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PublicationKnowledge Engineering and Ontology for Crime Investigation( 2022)Building upon the possibilities of technologies like ontology engineering, knowledge representational models, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAs with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and Organized Crime Groups (OCG). The paper at hand focuses on the developed ontology, the tool for Semantic Reasoning and the knowledge base and knowledge visualization.
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PublicationAccessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions( 2022)The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system’s hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.
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PublicationKnowledge Engineering for Crime Investigation( 2022)Building upon the possibilities of technologies like ontology engineering, knowledge representational models, text mining, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAS with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and OGCs. The paper at hand focuses on the developed ontology and the tools for text mining, Extract Transform Load, Semantic Reasoning and the knowledge base and knowledge visualization.
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PublicationCommon Representational Model and Ontologies for Effective Law Enforcement Solutions( 2020)Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a concept. For Artificial Intelligence, only the defined notions can be considered existent. Thus, in relation to AI, an ontology can be understood as part of a program which delineates a collection of descriptions. An ontology, therefore, correlates the labels of the entities in the universe of discourse with wording that holds meaning for humans, explaining what those labels signify, along with the precise principles that force the interpretation and semantic utilization of these labels. An ontology constitutes a proper statement of a logical theory. It is a crucial component of a system with the capability to process, manage, analyze, correlate and reason from the large datasets characterized by heterogeneity. This paper depicts the process of development of a Common Representational Model (CRM) on top of several ontologies, taxonomies and classifications to facilitate computational and data mining functionalities. The building blocks of said CRM are delineated in detail, as well as its application in a specific use case.
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PublicationThe Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions - MAGNETO Platform Use Case( 2019)Every single day more and more organizations face the challenge of finding a way to support their conduct with data. The flooding amounts of data currently available vastly outweigh human capabilities, thus Big Data processing becomes a pressing issue. This problem is especially prevailing for Law Enforcement Agencies (LEAs), where massive amounts of critical data are collected from heterogenous sources, often by various entities in different countries. Ontologies have been developed into a predominant technique for establishing semantic interoperability among heterogeneous systems which transact information. In this paper we propose the Magneto ontology - a solution built as a crucial part of the Magneto project. It has been developed on top of well-established ontologies dealing with people, events and security incidents, bearing in mind the heterogenous nature of the myriad of data sources as the starting point. Examples of the building blocks, a classification of the sources of data, an overview of the application in a specific use scenario and a discussion on the future use of the ontology will be given.
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PublicationImplementing the HERACLES Ontology - An Ontology as backbone for a Knowledge Base in the Cultural Heritage Protection Domain( 2019)
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PublicationThe Industrie 4.0 Asset Administration Shell as Information Source for Security Analysis( 2019)One of the essential concepts of the Reference Architecture Model Industrie 4.0 (RAMI4.0) is the uniform modelling of assets by means of a common meta-data model called the Asset Administration Shell (AAS). However, important practical experience with this concept is still missing, as not many use cases for the AAS have yet been implemented. Thus, practical issues within the AAS concept and respective solutions are hard to identify. In this paper, presents our experience with the implementation of an AAS use case. The AAS is used as information source to create an ontology, which is then used for security analysis. The paper discusses the use-case specific modelling language selection and provides a practical examination of several of our implementations that use OWL and OPC UA together. Furthermore, it provides recommendations for the implementation of Asset Administration Shells for this and similar use cases.
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PublicationAn Ontology for Cultural Heritage Protection against Climate Change( 2018)Environmental factors, worsened by the increasing climate change impact, represent significant threats to European Cultural Heritage (CH) assets. In Europe, the huge number and diversity of CH assets, together with the different climatological sub-regions aspects, as well as the different adaptation policies to climate change adopted (or to be adopted) by the different nations, generate a very complex scenario. This paper will present a multidisciplinary methodology that will bridge the gap between two different worlds: the CH stakeholders and the scientific/technological experts. Since protecting cultural heritage assets and increasing their resilience against effects caused by the climate change is a multidisciplinary task, experts from many domains need to work together to meet their conservation goals. This paper discusses a method for facilitating the work for the different experts. A new ontology has been designed integrating all necessary aspects for improving the resilience of cultural heritages on site. This ontology combines the following topics: Cultural Heritage Assets, Stakeholders and Roles, Climate and Weather Effects, Risk Management, Conservation Actions, Materials, Sensors, Models and Observations, Standard Operation Procedures/Workflows and Damages.
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PublicationThe sensor to decision chain in crisis management( 2018)
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PublicationOntology-based detection of cyber-attacks to SCADA-systems in critical infrastructures( 2016)The integration of networks within an organization made many critical infrastructures (CI) and their underlying communication networks that were rather isolated in the past, accessible from outside via internet. CI heavily rely on the security of their supervisory control and data acquisition (SCADA) systems. As attackers are using ever more sophisticated technologies the threats are always increasing. Therefore it is important to detect attacks quickly and react efficiently to them, thus increasing reliability, security and resilience of the system. To specify a model of security events, attacks and vulnerabilities, we propose an ontology. The system logs provide the events, which the intrusion detection systems (IDS) may recognize as suspicious and could be part of an attack. With the help of data bases for known vulnerabilities together with the system model ongoing attacks may be identified. The ontology-framework together with a respective reasoning component forms the common ground for compliance monitoring and correlation of security events and serves as a basis for the specification and implementation of security data normalization. Then security policies (or goals) can be refined into implementable configurations on critical infrastructure network devices.
