Reasoning with Language Models and Knowledge Graphs for Question Answering

From search engines to personal assistants, we use question-answering systems every day. When we ask a question ("Where was the painter of the Mona Lisa born?"), the system needs to gather background knowledge ("The Mona Lisa was painted by Leonardo da Vinci", "Leonardo da Vinci was born in Italy") and reason over it to produce the answer ("Italy"). Knowledge sources In recent AI research, such background knowledge is commonly available in the forms of knowledge graphs (KGs) and language models (LMs) pre-trained on a large set of documents. In KGs, entities are represented as nodes and relations between them as edges, e.g. Examples of KGs include Freebase (general-purpose facts)1, ConceptNet (commonsense)2, and UMLS (biomedical facts)3.