The researchers collated over 5000 financial news documents as part of the TKGQA dataset, and for each of these documents extracted facts, question-answer pairs and before and after temporal knowledge graphs. The TKGQA dataset was created to address these challenges by providing a more realistic basis for developing methods to update and verify knowledge graphs through the use of question and answering. Question and answeringĬurrently, it is challenging to accurately validate the evolution of a temporal knowledge graph and there is no reliable method to verify the updated knowledge graphs once information has been altered.
While similar datasets exist, the TKGQA is the first to represent the real world more closely as it uses entity and relationship extraction combined rather than simply one or the other.
However, that is unrealistic to the real world and therefore in order to capture changes over time, the knowledge graph must evolve into one that is temporal. Most research into knowledge graphs has focused on static knowledge graphs, where facts remain unchanged over time. What are knowledge graphs?Ī knowledge graph represents the world through structural facts, consisting of entities and relationships – entities are real-world objects such as people, companies, countries etc and relationships capture the relation between these real-world objects. Through this research, in collaboration with the Royal Bank of Canada, Imperial data scientists have created a way to more accurately update the knowledge graphs using a dataset that more closely represents the real world. It is important to ensure these knowledge graphs are being updated accurately with the latest information so that these applications can perform better and faster.
Knowledge graphs are like databases that represent information about the world and are increasingly being used in applications such as recommendation systems, chatbots and semantic searches. In a recent study, published in Data, PhD student Ryan Ong, Research Fellow Dr Ovidiu Serban, Honorary Research Fellow Dr Jiahao Sun and Co-Director Professor Yi-Ke Guo from the Data Science Institute created a new dataset called TKGQA (Temporal Knowledge Graph Question Answering) to help researchers develop better ways to update and validate knowledge graphs using question and answering. New study uses question and answering to help ensure the accurate update of knowledge graphs – used in recommendation systems and chatbots. Visual representation of a knowledge graph, created by PhD student Ryan Ong using artificial intelligence
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