Justifying Citizens' Observations and Concerns with Digital Twins for Smart Cities

大都市是一个复杂的系统，其中人为因素大大超过自动化和IT组件。复杂的系统意味着复杂的相互作用，具体的建筑或经济政策的影响难以预测。市区重建工程对同一城市的不同地区可能产生不同的影响，这可能是由于社会构成、邻近地区的历史或后勤和建筑上的不可能性。因此，以最公平的方式确保所有公民的福祉至关重要。确保这一点的一种方法是利用关于城市内事件的在线讨论（例如X以前的Twitter），并将其与从智能城市收集的黄金数据（例如交通信息，天气传感器）或可信的信息来源（例如警察报告，报纸）进行比较。为此，我们设想通过实时数据连接在真实的城市中不断通知城市数字孪生（CDT）。然而，以前的文献没有考虑到CDT的文本数据，我们将在这个项目。我们设想使用确定性和可解释的逻辑驱动的AI来确定和激励决策的结果。如果没有提供足够的背景，我们可能很难识别实体。例如，“纽卡斯尔”可以指三个地方之一：“泰恩河畔的纽卡斯尔”、“莱姆河畔的纽卡斯尔”和“澳大利亚的纽卡斯尔”。因此，我们必须确保全文有足够的上下文来进行分析。然后，这将需要使用常识网络，用于在确定用户所提及的兴趣的同时，在定义特定的感兴趣上下文时激励特定的重建。黄金数据将进一步缩小一套可能的解释的兴趣，以有关的纽卡斯尔泰恩具体。为此，我们需要确定两个或多个文本之间的相似性，这不能直接利用现有的矢量语义工具。在涉及基于学习的方法时存在局限性，例如，与否定作斗争，这意味着必须为给定的神经网络（NN）提供嵌入以确定两个句子是否相似。以下是不同的否定的例子，同一个实体作为主语：1。“在纽卡斯尔市中心没有交通“2.“纽卡斯尔没有交通“3.“有交通在纽卡斯尔，但不是在市中心“看上面的列表; 1.说没有任何交通特别是在市中心，但没有澄清是否有交通其他地方，2。说在纽卡斯尔任何地方都没有交通，3.说除了市中心到处都是车我们可以很容易地破译在这些例子中否定发生的位置，但AI正在努力解决这个问题。通过使用依赖图，我们可以通过精确地确定否定所指的内容来解决这个问题，然后应用重写规则来生成文本的声明性和机器可读的解释。最重要的是，来自在线讨论的噪音投诉可以与交通数据（我们的黄金数据）进行比较，以时间序列表示，以查看在线帖子的时间是否与我们的黄金数据中的交通拥堵时间相匹配，从而反映不同观察方法之间的差异。对这些差异的评估是无法学习的，因为主题可能会随着时间的推移而变化。基于信息检索的查询排序（QA）依赖于网络上或科学论文集中的大量文本，神经阅读理解算法读取这些文本以直接从给定文本中得出答案。基于知识的QA反而构建了查询的语义表示，例如将给定的问题映射到逻辑表示，这是广义图语法可以帮助我们实现的。总的来说，我们设想利用这种技术来弥合用户对工具的理解与数据结果之间的差距

Metropolises are complex systems where human factors substantially prevail over automatic and IT components. Complex systems imply complex interactions, where the effect of specific building or economic policies' is difficult to predict. Urban redevelopment works may not have the same impact on different districts of the same city, either because of the social composition, the neighbourhood's history, or the logistical and architectural impossibilities. It is therefore of the utmost importance to ensure all citizens' well-being in the most equitable way.One way to ensure this is to leverage online discussions (e.g. X formerly Twitter) about events within a city and compare them to golden data collected from a smart city (e.g. traffic information, weather sensors) or trusted sources of information (e.g. police reports, newspapers). For this, we envision City Digital Twins (CDTs) being continuously informed in a real city via real-time data connections. However, previous literature does not consider textual data into the CDT, which we will in this project. We envision the usage of deterministic and explainable logic-driven AI for determining and motivating the outcome of a decision. We could struggle with identifying entities if there is not enough context provided. For example, "Newcastle" could refer to one of three places: "Newcastle upon Tyne", "Newcastle under Lyme", and "Newcastle, Australia". So, we must ensure that full-texts have enough context for our analysis. This will then require the usage of CommonSense networks for motivating a specific reconstruction upon the definition of a specific context of interest, while determining the interest referred by the user. Golden data will further narrow the set of possible interpretations of interest to the ones related to Newcastle upon Tyne specifically. For this, we need to determine the similarity between two or multiple texts, where this cannot directly exploit existing vector semantic tools. There exist limitations when involving a learning-based approach, for example, struggles with negation, meaning embedding must be provided for a given neural network (NN) to determine if two sentences are similar or not. Provided below are examples of different negations with the same entity as the subject:1. "There is no traffic in Newcastle city centre"2. "There is no traffic in Newcastle"3. "There is traffic in Newcastle but not in the city centre"Looking at the list above; 1. says there is not any traffic specifically in the city centre but does not clarify if there is traffic anywhere else, 2. says there is not traffic anywhere in Newcastle, and 3. says there is traffic everywhere but the city centre. We can easily decipher where the negation is occurring in these examples but AI struggles with this problem. By using dependency graphs, we can tackle this problem by precisely determining what the negation is referring to, then apply rewriting rules for generating a declarative and machine-readable interpretation of the text. On top of this, noise complaints from online discussions could be compared to traffic data (our golden data), represented as a time series, to see if the time of the online post matches the time of the traffic congestion from our golden data, thus reflecting discrepancy between different means of observation. The assessment of these discrepancies cannot be learned, as topics might vary with time. Information Retrieval-based Query Answering (QA) relies on the vast amount of text on the web or in collections of scientific papers, which neural reading comprehension algorithms read to draw an answer directly from a given text. Knowledge-based QA instead builds a semantic representation of the query, such as mapping a given question to the logical representation, which generalised graph grammars can help us achieve. Overall, we envision exploiting such technology for bridging the gap between the user's understanding of the tool and the outcome of the data