Read about how academia research projects use GraphDB to power innovative solutions to challenges in the fields of Accounting, Healthcare and Cultural Heritage
In this installment of GraphDB In Action, we invite you to think of buildings, cyber-physical environments, and skies as knowledge spaces built of data.
With the research work we’ve picked this time, we walk you through diverse projects that have used Ontotext’s RDF database for knowledge graphs, GraphDB. This has enabled them to meet the requirements coming from heterogeneous data in building automation systems, the interoperability issues critical for design engineering, and, last but not least, the challenges in air-traffic control.
The first paper we are very excited to talk about is Knowledge Discovery Approach to Understand Occupant Experience in Cross-Domain Semantic Digital Twins by Alex Donkers, Bauke de Vries and Dujuan Yang. It was presented on May 29, 2022 at the 10th Linked Data in Architecture and Construction Workshop as part of the ESWC22 conference in Hersonissos, Greece and won the LDAC 2022 Best Paper Award!
The paper discusses the importance of integrating occupant-centric data with building information to better understand and meet the expectations of the users of a building. It reviews previous research on using semantic web technologies to integrate building information and sensor data and presents a new method for knowledge discovery in cross-domain semantic digital twins. The authors describe a five-step knowledge discovery process, with a focus on how semantic web technologies can be applied in each step.
Ontotext GraphDB was used as a semantic database to store and integrate data from various sources, including building information, sensor data, weather data and occupant information and feedback. This was used to create a cross-domain semantic digital twin, which provided a holistic view of the building and its occupants. The proposed knowledge discovery method was then applied to the data stored in GraphDB to identify patterns and insights into occupants’ experiences and preferences. The results of the analysis were then translated into RDF format and stored in the graph for future use.
The paper concludes by highlighting the potential benefits of integrating occupant data with building information for occupant-centric decision-making processes during the operational phase of buildings and improving design feedback. It suggests that future work should demonstrate the validity of the knowledge discovery results with larger sample sizes of occupants and buildings.
The second paper in focus is Driving digital engineering integration and interoperability through semantic integration of models with ontologies by Daniel Dunbar, Thomas Hagedorn, Mark Blackburn, John Dzielski, Steven Hespelt, Benjamin Kruse, Dinesh Verma, Zhongyuan Yu published in Systems Engineering, March 2023.
The paper discusses the use of ontologies and semantic web technologies for digital engineering and introduces the digital engineering framework for integration and interoperability (DEFII). The authors state that computer-assisted collaboration is increasingly necessary due to the growing complexity in the design and development of modern cyber-physical systems. They highlight the concepts of authoritative source of truth and digital thread in digital engineering as well as the traditional tool-to-tool integration approach, which is error prone and difficult to maintain.
As a potential solution, the paper proposes a graph data structure that captures system-specific data and connects it to domain knowledge. It shows that the DEFII framework satisfies three success criteria: it provides clear avenues for mapping data from engineering design and analysis models to an ontology-aligned data store; it allows access to contained data in a flexible, tool-agnostic manner; and it enables the transformation and enhancement of data using various semantic technologies.
The DEFII framework uses Ontotext’s GraphDB to store and query the ontology-aligned data. This enables the reasoning layer of the framework to provide additional inferred statements based on the reasoning profile set up in the triplestore. The paper also demonstrates the use of a SPARQL query to access the ontology-aligned data in GraphDB to identify a vulnerability embedded in the data.
The authors conclude that the DEFII framework is a successful framework for integrating semantic web technologies into digital engineering. The framework addresses current data integration needs and prepares for future capability. They also suggest further research to explore more complex applications of semantic web technologies.
The last paper we want to share with you is Ontology-based anomaly detection for air traffic control systems by Christopher Neal, Jean-Yves De Miceli, David Barrera, José Fernandez, published in ArXiv abs/2207.00637, July 2022.
The paper presents results from a hypothetical scenario in the air traffic control domain where an ontology-based approach could potentially improve the safety and reliability of air traffic control (ATC) systems. The problem the paper addresses is the challenges related to the size of the airline industry, and the difficulty in coordinating the large number of stakeholders and parties. The paper also aims to propose a solution to resolve the security issues inherent to the protocol for Automatic Dependent Surveillance-Broadcast (ADS-B protocol).
As detecting anomalies in ATC systems is crucial for maintaining the safety and efficiency of air traffic, researchers propose an approach that uses a knowledge graph to represent the ATC system and detect anomalies. It does that by identifying inconsistencies between the observed system behavior and the expected behavior described in the ontology. Using semantic technologies and machine learning techniques to analyze aircraft data, the work shows how ontologies can help identify anomalous behavior.
The anomaly detection system based on ontologies that the authors describe uses GraphDB to store data converted into RDF. Prior to executing the attack scenarios in a simulated ATC environment, GraphDB was populated with static base information such as Airspace, Flight Plan, and Airport data. Then this data was used in the reasoning process in conjunction with the real-time data coming from the DDS Network to infer the presence of malicious input.
Researchers conclude that simulated experiments with a falsified ghost aircraft operating under various scenarios demonstrate the feasibility of ontology-based anomaly detection for ATC systems and other real-time security domains.
Every day, we see more and more examples of knowledge graph technology applications in a wide range of real-life use cases. Knowledge graphs are becoming a necessity in Building Automation Systems and Engineering. What’s more, as we have already pointed out in this post, they are becoming a crucial technology in the Aerospace industry, where six out of the top ten aerospace companies in the world use GraphDB in some part of their operations.
Knowledge graphs bring new ways of managing complexity in a world that is increasingly built of sensors, devices, and data spaces. These ways are increasingly paved by the application of semantic technology, leading toward a more resilient and sustainable data future.