In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.
The process of modeling a city is complex. The intrinsic complexity of interactions between city entities make it very difficult to map relevant sets of dynamic aspects that are often used to characterize a city. Moreover, these entity interactions, along with the numerous entities and processes, differ from one city to another. Thus, the process of modeling the city is typically use-case centered, where the modeling is performed towards a specified goal. This approach, hence, streamlines the process, identifying which characteristics need to be modeled. The work in @cite_1 proposes a core conceptual model for the Domain Knowledge Model of a Smart City, which originally involves multiple domains and cities. The proposed work aims to support cross-domain and cross-city interoperability by specifying terms from different stakeholders. Ontologies play a big role in enabling cross-city comparison. The Semantic Web has been used in the Open Government Data (OGD) approach to make it possible for cities to share information and knowledge under a common vocabulary. Pushing this further, the GCI (Global City Indicators) Ontology @cite_7 is an effort for the modeling of city entities that covers the concepts used by global indicators using Semantic Web technologies.
@cite_1 @cite_7
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As the emergence of the requirement of building Smart Cities across many countries, the Domain Knowledge Model of Smart City (Short as DKM4SC) becomes necessary. The Domain Knowledge Model of Smart City has abundant contents and involves many domains and cities. In order to support cross-domain and cross-city interoperation of knowledge, we should concise the common concepts and their relationship from domains and cities and construct a core concept model (short as CCM of DKM4SC) that can specify terms from different stakeholders, support semantic understanding and give standard knowledge expression. The model is proposed to represent the core concept of Smart City for exchanging data, service. A case study is presented to illustrate the usage of the model. Abstract This paper addresses the problem of how city indicators and their supporting data are to be published on the Semantic Web so that automated analysis can be performed. With the publishing of ISO 37120, cities have a standard set of indicators that can be used to compare their performance. The problem is that no standards exist for publishing indicators on the Semantic Web. In this paper we introduce the Global City Indicator Ontology (GCIO). The GCIO addresses five issues: 1) how is meta-data associated with a single indicator value represented? 2) how are indicator definitions represented? 3) how is the data used to derive an indicator value represented? 4) how is indicator theme specific knowledge represented, and 5) how is city specific knowledge represented? The GCIO has been implemented and validated using the City of Toronto ISO 37120 indicators reported for 2013. Research continues in developing ontologies specifically for each of the indicator themes such as: Education, Shelter, Health, Transportation and Innovation. We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (, 2013) and TransE (, 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2 vs. 54.7 by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning. Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (, 2013) and TransE (, 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2 vs. 54.7 by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning.