Knowledge reuse can increase the quality and efficiency of different activities, as well as reduce risks. The digital transformation of a city is a complex task that requires new approaches and management technologies. Knowledge reuse can reinforce city digital transformation initiatives, e.g. data integration and modeling activities can be improved via ontology reuse, design and development of digital solutions can be enhanced via reference architectures and reuse of existing methods and systems. Such reuse can speed up the work of city development managers, digital architects, and other ICT specialists, as well as decrease costs and risks. Although there are platforms for knowledge sharing and reuse within the smart city domain, they apply a traditional document-oriented approach to knowledge representation. This limits knowledge integration and the creation of intelligent services. So, knowledge graph technology was selected for collecting and curating digital city planning reusable content. The Open Research Knowledge Graph (ORKG) provided an opportunity for piloting this approach. The current paper will overview the digital city planning observatory within the ORKG, including main knowledge representation mechanisms and representative content examples, and provide the link between content and application scenarios.
People and organizations can reuse previous knowledge when encountering new tasks and challenges.
They can either take and apply documented knowledge or ask for advice from someone with relevant
prior experience. Knowledge reuse situations vary: reuse by shared knowledge producers, reuse by
shared work practitioners, reuse by expertise-seeking novices, and reuse by secondary knowledge
miners [
Number of tasks, stakeholders, problem-solving ways, and approaches is growing, Life speed is increasing: people need the newest innovative technologies and applications, New digital services for citizens and businesses are required.
Knowledge reuse can improve many activities (areas) within a city’s digital transformation:
Data integration and modeling activities can be improved via the reuse of data models and ontologies,
2020 Copyright for this paper by its authors. 2. Design and development of city digital solutions and systems can be improved via reference architectures and reuse of existing solutions/systems/methods, 3. (Re-)Design of public services and processes can be improved via the reuse of patterns and reference process models, 4. Smart city management and development can be improved by reusing proven key performance indicators from existing reference models and standards or by reusing existing cutting-edge solutions for city analysis, monitoring, and control (including city digital twins).
Some initiatives and platforms support knowledge transfer and reuse in the smart city domain (see section 2). But these initiatives and platforms use traditional document-oriented formats for knowledge representations, so knowledge integration, access, and reuse are limited. It is also hard to develop intelligent services using such knowledge representation. So there is a need for semantic representation of reusable knowledge in the smart city domain.
There are several existing solutions and platforms for knowledge sharing and reuse in the smart city domain including platforms with a broad scope, domain-specific platforms, and platforms for the specific type of content.
1. Broad scope platforms with smart city solutions: • Bee smart city platform (https://www.beesmart.city/) is a database of smart city cases and solutions that is extended with elements of a social network of smart city experts. This platform also collects information about tenders and is supported by matchmaking and consulting services. • Bable (https://www.bable-smartcities.eu/) offers information on how to implement smart city solutions and includes a library of use cases, solutions, and products. It also provides the opportunity for communication between companies and the city’s administration. 2. Domain-specific platforms: • Eltis – The Urban Mobility Observatory (https://www.eltis.org/) is an EU-funded platform that consists of tools, information, communication channels, and best practices to help transport planners to create sustainable mobility. 3. Platforms for the specific type of content: • Smartcity.linkeddata.es – a repository of ontologies about smart cities, energy, and other related fields. The repository has a list of ontologies with their attributes: availability, format, type of licenses, syntaxis, using language, domain, and link. Even though it is a unique example of this type of ontology library, the biggest problem is it has not been updated since 2015.
Although these platforms include well-structured libraries of materials, they use the "traditional" approach for knowledge representation: text documents with metadata. Such an approach has limitations in terms of knowledge access, reuse, and applicability for creating intelligent services. Smart city best practices and cases can be accumulated via knowledge graphs in order to make them more findable and reusable.
Also attempts to create a city knowledge graph were made several times all over the world. An example,
a Zaragoza’s Knowledge Graph [
Some examples of information/knowledge requirements within city digital transformation activities, which can be supported by knowledge reuse, are presented in table 1. What are the examples of digital solutions/systems for improving a specific domain or solving a particular problem? Are there any reusable ones? What are the results and outcomes of a solution/system application in smart cities? What methods and approaches are used within the particular solution? What digital services can be offered to a specific category of citizens or businesses within a particular context (event)? How can services be delivered and implemented? How specific processes can be organized and/or transformed using available technologies? What indicators can we use for measuring domain X? What city ranking systems exist, and what are the differences between them? What indicators can we use for measuring outcomes, outputs, processes, and inputs in domain X? How to integrate and present city-related data for decision-making?
How city digital twins can be used for city management and development? These information/knowledge requirements arise among city development managers, their digital transformation teams, and/or solution providers.
