Authors: Jiaheng Lu, Jianhong Tu, Ju Fan, Tok Wang Ling, Xiaoyong Du, Zhaoan Dong
Tags: 2018, conceptual modeling
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.Read the full paper here: https://link-springer-com.proxy2.hec.ca/chapter/10.1007/978-3-030-01391-2_4
