2017/05/16 by Rui Meng, Meng, Rui, Hao Xin +5
Computer Science · #Artificial Intelligence (cs.AI) #Databases (cs.DB) #Domain Adaptation and Few-Shot Learning #Expert finding and Q&A systems #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #cs.AI #cs.DB #cs.HC
paper · pdf · doi:10.48550/arxiv.1705.05720
arxiv created 2017/05/16 · openalex publication_date 2017/05/16 · arxiv updated 2017/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Knowledge bases (KBs) have attracted increasing attention due to its great success in various areas, such as Web and mobile search.Existing KBs are restricted to objective factual knowledge, such as city population or fruit shape, whereas,subjective knowledge, such as big city, which is commonly mentioned in Web and mobile queries, has been neglected. Subjective knowledge differs from objective knowledge in that it has no documented or observed ground truth. Instead, the truth relies on people's dominant opinion. Thus, we can use the crowdsourcing technique to get opinion from the crowd. In our work, we propose a system, called crowdsourced subjective knowledge acquisition (CoSKA),for subjective knowledge acquisition powered by crowdsourcing and existing KBs. The acquired knowledge can be used to enrich existing KBs in the subjective dimension which bridges the gap between existing objective knowledge and subjective queries.The main challenge of CoSKA is the conflict between large scale knowledge facts and limited crowdsourcing resource. To address this challenge, in this work, we define knowledge inference rules and then select the seed knowledge judiciously for crowdsourcing to maximize the inference power under the resource constraint. Our experimental results on real knowledge base and crowdsourcing platform verify the effectiveness of CoSKA system.