Cities worldwide are rapidly adopting smart technologies, transforming urban
life. Despite this trend, a universally accepted definition of 'smart city'
remains elusive. Past efforts to define it have not yielded a consensus, as
evidenced by the numerous definitions in use. In this paper, we endeavored to
create a new 'compromise' definition that should resonate with most experts
previously involved in defining this concept and aimed to validate one of the
existing definitions. We reviewed 60 definitions of smart cities from industry,
academia, and various relevant organizations, employing transformer
architecture-based generative AI and semantic text analysis to reach this
compromise. We proposed a semantic similarity measure as an evaluation
technique, which could generally be used to compare different smart city
definitions, assessing their uniqueness or resemblance. Our methodology
employed generative AI to analyze various existing definitions of smart cities,
generating a list of potential new composite definitions. Each of these new
definitions was then tested against the pre-existing individual definitions we
have gathered, using cosine similarity as our metric. This process identified
smart city definitions with the highest average cosine similarity, semantically
positioning them as the closest on average to all the 60 individual definitions
selected.

通过使用基于 Transformer 架构的生成式人工智能和语义文本分析技术，本研究从工业、学术界和相关组织中收集并回顾了 60 个智慧城市的定义，提出了一种语义相似度度量方法，并使用余弦相似度作为评估指标，最终确定了与这 60 个定义中最为相符的综合型智慧城市定义。

使用 Transformer 神经网络来定义智慧城市

On Defining Smart Cities using Transformer Neural Networks

Candidate axiom scoring is the task of assessing the acceptability of a
candidate axiom against the evidence provided by known facts or data. The
ability to score candidate axioms reliably is required for automated schema or
ontology induction, but it can also be valuable for ontology and/or knowledge
graph validation. Accurate axiom scoring heuristics are often computationally
expensive, which is an issue if you wish to use them in iterative search
techniques like level-wise generate-and-test or evolutionary algorithms, which
require scoring a large number of candidate axioms. We address the problem of
developing a predictive model as a substitute for reasoning that predicts the
possibility score of candidate class axioms and is quick enough to be employed
in such situations. We use a semantic similarity measure taken from an
ontology's subsumption structure for this purpose. We show that the approach
provided in this work can accurately learn the possibility scores of candidate
OWL class axioms and that it can do so for a variety of OWL class axioms.

本文提出基于语义相似度的预测模型，用于快速准确地评估候选公理的可接受性，在自动架构归纳和本体验证等领域有着广泛应用。