Knowledge plays a critical role in artificial intelligence. Recently, the
extensive success of pre-trained language models (PLMs) has raised significant
attention about how knowledge can be acquired, maintained, updated and used by
language models. Despite the enormous amount of related studies, there still
lacks a unified view of how knowledge circulates within language models
throughout the learning, tuning, and application processes, which may prevent
us from further understanding the connections between current progress or
realizing existing limitations. In this survey, we revisit PLMs as
knowledge-based systems by dividing the life circle of knowledge in PLMs into
five critical periods, and investigating how knowledge circulates when it is
built, maintained and used. To this end, we systematically review existing
studies of each period of the knowledge life cycle, summarize the main
challenges and current limitations, and discuss future directions.

本研究调查了预训练语言模型作为基于知识的系统的生命周期，并系统地审查了知识生命周期的每个阶段的现有研究，总结了主要挑战和当前限制，并讨论了未来的方向。

大语言模型中知识的生命周期：综述

The Life Cycle of Knowledge in Big Language Models: A Survey

Knowledge-based systems have been used to monitor machines and processes in
the real world. In this paper we propose the use of knowledge-based systems to
monitor other AI systems in operation. We motivate and provide a problem
analysis of this novel setting and subsequently propose a framework that allows
for structuring future research related to this setting. Several directions for
further research are also discussed.

本文提出使用基于知识的系统监控运行中的 AI 系统并构建了一个框架来规划与此相关的研究方向和问题分析。

监控人工智能系统：问题分析、框架和展望

Monitoring AI systems: A Problem Analysis, Framework and Outlook

Research in multi-agent teaming has increased substantially over recent
years, with knowledge-based systems to support teaming processes typically
focused on delivering functional (communicative) solutions for a team to act
meaningfully in response to direction. Enabling humans to effectively interact
and team with a swarm of autonomous cognitive agents is an open research
challenge in Human-Swarm Teaming research, partially due to the focus on
developing the enabling architectures to support these systems. Typically,
bi-directional transparency and shared semantic understanding between agents
has not prioritised a designed mechanism in Human-Swarm Teaming, potentially
limiting how a human and a swarm team can share understanding and
information\textemdash data through concepts and contexts\textemdash to achieve
a goal. To address this, we provide a formal knowledge representation design
that enables the swarm Artificial Intelligence to reason about its environment
and system, ultimately achieving a shared goal. We propose the Ontology for
Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming
between humans and teams through the biologically-inspired approach of
shepherding.

本篇研究提出了 Onto4MAT，这是一种形式化的知识表示设计，通过牧羊的生物学方法，促进了人类和团队之间更有效的协作。

Onto4MAT: 用于广义多智能体团队协作的群体管理本体

Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming

Interpretability of a predictive model is a powerful feature that gains the
trust of users in the correctness of the predictions. In word sense
disambiguation (WSD), knowledge-based systems tend to be much more
interpretable than knowledge-free counterparts as they rely on the wealth of
manually-encoded elements representing word senses, such as hypernyms, usage
examples, and images. We present a WSD system that bridges the gap between
these two so far disconnected groups of methods. Namely, our system, providing
access to several state-of-the-art WSD models, aims to be interpretable as a
knowledge-based system while it remains completely unsupervised and
knowledge-free. The presented tool features a Web interface for all-word
disambiguation of texts that makes the sense predictions human readable by
providing interpretable word sense inventories, sense representations, and
disambiguation results. We provide a public API, enabling seamless integration.

该论文介绍了一种具有可解释性的 WSD 系统，它将基于知识的方法与无监督学习的方法进行了结合，提供 Web 界面和公共 API 以使意义预测对人类可读。