Fact Discovery from Knowledge Base via Facet Decomposition
Refereed conference paper presented and published in conference proceedings


摘要During the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and relation prediction to help build the representation of KBs. However, there are some issues unsettled towards enriching the KBs. Knowledge base completion and relation prediction assume that we know two elements of the fact triples and we are going to predict the missing one. This assumption is too restricted in practice and prevents it from discovering new facts directly. To address this issue, we propose a new task, namely, fact discovery from knowledge base. This task only requires that we know the head entity and the goal is to discover facts associated with the head entity. To tackle this new problem, we propose a novel framework that decomposes the discovery problem into several facet discovery components. We also propose a novel auto-encoder based facet component to estimate some facets of the fact. Besides, we propose a feedback learning component to share the information between each facet. We evaluate our framework using a benchmark dataset and the experimental results show that our framework achieves promising results. We also conduct extensive analysis of our framework in discovering different kinds of facts. The source code of this paper can be obtained from this https://github.com/thunlp/FFD
著者Zihao Fu, Yankai Lin, Zhiyuan Liu, Wai Lam
會議名稱The Annual Conference of the North American Chapter of the Association for Computational Linguistics
會議論文集題名Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics
頁次2892 - 2901

上次更新時間 2019-28-11 於 12:09