
facts in a given knowledge base due to high representational and linguistic variability. For …

Abstract 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 …

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 …

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 …

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 …