ConCur: Knowledge Base Construction and Curation

Knowledge graphs are graph-structured knowledge resources. A knowledge graph may include basic facts such as “London is the capital of the UK” and “London is a city” as well as structural knowledge about the domain such as “a city is a kind of human settlement”. A knowledge graph consisting largely or wholly of structural knowledge is often called an ontology. 

Some knowledge graphs are general purpose, such as Wikidata and the Google knowledge graph, while others are developed for specific domains such as medicine. They are rapidly gaining in importance and are playing a key role in many applications. For example, Google uses its knowledge graph for search, question answering and Google Assistant, while Amazon and Apple also use knowledge graphs to power their personal assistants Alexa and Siri, respectively. Knowledge graphs are also widely used in the domain of health and wellbeing, e.g., for organising and exchanging information and to power clinical artificial intelligence (AI).  

Knowledge graph construction and maintenance is, however, very challenging, and may require a considerable amount of human effort. Notwithstanding the high cost of knowledge creation, knowledge graphs are often still biased, incomplete or too coarse-grained. These issues significantly impact the usefulness of knowledge graphs and the reliability of the systems that use them. Therefore, effective knowledge graph construction and curation is urgently required and will play a critical role in exploiting their full value.  

This project will study a range of semantic and machine learning techniques, and how to combine them to support knowledge graph construction and curation. As well as its application to knowledge graph construction and curation, the research will also contribute to the development of new neural-symbolic theories, paradigms and methods, such as deep semantic embedding for learning representations for expressive knowledge, and knowledge-guided learning for addressing sample shortage problems. These techniques promise to revolutionise many AI and big data technologies.