Semantic City Planning Systems (SCPS): A Literature Review

This review focuses on recent research literature on the use of Semantic Web Technologies (SWT) in city planning. The review foregrounds representational, evaluative, projective, and synthetical meta-practices as constituent practices of city planning. We structure our review around these four meta-practices that we consider fundamental to those processes. We find that significant research exists in all four metapractices. Linking across domains by combining various methods of semantic knowledge generation, processing, and management is necessary to bridge gaps between these meta-practices and will enable future Semantic City Planning Systems.

An Integration of Health Monitoring System in Public Transport Using the Semantic Web of Things

The integration of the Internet of Things (IoT) technology and artificial intelligence has become essential in many aspects of daily life since the expansion of the communications and information field. Healthcare is one area that urgently needs to benefit from these technologies to keep up with the dramatic evolution of communications for contemporary human life. IoT, through wearable devices, provides real-time data related to the measurement of a person’s vital signs of health. However, for this data to become more relevant and valuable, it needs to be linked to other domains. Public transport is a domain related to the daily activity of people who take advantage of the IoT to provide exemplary transport services whose quality of service can greatly affect people’s health. The integration of these two domains offers many benefits, especially when providing services adapted to passengers’ health status, making them safer and healthier. This paper proposes an approach based on an IoT architecture using Semantic Web technologies; it aims to integrate health monitoring in public transport, provide passengers with quality transport services, and ensure continuous health monitoring. The use of Semantic Web technologies overcomes the lack of interoperability due to the heterogeneity of data collected by different devices and generated by two different domains. An experimental study was conducted, and the proposed approach’s results were compared with those obtained by the evaluation of a physician. The results show that the approach is effective and should allow passengers to benefit from appropriate transport services that better match their health status.

CODCA – COVID-19 ONTOLOGY FOR DATA COLLECTION AND ANALYSIS IN E-HEALTH

Coronavirus (Covid-19) pandemic is one of the most deadly diseases that cause the death of millions around the world. Automatic collection and analysis of Covid-19 patient data will help medical practitioners in containing the virus. For this purpose, Semantic Web technologies can be utilized, which allows machine-processable data and enables data sharing, and reuse across machines. In this paper, we propose a Covid-19 ontology (named CODCA) that helps in collecting, analysing, and sharing medical information about people in the e-health domain. In particular, the proposed ontology uses information about medical history, drug history, vaccination history, and symptoms in order to analyse Covid-19 risk factors of people and their treatment plans. In this way, information about Covid-19 patients can be automatically processed and can be re-usable by other applications. We also demonstrate extensive semantic queries (i.e. SPARQL queries) to search the created metadata. Furthermore, we illustrate the usage of semantic rules (i.e. SWRL) so that treatment plans for individual patients can be inferred from the available knowledge.

Cohort Identification Using Semantic Web Technologies: Triplestores as Engines for Complex Computable Phenotyping

ABSTRACTBackgroundComputable phenotypes are increasingly important tools for patient cohort identification. As part of a study of risk of chronic opioid use after surgery, we used a Resource Description Framework (RDF) triplestore as our computable phenotyping platform, hypothesizing that the unique affordances of triplestores may aid in making complex computable phenotypes more interoperable and reproducible than traditional relational database queries.To identify and model risk for new chronic opioid users post-surgery, we loaded several heterogeneous data sources into a Blazegraph triplestore: (1) electronic health record data; (2) claims data; (3) American Community Survey data; and (4) Centers for Disease Control Social Vulnerability Index, opioid prescription rate, and drug poisoning rate data. We then ran a series of queries to execute each of the rules in our “new chronic opioid user” phenotype definition to ultimately arrive at our qualifying cohort.ResultsOf the 4,163 patients in the denominator, our computable phenotype identified 248 patients as new chronic opioid users after their index surgical procedure. After validation against charts, 228 of the 248 were revealed to be true positive cases, giving our phenotype a PPV of 0.92.ConclusionWe successfully used the triplestore to execute the new chronic opioid user phenotype logic, and in doing so noted some advantages of the triplestore in terms of schemalessness, interoperability, and reproducibility. Future work will use the triplestore to create the planned risk model and leverage the additional links with ontologies, and ontological reasoning.

Diverse data! Diverse schemata?

One of the key value propositions for knowledge graphs and semantic web technologies is fostering semantic interoperability, i.e., integrating data across different themes and domains. But why do we aim at interoperability in the first place? A common answer to this question is that each individual data source only contains partial information about some phenomenon of interest. Consequently, combining multiple diverse datasets provides a more holistic perspective and enables us to answer more complex questions, e.g., those that span between the physical sciences and the social sciences. Interestingly, while these arguments are well established and go by different names, e.g., variety in the realm of big data, we seem less clear about whether the same arguments apply on the level of schemata. Put differently, we want diverse data, but do we also want diverse schemata or a single one to rule them all?

The automated construction of a scheme for solving compute-intensive problems based on the ontological approach and Semantic Web technologies

This paper describes the tools for supporting researchers in the development of a parallel code. The tools are based on the ontology of the knowledge area “Support for solving compute-intensive problems of mathematical physics on supercomputers”. The main result of these tools operation is a scheme for solving the problem, built according to its specification provided by the user. The scheme includes the most suitable mathematical models for solving the problem, numerical methods, algorithms, and parallel architectures, links to available fragments of a parallel code that the user can use when developing his own code. The scheme construction is carried out on the basis of ontology and expert rules built using the Semantic Web technology.

Interoperable Digital Building Twins Through Communicating Materials and Semantic BIM

This paper presents contributions of the ANR McBIM (Communicating Material for BIM) project regarding Digital Building Twins, specifically how Semantic Web technologies allow providing explainable decision-support. Following an introduction stating our understanding of a Digital Building Twin (DBT), namely a lively representation of a buildings' status and environment, we identify five main research domains following the study of main research issues related to DBT. We then present the state-of-the-art and existing standards for digitizing the construction process, Semantic Web technologies, and wireless sensor networks. We further position the main contributions made so far in the ANR McBIM project's context according to this analysis, e.g., sensor placement in the communicating material and explainable decision-support.

KNOWLEDGE GRAPH CONSTRUCTION FOR SUBSURFACE OBJECTS INCLUDING UNCERTAINTY AND TIME VARIATION

In the recent years the concept of knowledge graph has emerged as a way to aggregate information from various sources without imposing too strict data modelling constraints. Several graph models have been proposed during the years, ranging from the “standard” RDF to more expressive ones, such as Neo4J and RDF-star. The adoption of knowledge graph has become established in several domains. It is for instance the case of the 3D geoinformation domain, where the adoption of semantic web technologies has led to several works in data integration and publishing. However, yet there is not a well-defined model or technique to represent 3D geoinformation including uncertainty and time variation in knowledge graphs. In this paper we propose a model to represent parameterized geometries of subsurface objects. The vocabulary of the model has been defined as an OWL ontology and it extends existing ontologies by adding classes and properties to represent the uncertainty and the spatio-temporal behaviour of a geometry, as well as additional attributes, such as the data provenance. The model has been validated on significant use cases showing different types of uncertainties on 3D subsurface objects. A possible implementation is also presented, using RDF-star for the data representation.

How Industry 4.0 Can Benefit From Semantic Web Technologies and Artefacts

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.