The timely and efficient cooperation across sectors and borders during maritime crises is paramount for the safety of human lives. Maritime monitoring authorities are now realizing the grave importance of cross-sector and cross-border information sharing. However, this cooperation is compromised by the diversity of existing systems and the vast volumes of heterogeneous data generated and exchanged during maritime operations. In order to address these challenges, the EU has been driving several initiatives, including several EU-funded projects, for facilitating information exchange across sectors and borders. A key outcome from these efforts is the Common Information Sharing Environment (CISE), which constitutes a collaborative initiative for promoting automated information sharing between maritime monitoring authorities. However, the adoption of CISE is substantially limited by its existing serialization as an XML Schema only, which facilitates information sharing and exchange to some extent, but fails to deliver the fundamental additional benefits provided by ontologies, like the richer semantics, enhanced semantic interoperability and semantic reasoning capabilities. Thus, this paper presents EUCISE-OWL, an ontology representation of the CISE data model that capitalizes on the benefits provided by ontologies and aims to encourage the adoption of CISE. EUCISE-OWL is an outcome from close collaboration in an EU-funded project with domain experts with extensive experience in deploying CISE in practice. The paper also presents a representative example for handling information exchange during a maritime crisis as well as performance results for specific querying tasks that can demonstrate and evaluate the use of the proposed ontology in practice.

Cross-border crime utilizes recent advanced systems to perform their illegal activities. Innovative sensory systems and specialized equipment are examples that were used for trafficking of human and of various illicit materials. The increasing challenges that border personnel must resolve require the usage of recent technological advances as well. Thus, the utilization of pioneer technologies seems imperative to precede technologically organized crime. Towards this objective, the introduction of unmanned vehicles (UxV) and the advances of relevant sub-systems have created a new solution to fight cross-border crime. Utilizing a combination of UxVs enriched with enhanced detection capabilities comprises an effective solution. The chapter will introduce and present the capability of an autonomous navigation system by exploiting swarm intelligence principles towards simplifying the overall operation. Computer vision advances and semantic enrichment of the acquired information are incorporated to deliver cutting-edge technologies. The described architecture and services can provide a complete solution for optimal border surveillance and increased situation awareness.

This paper presents a rule-based approach towards spatial awareness for the deafblind through natural language constructs. The approach entails two components, a novel ontology for the interoperable representation of data pertaining the domain (objects, space, etc.) and a rule set to derive the natural language constructs for spatial awareness and answer related user queries. The rule set is expressed in SPARQL Inferencing Notation (SPIN), which enables simplicity and flexibility in rule definition as opposed to other frameworks. Both are applied in a use case scenario of the SUITCEYES platform for the deafblind, extending it with the ability to answer spatial awareness queries. More specifically, the ontology component uses rules to provide the users of the platform answers to queries regarding their environment. We present those rules and show how they inform the user of their surroundings, using natural language. Furthermore, we provide a differential population solution to avoid overloading the ontology with unnecessary data. Keywordsontology; rules; natural language; SPIN; deafblindness.

Based on the ontology developed in the ongoing SUITCEYES EU-funded project to bridge visual analytics for situational awareness and navigation with semantic labelling of environmental cues, we desi ...

Ontologies are a rapidly emerging paradigm for knowledge representation, with a growing number of applications in various domains. However, populating ontologies with massive volumes of data is an extremely challenging task. The field of ontology population offers a wide array of approaches for populating ontologies in an automated or semi-automated way. Nevertheless, most of the related tools typically analyse natural language text, while sources of more structured information like Linked Open Data would arguably be more appropriate. The paper presents PROPheT, a novel software tool for ontology population and enrichment. PROPheT can populate a local ontology model with instances retrieved from diverse Linked Data sources served by SPARQL endpoints. To the best of our knowledge, no existing tool can offer PROPheT’s diverse extent of functionality.

As urban atmospheric conditions are tightly connected to citizens’ quality of life, the concept of efficient environmental decision support systems becomes highly relevant. However, the scale and heterogeneity of the involved data, together with the need for associating environmental information with physical reality, increase the complexity of the problem. In this work, we capitalize on the semantic expressiveness of ontologies to build a framework that uniformly covers all phases of the decision making process: from structuring and integration of data, to inference of new knowledge. We define a simplified ontology schema for representing the status of the environment and its impact on citizens’ health and actions. We also implement a novel ontology- and rule-based reasoning mechanism for generating personalized recommendations, capable of treating differently individuals with diverse levels of vulnerability under poor air quality conditions. The overall framework is easily adaptable to new sources and needs.

