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Title: Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management [ppt] [pdf]


Data driven Knowledge Graph is rapidly adapted by different societies. Many open domain and specific domain knowledge graphs have been constructed, and many industries have benefited from knowledge graph. Currently, enterprise related knowledge graph is classified as specific domain, but the applications span from solving a narrow specific problem to Enterprise Knowledge Management system. With the digital transform of traditional industry, Enterprise knowledge becomes more and more complicated, it involves knowledge from common domain, multiple specific domains, and corporate-specific in general. This tutorial provides an overview of current Enterprise Knowledge Graph(EKG). It distinguishes the EKG from specific domain according to the knowledge it covers, and provides the examples to illustrate the difference between EKG and specific domain KG. The tutorial further summarizes EKG into three types: Specific Business Task Enterprise KG, Specific Business Unit Enterprise KG and Cross Business Unit Enterprise KG, and illustrates the characteristics, steps, challenges, and future research in constructing and consuming of each of these three types of EKG.


Part 1. Enterprise Knowledge and Enterprise Knowledge Graph (60 minutes)

There are three types of EKG: Specific Business Task EKG, Specific Business Unit EKG and Cross Business Unit EKG. This section discusses the characteristics of these three types of EKG, and challenges to construct different types of EKG through examples.

Breaks 15 minutes

Part 2. Construction of Enterprise Knowledge Graph (60 minutes)

The section provides review of EKG construction techniques, which includes entity recognition, relation extraction, and KG refinement.

Part 3. Challenges and future research in Enterprise Knowledge Graph (45 minutes).

The last section will discuss the challenges and Potential future research directions in EKG field.



Dr. Rong Duan currently is Chief Data Scientist of Corporate Data Management Department, Huawei Technologies Co.,Ltd. She is guiding a group to build various AI applications in supporting internal users. Multiple enterprise knowledge graphs have been constructed under her supervision. Before joined Huawei, Rong was principal inventive scientist at AT&T Labs, big data research and adjunct professor at Stevens Institute of Technology. Has been working in AT&T Labs for more than 20 years, Rong has extensive experience on statistical learning, data mining, predictive modeling and data analysis for business data. Rong received her PhD and MSc in computer engineering and computer science respectively from Stevens Institute of Technology, US. Her research interests include data mining, statistical learning theory and methods, Spatial-temporal Risk Assessment and management , Data Integration and Data Quality Assessment. Rong was former Chair for the Data Mining Section of INFORMS, and Data Mining cluster co-chair for INFORMS International Beijing. Rong also served as a program co-chair for the 1st and the 2nd International Symposium on System Informatics and Engineering,Panelist for ICDM, tutor for DSAA, DASFAA, INFORMS, etc.


Dr. Yanghua Xiao got his Ph.D. degree from Fudan University, Shanghai, China, in 2009. He now is a full professor of the School of Computer Science at Fudan University. His research interest includes big data management and mining, graph database,knowledge graph. He was a visiting professor of Human Genome Sequencing Center at Baylor College Medicine, and visiting researcher of Microsoft Research Asia and Alibaba. He won 10+ research awards granted by governments or industries, including CCF Natural Science Award (second level), ACM(CCF) Shanghai distinguished young scientists and Alibaba Research Fellowship Award. Recently, he has published 100+ papers in international leading journals and top conferences, including TKDE, SIGMOD,VLDB, ICDE, IJCAI, AAAI. He is the PI or Co-PI of 30+ projects supported by 10+ national and local funding agency and big companies including Microsoft, IBM,HUAWEI, China Telecom, China Mobile, Baidu, XiaoI Robot etc. He regularly serves as the reviewer of 10+ national and local funding agencies and SPC or PC members of 50+ top conferences including IJCAI, AAAI, SIGKDD, ICDE, WWW, CIKM, ICDM,SDM etc. He is the Associate Editor of Frontier of Computer Science, and reviewers of 20+ leading journals. He is a member of ACM, IEEE, AAAI and senior member of CCF.He is the director of Knowledge Works4 Research Laboratory at Fudan University. His team at Fudan published a lot of Chinese knowledge graphs, which serve industries with 1 billion of API calls. He is the chief scientist or senior advisors of many top Chinese big data companies or AI companies. He has ever given more than 10 keynote speeches or tutorials in international leading conference including SIGMOD2012, IDEAL2017, ADMA2018, PIC2017, CCKS2018 etc.

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