Digital City Planner company aims to support city digital transformation via knowledge reuse and application of knowledge graphs and semantic technologies. This initiative may help city development managers do the following things: 1. Have easy access to the world’s best practices and experience in the smart city area, 2. Minimize hiring top/external consultants, 3. Have a holistic and evidence-based smart city development system, 4. Collect, prepare and pack knowledge for reuse and worldwide replication.
There are a lot of reference and reusable content in smart city domain, such as reference models,
ontologies, data models, classifications/taxonomies (e.g. of processes or services), patterns, good
practice cases, reusable methods, and systems. This content can be integrated and prepared for reuse
both by practitioners and researchers. From an industry point of view, such an effort can be considered
as a step toward the digital transformation of IT consulting services [
We decided to make a pilot project using publicly available research papers as a source of reusable
knowledge for city digital transformation. So despite the existence of different platforms for managing
knowledge graphs, we selected the Open Research Knowledge Graph (ORKG) platform provided by
the TIB Leibniz Information Centre for Science and Technology [
So our pilot project for creating a library of reusable knowledge resulted in the curation of the digital
city planning observatory within the ORKG [
We took examples of city digital transformation information/knowledge requirements as input,
searched information via Google Scholar and Scopus, collected relevant research papers (primarily
reviews and comparisons), and transformed them into the ORKG format. This format includes a
structured description of the paper’s contributions, comparisons, and reviews (for more details about
the representation format see the ORKG documentation [
In the ORKG, knowledge that is traditionally described in scholarly articles is described semantically, i.e. machine-readable. Research papers are added to the ORKG, and their contributions are described in a structured way. Typically, a research contribution describes the addressed problem, the utilized materials and methods, and the obtained result.
Our digital city planning observatory within the ORKG describes 296 research papers (for 30.07.2022).
Figure 1 demonstrates two examples of structured descriptions of research papers.
Part A of Figure 2 describes the KM4City ontology (a contribution of the paper [
Every paper has a mandatory property: research problem. In addition, this property helps to categorize
papers inside of the ORKG system. All other properties were chosen based on the aim of our final
research. We have added properties “ontology name” and “type” to describe ontology and categorize it
for feature comparison. It was important to highlight which of the existing ontologies have been reused
to create current ontologies and link them with their articles (if there is one in the ORKG system).
Part B of Figure 2 describes a building permit recommender system for smart cities (a contribution of
the paper [
As it was mentioned before, this article has a mandatory property research problem. Other properties mainly classify the suggested recommender systems: by the smart city dimension, smart city action, and smart city goal. Because the article includes use cases, we have added properties such as country, city, and application scope to identify use case characteristics.
https://orkg.org/paper/R138642/R138645
A) provides a structured description of a paper,
which suggests smart city ontology [
https://orkg.org/paper/R138187/R138189
B) provides a structured description of a paper,
which suggests a recommender system for smart
cities [
Fig. 2. Structured description of research contributions within the DCP observatory
Comparisons are the main element of the ORKG platform, which integrates the contributions of
individual papers and gives an overview of any topic. The ORKG documentation says that
“comparisons are the core type of ORKG content and give a condensed overview on the
state-of-theart for a particular research question. Contributions towards the problem are organized in a tabular view
and can be compared and filtered along different properties.” [
The Digital city planning observatory within the ORKG includes 32 comparisons (for 30.07.2022).
Figure 3 demonstrates a fragment of comparison “Ontologies for smart city: levels and key classes”,
which compares the content of 6 smart city ontologies described in the corresponding research papers
[
Reviews is the most integrative element of the ORKG. They can integrate one or more comparisons, a
description of the properties used in the comparison, visualizations and text blocks including an
introduction, conclusions, and links between the embedded elements. So, they give an opportunity to
create a holistic overview of a specific domain. “Reviews are a novel ORKG-based method to create
review or survey articles for giving an overview on research addressing a particular research question.”
[
Totally the Digital City Planning (DCP) observatory within the Open Research Knowledge Graph (ORKG) includes 296 papers, 32 comparisons and 3 reviews.
Table 2 gives an overview of reviews and comparisons within the DCP observatory and shows how they can support various city digital transformation activities and corresponding information/knowledge requirements from Table 1.
The following ORKG tools were actively used during the curation work:
Templates – they provide structure to comparisons and help to simplify entering contributions (for more
details see [
Classes and resources (instances) – they helped us minimize duplicates in descriptions of papers (contributions) and comparisons. 4.5.
Our pilot project and the resultant observatory within the ORKG can support city digital transformation activities via two additional knowledge services: search and visual analytics.
