Knowledge graph entity typing aims to infer entities' missing types in
knowledge graphs which is an important but under-explored issue. This paper
proposes a novel method for this task by utilizing entities' contextual
information. Specifically, we design two inference mechanisms: i) N2T:
independently use each neighbor of an entity to infer its type; ii) Agg2T:
aggregate the neighbors of an entity to infer its type. Those mechanisms will
produce multiple inference results, and an exponentially weighted pooling
method is used to generate the final inference result. Furthermore, we propose
a novel loss function to alleviate the false-negative problem during training.
Experiments on two real-world KGs demonstrate the effectiveness of our method.
The source code and data of this paper can be obtained from
this https URL

本研究提出了一种利用上下文信息进行知识图谱实体类型推理的新方法，设计了两种推理机制：N2T 和 Agg2T，并提出了一种新的损失函数以减轻训练期间的误测问题。实验证明了该方法的有效性。

知识图谱中基于上下文的实体类型识别

Context-aware Entity Typing in Knowledge Graphs

This paper explores the role of Directed Acyclic Graphs (DAGs) as a
representation of conditional independence relationships. We show that DAGs
offer polynomially sound and complete inference mechanisms for inferring
conditional independence relationships from a given causal set of such
relationships. As a consequence, d-separation, a graphical criterion for
identifying independencies in a DAG, is shown to uncover more valid
independencies then any other criterion. In addition, we employ the Armstrong
property of conditional independence to show that the dependence relationships
displayed by a DAG are inherently consistent, i.e. for every DAG D there exists
some probability distribution P that embodies all the conditional
independencies displayed in D and none other.

本研究研究了有向无环图在表示条件独立关系方面的作用，提出 DAG 可以用来推断条件独立关系并可以比其他准则发现更多合法的独立关系。此外，研究还表明 DAG 所显示的依赖关系是相一致的。

因果模型的逻辑

On the Logic of Causal Models

Today, many different probabilistic programming languages exist and even more
inference mechanisms for these languages. Still, most logic programming based
languages use backward reasoning based on SLD resolution for inference. While
these methods are typically computationally efficient, they often can neither
handle infinite and/or continuous distributions, nor evidence. To overcome
these limitations, we introduce distributional clauses, a variation and
extension of Sato's distribution semantics. We also contribute a novel
approximate inference method that integrates forward reasoning with importance
sampling, a well-known technique for probabilistic inference. To achieve
efficiency, we integrate two logic programming techniques to direct forward
sampling. Magic sets are used to focus on relevant parts of the program, while
the integration of backward reasoning allows one to identify and avoid regions
of the sample space that are inconsistent with the evidence.

本论文提出了一种基于分布句式的逻辑推理方法，并结合前向推理和重要性抽样技术，采用魔法集和后向推理技术来实现高效的概率推理。