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

Smart speakers such as Alexa and Google Home introduced Artificial Intelligence (AI) in
millions soon billions of households, making AI an everyday experience. We can now look …

In the last few years, a number of NLP researchers have developed and participated in the
task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language …

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 …

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 …

Question answering over knowledge graphs (KGQA) has evolved from simple single-fact
questions to complex questions that require graph traversal and aggregation. We propose a …

Modern question answering (QA) systems need to flexibly integrate a number of
components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named …

Abstract Community Question Answering (cQA) services like Yahoo! Answers1, Baidu
Zhidao2, Quora3, StackOverflow4 etc. provide a platform for interaction with experts and …

The names of tourism types formed by scholars and practitioners reflect the connotations of
various tourism types from different aspects and carry a wealth of tourism knowledge. The …