Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties

Knowledge bases (KBs) contain huge amounts of facts about entities, their properties, and relations between them. They are thus the key asset in any intelligent system for tasks such as structured search and question answering. However, due to dynamics in the real world, properties and relations change over time, and stored knowledge may become outdated. While KB information evolves steadily, there is no information whether or not a KB property might be subject to change with high probability or whether it is likely to be stable. Systems exploiting KB information, however, could benefit a lot if they had access to this kind of information. In this paper, we analyze and predict the stability of KB entries, which allows to accompany entries with stability scores. Our predictive model exploits entity-based features and learns through historic data. A particular challenge to determine stability scores is that KB entries are not only added or modified due to real-world changes but also to reduce the incompleteness of KBs in general. Nevertheless, our evaluation of sample properties demonstrates the effectiveness of our method for predicting the one-year stability of KB properties.