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

Two important aspects of semantic parsing for question answering are the breadth of the
knowledge source and the depth of logical compositionality. While existing work trades off …

Question Answering (QA) systems over Knowledge Graphs (KG) automatically answer
natural language questions using facts contained in a knowledge graph. Simple questions …

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 …

This paper is a survey of the research topics in the field of Semantic Web, Linked Data and
Web of Data. This study looks at the contributions of this research community over its first …

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

As an increasing amount of RDF data is published as Linked Data, intuitive ways of
accessing this data become more and more important. Question answering approaches …

A few years after Google announced that their 'Knowledge Graph'would have allowed
searching for things, not strings, 1 knowledge graphs start entering information retrieval …

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 …

The need to query information content available in various formats including structured and
unstructured data (text in natural language, semi-structured Web documents, structured RDF …