TY - GEN
T1 - Challenges in Chinese knowledge graph construction
AU - Wang, Chengyu
AU - Gao, Ming
AU - He, Xiaofeng
AU - Zhang, Rong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/19
Y1 - 2015/6/19
N2 - The automatic construction of large-scale knowledge graphs has received much attention from both academia and industry in the past few years. Notable knowledge graph systems include Google Knowledge Graph, DBPedia, YAGO, NELL, Probase and many others. Knowledge graph organizes the information in a structured way by explicitly describing the relations among entities. Since entity identification and relation extraction are highly depending on language itself, data sources largely determine the way the data are processed, relations are extracted, and ultimately how knowledge graphs are formed, which deeply involves the analysis of lexicon, syntax and semantics of the content. Currently, much progress has been made for knowledge graphs in English language. In this paper, we discuss the challenges facing Chinese knowledge graph construction because Chinese is significantly different from English in various linguistic perspectives. Specifically, we analyze the challenges from three aspects: data sources, taxonomy derivation and knowledge extraction. We also present our insights in addressing these challenges.
AB - The automatic construction of large-scale knowledge graphs has received much attention from both academia and industry in the past few years. Notable knowledge graph systems include Google Knowledge Graph, DBPedia, YAGO, NELL, Probase and many others. Knowledge graph organizes the information in a structured way by explicitly describing the relations among entities. Since entity identification and relation extraction are highly depending on language itself, data sources largely determine the way the data are processed, relations are extracted, and ultimately how knowledge graphs are formed, which deeply involves the analysis of lexicon, syntax and semantics of the content. Currently, much progress has been made for knowledge graphs in English language. In this paper, we discuss the challenges facing Chinese knowledge graph construction because Chinese is significantly different from English in various linguistic perspectives. Specifically, we analyze the challenges from three aspects: data sources, taxonomy derivation and knowledge extraction. We also present our insights in addressing these challenges.
UR - https://www.scopus.com/pages/publications/84944313968
U2 - 10.1109/ICDEW.2015.7129545
DO - 10.1109/ICDEW.2015.7129545
M3 - 会议稿件
AN - SCOPUS:84944313968
T3 - Proceedings - International Conference on Data Engineering
SP - 59
EP - 61
BT - ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops
PB - IEEE Computer Society
T2 - 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015
Y2 - 13 April 2015 through 17 April 2015
ER -