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

大都市是复杂的系统，其中人为因素大大超过了自动和IT组件。复杂的系统意味着复杂的相互作用，其中具体的建筑或经济政策的影响是难以预测的。城市重建工程可能对同一城市的不同地区产生不同的影响，这可能是由于社会构成、社区历史或后勤和建筑的不可能性。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在线讨论（例如X以前是Twitter），并将其与从智能城市（例如交通信息，天气传感器）或可信信息来源（例如警察报告，报纸）收集的黄金数据进行比较。为此，我们设想城市数字双胞胎（cdt）通过实时数据连接在真实城市中不断获得信息。然而，以前的文献没有将文本数据考虑到CDT中，我们将在本项目中考虑到这一点。我们设想使用确定性和可解释的逻辑驱动的人工智能来确定和激励决策的结果。如果没有提供足够的上下文，我们可能难以识别实体。例如，“纽卡斯尔”可以指三个地方之一：“泰恩河畔的纽卡斯尔”、“莱姆河下的纽卡斯尔”和“澳大利亚的纽卡斯尔”。因此，我们必须确保全文为我们的分析提供足够的背景。这就需要使用常识网络，根据特定兴趣上下文的定义来激发特定重建，同时确定用户引用的兴趣。黄金数据将进一步缩小兴趣的可能解释集，特别是与泰恩河畔纽卡斯尔有关的解释。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向量语义工具。当涉及到基于学习的方法时，存在局限性，例如，与否定作斗争，必须为给定的神经网络（NN）提供意义嵌入来确定两个句子是否相似。下面是用同一实体作为主语的不同否定的例子：纽卡斯尔市中心没有交通。“纽卡斯尔没有交通。”“纽卡斯尔有交通，但市中心没有。”看看上面的列表；1. 说市中心没有特别的交通，但没有说明其他地方是否有交通。说纽卡斯尔任何地方都没有交通；说除了市中心到处都是交通堵塞。在这些例子中，我们可以很容易地解读出否定在哪里发生，但人工智能却很难解决这个问题。通过使用依赖关系图，我们可以通过精确地确定否定指的是什么来解决这个问题，然后应用重写规则来生成声明性和机器可读的文本解释。除此之外，我们还可以将网络讨论的噪音投诉与以时间序列表示的交通数据（我们的黄金数据）进行比较，看看网络帖子的时间是否与黄金数据中交通拥堵的时间相匹配，从而反映出不同观察手段之间的差异。对这些差异的评估是无法学习的，因为主题可能随时间而变化。基于信息检索的查询回答（Information Retrieval-based Query answer，简称QA）依赖于网络上或科学论文集合中的大量文本，神经阅读理解算法通过阅读这些文本直接从给定文本中得出答案。相反，基于知识的QA构建查询的语义表示，例如将给定的问题映射到逻辑表示，这可以帮助我们实现广义图语法。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和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