Google Trends (GT) describes the variation of the relevant interest of internet searches toward medical conditions and related symptoms. Allergic rhinitis symptom levels result from the intensity of exposure to aeroallergens in combination with relevant medication use. We analyze data from Germany to examine the relationship between hay fever-related Google search terms, symptom levels, medication use, and pollen count levels. For doing so, we also employ the new definitions on pollen season and peak pollen period start and end as proposed by the European Academy of Allergy and Clinical Immunology in a recently published position paper. We extract GT data for a number of search terms related to allergic rhinitis for Germany. We use total nasal symptom and mediation scores as reported by patients via a patient hay fever diary in the Berlin and Brandenburg areas in Germany for 3 years (2014–2016), accompanied by pollen data. Then a Pearson and Spearman correlation analysis is performed between symptom data and GT data. A graphical analysis is conducted, and the identification of pollen season and peak pollen periods is done based on the EAACI criteria. The analysis reveals that GT data are highly correlated with symptom levels and follow peak pollen period start–end, concerning grass and birch pollen-induced allergic rhinitis symptoms. GT data can be used as a proxy for the identification of the onset and variation of nasal symptom and medication score for allergic rhinitis sufferers.

Although air pollution is one of the most significant environmental factors posing a threat to human health worldwide, air quality data are scarce or not easily accessible in most European countries. The current work aims to develop a centralized air quality data hub that enables citizens to contribute to air quality monitoring. In this work, data from official air quality monitoring stations are combined with air pollution estimates from sky-depicting photos and from low-cost sensing devices that citizens build on their own so that citizens receive improved information about the quality of the air they breathe. Additionally, a data fusion algorithm merges air quality information from various sources to provide information in areas where no air quality measurements exist.

Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents the findings, current limitations and lessons learned throughout the development and the application of a novel software tool, developed in the context of the PERICLES FP7 project, which integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protégé. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.

Management of change is essential to ensure the long-term reusabilityof digital assets. Change can be brought about in many ways, includingthrough technological, user community and policy factors. ...

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperati ...

Semantic drift is an active field of research, aiming to identify and measure changes in ontologies across time and versions, closely related to ontology evolution. However, practical and widely adopted methods that are directly applicable to Semantic Web constructs have yet to emerge. Building upon and extending existing work, this paper presents a framework for measuring semantic drift in ontologies across time or multiple versions, using text and structural similarity methods to provide valuable insights. Its applicability and usefulness are validated through a proof-ofconcept scenario in Digital Preservation, where long-term insights about change are crucial, to track drift across a decade’s worth of real-world digital media data.

The current deliverable summarises the work conducted within task T4.4 of WP4, presenting our proposed models for semantically representing digital content and its respective context – the latter r ...

In order for ontology-based applications to be deployed in real-life scenarios, significant volumes of data are required to populate the underlying models. Populating ontologies manually is a time-consuming and error-prone task and, thus, research has shifted its attention to automatic ontology population methodologies. However, the majority of the proposed approaches and tools focus on analysing natural language text and often neglect other more appropriate sources of information, such as the already structured and semantically rich sets of Linked Data. The paper presents PROPheT, a novel ontology population tool for retrieving instances from Linked Data sources and subsequently inserting them into an OWL ontology. The tool, to the best of our knowledge, offers entirely novel ontology population functionality to a great extent and has already been positively received according to user evaluation.

The rise of the Semantic Web has provided cultural heritage researchers and practitioners with several tools for providing semantically rich representations and interoperability of cultural heritage collections. Although indeed offering a lot of advantages, these tools, which come mostly in the form of ontologies and related vocabularies, do not provide a conceptual model for capturing contextual and environmental dependencies, contributing to long-term digital preservation. This paper presents one of the key outcomes of the PERICLES FP7 project, the Linked Resource Model, for modelling dependencies as a set of evolving linked resources. The adoption of the proposed model and the consistency of its representation are evaluated via a specific instantiation involving the domain of digital video art.

In this paper we introduce an ODP for representing digital video resources. The aim is to model digital video files, their components and other associated entities, such as codecs and containers. The proposed design pattern facilitates the creation of relevant domain ontologies that will be deployed in the fields of media archiving and digital preservation of videos and video artworks. This ODP has been developed within the PERICLES FP7 project.

The rise of the Semantic Web has provided cultural heritage researchers and practitioners with several tools for ensuring semantic-rich representations and interoperability of cultural heritage collections. Although indeed offering a lot of advantages, these tools, which come mostly in the form of ontologies and related vocabularies, do not provide a conceptual model for capturing contextual and environmental dependencies contributing to long-term digital preservation. This paper presents one of the key outcomes of the PERICLES FP7 project, the Linked Resource Model, for modelling dependencies as a set of evolving linked resources. The proposed model is evaluated via a domain-specific representation involving digital video art.