Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

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) …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

We address the problem of Visual Question Answering (VQA), which requires joint image
and language understanding to answer a question about a given photograph. Recent …

Training large-scale question answering systems is complicated because training sources
usually cover a small portion of the range of possible questions. This paper studies the …

In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on
annotated logical forms, which is especially expensive to obtain at large scale, we learn from …

RDF question/answering (Q/A) allows users to ask questions in natural languages over a
knowledge base represented by RDF. To answer a natural language question, the existing …

Answering natural language questions using the Freebase knowledge base has recently
been explored as a platform for advancing the state of the art in open domain semantic …

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 …

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 …