HOFD: An Outdated Fact Detector for Knowledge Bases

Shuang Hao, Chengliang Chai*, Guoliang Li, Nan Tang, Ning Wang, Xiang Yu

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose HOFD, a novel human-in-the-loop approach for outdated fact detection in KBs. HOFD trains a binary classifier using features such as historical update frequency and update time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, HOFD interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, HOFD also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for data augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.

源语言英语
页(从-至)10775-10789
页数15
期刊IEEE Transactions on Knowledge and Data Engineering
35
10
DOI
出版状态已出版 - 1 10月 2023

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引用此

Hao, S., Chai, C., Li, G., Tang, N., Wang, N., & Yu, X. (2023). HOFD: An Outdated Fact Detector for Knowledge Bases. IEEE Transactions on Knowledge and Data Engineering, 35(10), 10775-10789. https://doi.org/10.1109/TKDE.2023.3248223