Crowd-Type: A Crowdsourcing-Based Tool for Type Completion in Knowledge Bases

Zhaoan Dong, Jianhong Tu, Ju Fan, Jiaheng Lu, Xiaoyong Du, Tok Wang Ling

Research output: Conference materialsOther conference material


Entity type completion in Knowledge Bases (KBs) is an important and challenging problem. In our recent work, we have proposed a hybrid framework which combines the human intelligence of crowdsourcing with automatic algorithms to address the problem. In this demo, we have implemented the framework in a crowdsourcing-based system, named Crowd-Type, for fine-grained type completion in KBs. In particular, Crowd-Type firstly employs automatic algorithms to select the most representative entities and assigns them to human workers, who will verify the types for assigned entities. Then, the system infers and determines the correct types for all entities utilizing both the results of crowdsourcing and machine-based algorithms. Our system gives a vivid demonstration to show how crowdsourcing significantly improves the performance of automatic type completion algorithms.
Original languageEnglish
Number of pages5
Publication statusPublished - 2018
MoE publication typeNot Eligible
EventInternational Conference on Conceptual Modeling - Xi'an, China
Duration: 22 Oct 201825 Oct 2018
Conference number: 37


ConferenceInternational Conference on Conceptual Modeling
Abbreviated titleER 2018

Fields of Science

  • 113 Computer and information sciences

Cite this

Dong, Z., Tu, J., Fan, J., Lu, J., Du, X., & Ling, T. W. (2018). Crowd-Type: A Crowdsourcing-Based Tool for Type Completion in Knowledge Bases. 17-21. International Conference on Conceptual Modeling , Xi'an, China.