Knowledge Bases (KBs) provide structured representation of the real-world in
the form of extensive collections of facts about real-world entities, their
properties and relationships. They are ubiquitous in large-scale intelligent
systems that exploit structured information such as in tasks like structured
search, question answering and reasoning, and hence their data quality becomes
paramount. The inevitability of change in the real-world, brings us to a
central property of KBs -- they are highly dynamic in that the information they
contain are constantly subject to change. In other words, KBs are unstable.
In this paper, we investigate the notion of KB stability, specifically, the
problem of KBs changing due to real-world change. Some entity-property-pairs do
not undergo change in reality anymore (e.g., Einstein-children or
Tesla-founders), while others might well change in the future (e.g.,
Tesla-board member or Ronaldo-occupation as of 2022). This notion of real-world
grounded change is different from other changes that affect the data only,
notably correction and delayed insertion, which have received attention in data
cleaning, vandalism detection, and completeness estimation already. To analyze
KB stability, we proceed in three steps. (1) We present heuristics to delineate
changes due to world evolution from delayed completions and corrections, and
use these to study the real-world evolution behaviour of diverse Wikidata
domains, finding a high skew in terms of properties. (2) We evaluate heuristics
to identify entities and properties likely to not change due to real-world
change, and filter inherently stable entities and properties. (3) We evaluate
the possibility of predicting stability post-hoc, specifically predicting
change in a property of an entity, finding that this is possible with up to 83%
F1 score, on a balanced binary stability prediction task.

本研究探讨了知识库的稳定性与真实世界的演变之间的关系，提出了一些启发式规则以预测实体属性对未来的稳定性和可靠性，并在 Wikidata 领域进行了实证研究。