Knowledge bases (KBs) are repositories of interconnected facts with an inference engine. Companies are increasingly populating KBs with facts from disparate sources to create a central repository of information to provide users with a richer and more integrated user experience [Herman and Delurey 2013]. Additionally, inference over the constructed KB can produce new facts not specifically mentioned in the KB. Google is now employing KBs to surface additional information for user search [Dong et al. 2014a]. Manually constructed KBs, such as YAGO [Hoffart et al. 2013] and DBpedia [Auer et al. 2007], are increasingly being used as the gold standard and ground truth of newer KBs [Dong et al. 2014b]. However, the growing number of KBs inside an organization require a sufficiently high level of quality and must be meticulously maintained. Both YAGO and DBPedia were constructed based on data from Wikipedia. Within Wikipedia, the medium lag between the occurrence of a notable event and the addition of the event was measured at 356 days [Frank et al. 2012]. This fact spurred many efforts to discover methods to automatically build, extend, and clean KBs [Frank et al. 2012; Ellis et al. 2012; Ji et al. 2014; Surdeanu and Ji 2014]. In these contests, teams build systems to explore the creation of Web-scale KBs; however, by and large, these contests stop short of designing systems for deployment in a production system. We believe that there are two main questions that are wholly understudied across research communities: in KBs, over time, (1) what stale information needs to be cleaned? and (2) when should this information be updated? In this article, we present a challenge to the information quality community to develop techniques that support the long-term support and maintenance of critical, rapidly growing KBs. We follow this challenge with two notable papers that make strides in this direction. We end this group of papers with a discussion of three research questions in response to this challenge.
Authors:
Christan Grant, Daisy Zhe Wang
Bibtex:
@article{,
author = "Christan Grant, Daisy Zhe Wang",
title = "A Challenge for Long-Term Knowledge Base Maintenance",
journal = "Proceedings of ACM Journal on Data and Information Quality",
year = "2015"
}
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