Details of Research Outputs

Knowledge base construction (KBC) has become a hot and in-time topic recently with the increasing application need of large-scale knowledge bases (KBs), such as semantic search, QA systems, etc. Existing KBs mainly focus on encoding the factual facts of the world, e.g., city area and company product, which are regarded as the objective knowledge, whereas the subjective knowledge, which is frequently mentioned in Web queries, has been neglected. The subjective knowledge has no documented ground truth, instead, the truth relies on people's dominant opinion, which can be solicited from online crowd workers. In our work, we propose a KBC framework for subjective knowledge base construction taking advantage of the knowledge from the crowd and existing KBs. We develop a two-staged framework for subjective KB construction which consists of core subjective KB construction and subjective KB enrichment. Firstly, we try to build a core subjective KB mined from existing KBs, where every instance has rich objective properties. Then, we populate the core subjective KB with instances extracted from existing KBs, in which the crowd is leverage to annotate the subjective property of the instances. In order to optimize the crowd annotation process, we formulate the problem of subjective KB enrichment procedure as a cost-aware instance annotation problem and propose two instance annotation algorithms, i.e., adaptive instance annotation and batch-mode instance annotation algorithms. We evaluate our framework on real knowledge bases and a real crowdsourcing platform, the experimental results show that we can derive high quality subjective knowledge facts from existing KBs and crowdsourcing techniques through our proposed framework.