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。

知识库中的知识有多稳定？

How Stable is Knowledge Base Knowledge?

Given a knowledge base or KB containing (noisy) facts about common nouns or
generics, such as "all trees produce oxygen" or "some animals live in forests",
we consider the problem of inferring additional such facts at a precision
similar to that of the starting KB. Such KBs capture general knowledge about
the world, and are crucial for various applications such as question answering.
Different from commonly studied named entity KBs such as Freebase, generics KBs
involve quantification, have more complex underlying regularities, tend to be
more incomplete, and violate the commonly used locally closed world assumption
(LCWA). We show that existing KB completion methods struggle with this new
task, and present the first approach that is successful. Our results
demonstrate that external information, such as relation schemas and entity
taxonomies, if used appropriately, can be a surprisingly powerful tool in this
setting. First, our simple yet effective knowledge guided tensor factorization
approach achieves state-of-the-art results on two generics KBs (80% precise)
for science, doubling their size at 74%-86% precision. Second, our novel
taxonomy guided, submodular, active learning method for collecting annotations
about rare entities (e.g., oriole, a bird) is 6x more effective at inferring
further new facts about them than multiple active learning baselines.

通过使用外部信息，如关系模式和实体分类法，在基础知识库或 KB 中推断出更多与通用名词或总称有关的事实，这是以前的 KB 补全方法所无法处理的。同时，在收集有关罕见实体的注释方面，我们提出了一种新的方法，可以比多个常规方法更有效地推断出这些实体的信息。

引导张量因子分解的通用知识补全

Knowledge Completion for Generics using Guided Tensor Factorization

This work deals with the problem of combining reactive features, such as the
ability to respond to events and define complex events, with the execution of
transactions over general Knowledge Bases (KBs).
With this as goal, we build on Transaction Logic (TR), a logic precisely
designed to model and execute transactions in KBs defined by arbitrary logic
theories. In it, transactions are written in a logic-programming style, by
combining primitive update operations over a general KB, with the usual logic
programming connectives and some additional connectives e.g. to express
sequence of actions. While TR is a natural choice to deal with transactions, it
remains the question whether TR can be used to express complex events, but also
to deal simultaneously with the detection of complex events and the execution
of transactions. In this paper we show that the former is possible while the
latter is not. For that, we start by illustrating how TR can express complex
events, and in particular, how SNOOP event expressions can be translated in the
logic. Afterwards, we show why TR fails to deal with the two issues together,
and to solve the intended problem propose Transaction Logic with Events, its
syntax, model theory and executional semantics. The achieved solution is a
non-monotonic extension of TR, which guarantees that every complex event
detected in a transaction is necessarily responded.

本文提出了 Transaction Logic with Events，其是 Transaction Logic 的非单调扩展，使得每个在事务中检测到的复杂事件都能得到响应。