Zhao, Junfeng is an research professor in the Software Institute, School of Computer Science, Peking University. She obtained her B.Sc. from Xi'an Polytechnic University in 1996, M.S. from Xi'an Jiaotong University in 1999 and Ph.D. from Peking University in 2015 respectively. Her research interests include Big Data Analysis, Software Engineering and Knowledge Engineering，Software Reuse and Component Technology.
Dr. Zhao has published more than 40 research papers, and most of them are published in High rank conferences, such as AAAI, ICSR, and ICDM. She has presided 6 national technical research projects including NSFC, 863 project, etc. She also took in charge of 4 Provincial and ministerial level projects. She was awarded Second prize of national science and technology progress award (Rank Fifth) in 2006 and Second prize of Beijing science and technology progress award (Rank Fifth). She is the Secretary General of “Big Data Techniques Standardization Group” and “SOA Key Techniques and Middleware Standardization Working Group” of China national information Technology Standardization. She has attended 5 International standards work and was awarded advanced individuals in standardization work of in 2014.
Dr. Zhao’s research achievements are summarized as follows:
1) Software Big Data Driven Software Knowledge Graph Construction and Application: To develop a crawler system to collect various software data resources from the Internet incrementally. A unified graph based meta model was used to define data oracles for different data resources. An automated software traceability link recovering techniques was investigated to discover cross-resource relations. Some data mining and machine learning techniques were adopted in the entity/relation-rich and easy-access data graph to discovery insight software knowledge for software practitioners.
2) Data-driven Knowledge Modeling and Management in Smart Cites: My research is focused on set up domain knowledge models to model the knowledge about core objects in cities, such as people, environments, social organizations, physical entities and information entities. In order to realize knowledge self-accumulation and self-evolvement, an open mechanism was investigated for the construction and evolution of the domain knowledge models to support smart city knowledge acquisition based on both experts’ collaboration and/or active searching.