Figure 5 demonstrates the application of property-based filters for searching relevant AI systems (recommender systems) within the comparison Recommender systems for smart cities, Smart governance dimension [19]. Properties for the current comparison were chosen based on the original research made by Lara Quijano-Sánchez et al [26].
The following filters are applied: • Addresses smart city action = Citizen participation and inclusion AND • Recommended items = Political candidates AND • Application domain = Smart governance (restricted by the scope of the comparison). This filtering results in the following two contributions: 1. A Recommender System with Uncertainty on the Example of Political Elections 2. A Fuzzy Recommender System for eElections And what is especially important is that these two contributions are presented in a structured way, which enables one to get answers to many questions without reading the papers. For example, we see the recommendation approach and methods which were used within the suggested contributions. We also see that one paper suggested a prototype and another one – an algorithm.
Although similar attributive or faceted search is provided by existing smart city knowledge sharing platforms, their filtering properties are very limited and associated with a predefined document e.g. case description.
The content of the comparisons within the ORKG can be also represented in the visual format. Figure 6 demonstrates the visualization of comparison results – several standardised sets of reference indicators for Smart Sustainable cities are compared in terms of the types of indicators they have, whether they mostly measure Input, Process, Output, Outcome or Impact aspects of the city development and transformation activities [21].
The standardised sets of reference indicators for Smart Sustainable cities are mostly reflected in international standards, which can be considered as the next step of maturity of research results. The suggested visualization helps to identify standards which include, for example, a lot of Impact indicators – ISO 37120, ITU 4902, UN SDG 11+.
Then another comparison can be used to get access to all these impact indicators – see figure 7 [22].
Fig. 6. Visualization of the comparison content
The suggested Digital City Planning observatory within the ORKG can be considered as a MVP for the knowledge-based product for city digital transformation.
Next steps can be associated with two sides: knowledge consumers and knowledge producers.
We envision consumption of reusable knowledge by city development managers and their teams within different city digital transformation activities, such as design and development of city digital solutions and systems, data integration and modeling, (re-)design of public services and processes. Semantic knowledge representation enables the infrastructure for creating knowledge services for users, for example, recommendations and/or reasoning. Also, reusable smart city knowledge in the form of knowledge graph fragments can be used as building blocks for ontology-based city modeling and creating city digital twins. The ideas of knowledge graph application for these purposes are already elaborated for organizations in general [27, 28]. In order to establish effective consumption of reusable knowledge, additional marketing research and customer development should be done: clarify jobs to be done and pains of potential users, specify information requirements and possible use cases. Such market understanding will enable the creation of demanded value proposition.
The production of reusable knowledge – the development of reference city knowledge graph should also be modified. Nickel et al. [29] divided knowledge graph (KG) construction methods into four groups: (1) curated approaches, i.e., KG created manually by a closed group of experts, (2) collaborative approaches, i.e., KG created manually by an open group of volunteers, (3) automated semi-structured approaches, i.e., KG extracted automatically from semi-structured text via handcrafted rules, and (4) automated unstructured approaches, i.e., KG are extracted automatically from unstructured text. So it is necessary to move from the current curated approach (1) to a collaborative one (2) with some elements of automation (3 and/or 4). We envision the involvement of knowledge providers in this collaborative effort since reusable knowledge for city digital transformation is created by research institutes, providers of city digital solutions, universities, lighthouse cities, and associations; all these parties can participate in the knowledge graph creation. So there is a need to establish the ecosystem around the reference knowledge graph, which integrates reusable knowledge.
This approach is represented in figure 8.
City digital transformation can be enhanced via knowledge reuse. The city development managers and their teams may do their work faster, cheaper and with low risks by reusing best practice cases, reference models, patterns (task patterns, ontology patterns, workflow patterns, etc.), and other “knowledge building blocks”. Although there are several platforms for smart city knowledge sharing and reuse, they apply a traditional document-oriented approach to knowledge representation. Such an approach has limitations in terms of knowledge access, reuse, and applicability for creating intelligent services. So, knowledge graphs technology was selected for collecting and curating digital city planning reusable content. We decided to make a pilot project using publicly available research papers as a source of reusable knowledge for city digital transformation. So despite the existence of different platforms for managing knowledge graphs, we selected the Open Research Knowledge Graph (ORKG) platform. So our pilot project for creating a library of reusable knowledge resulted in the curation of the digital city planning observatory within the ORKG. The paper described a representation of smart city reusable knowledge in the ORKG, gave an overview of the digital city planning observatory content, highlighted knowledge curation methods and techniques, and provided current and future application scenarios.
We would like to thank Sören Auer, Lars Vogt, and their colleagues from the ORKG team for their support, advice, and direction. Knowledge curation work within the smart city planning observatory was also partially supported by the ORKG Curation Grant from TIB.
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