Dr. Jianzhong Qi: Knowledge base enrichment via deep neural networks

Presenter: Jianzhong Qi

Abstract:

A knowledge base is a large repository that typically stores information about real world entities. Several efforts have been made to develop knowledge bases in general and specific domains such as DBpedia, YAGO, LinkedGeoData, and Wikidata. These knowledge bases contain millions of facts about entities. However, they are far from complete and mandate continuous enrichment and curation. In this talk, we present three methods to enrich a knowledge base. The first is a Knowledge Bases Alignment method that finds entities in two knowledge bases which represent the same real-world entity, and then integrates these knowledge bases based on the aligned entities. The second is a Relation Extraction method that extracts entities and their relationships from sentences of a corpus and map them to an existing knowledge base. The third is a Description Generation method that generates a sentence to describe a target entity from its properties in a knowledge base.

About the speaker:

Dr. Jianzhong Qi is a senior lecturer in the School of Computing and Information Systems at The University of Melbourne. He obtained his PhD degree from The University of Melbourne in 2014. He has been an intern at Microsoft Redmond in 2014 and a visiting scholar at Northwestern University in 2017. Jianzhong Qi publishes in leading venues in database management and artificial intelligence such as TPAMI, TODS, VLDBJ, ICML, NeurIPS, and PVLDB. He has won the “Best Vision Paper” award in ACM SIGSPATIAL 2017. He is the PC Co-Chair for the Australasian Database Conference 2020. His research interests include spatio-temporal databases and artificial intelligence.