PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

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 imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

The sensor to decision chain in crisis management

In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. Decision Support Systems (DSSs) for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things (IoT) and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with existing risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step

Applying Semantic Web Technologies for Decision Support in Climate-Related Crisis Management: Paper presented at 2nd International Conference on Citizen Observatories for natural hazards and Water Management, COWM 2018, Venice, 27-30 November 2018

- During climate-related crises vast volumes of heterogeneous multimodal information are generated. - Meaningfully processing and communicating this information for efficient decision support is a key challenge. - The paper describes applying Semantic Web technologies for decision support during such crises. - We are proposing the application of these technologies in the whole "sensor to decision chain". - This approach is being tested within the beAWARE EU project, with contributions by domain experts

Deliverable 4.5: Context-aware Content Interpretation

The current deliverable summarises the work conducted within task T4.5 of WP4, presenting our proposed approaches for contextualised content interpretation, aimed at gaining insightful contextualised views on content semantics. This is achieved through the adoption of appropriate context-aware semantic models developed within the project, and via enriching the semantic descriptions with background knowledge, deriving thus higher level contextualised content interpretations that are closer to human perception and appraisal needs. More specifically, the main contributions of the deliverable are the following: A theoretical framework using physics as a metaphor to develop different models of evolving semantic content. A set of proof-of-concept models for semantic drifts due to field dynamics, introducing two methods to identify quantum-like (QL) patterns in evolving information searching behaviour, and a QL model akin to particle-wave duality for semantic content classification. Integration of two specific tools, Somoclu for drift detection and Ncpol2spda for entanglement detection. An “energetic” hypothesis accounting for contextualized evolving semantic structures over time. A proposed semantic interpretation framework, integrating (a) an ontological inference scheme based on Description Logics (DL), (b) a rule-based reasoning layer built on SPARQL Inference Notation (SPIN), (c) an uncertainty management framework based on non-monotonic logics. A novel scheme for contextualized reasoning on semantic drift, based on LRM dependencies and OWL’s punning mechanism. An implementation of SPIN rules for policy and ecosystem change management, with the adoption of LRM preconditions and impacts. Specific use case scenarios demonstrate the context under development and the efficiency of the approach. Respective open-source implementations and experimental results that validate all the above.All these contributions are tightly interlinked with the other PERICLES work packages: WP2 supplies the use cases and sample datasets for validating our proposed approaches, WP3 provides the models (LRM and Digital Ecosystem models) that form the basis for our semantic representations of content and context, WP5 provides the practical application of the technologies developed to preservation processes, while the tools and algorithms presented in this deliverable can be deployed in combination with test scenarios, which will be part of the WP6 test beds.PERICLE

PERICLES Deliverable 4.4: Modelling Contextualised Semantics

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 refers to any information coming from the environment of the digital object (DO) that offers a better insight into the object’s status, its  interrelationships with other content items and information about the object’s context of use. Within PERICLES, we refer to the content semantics enriched with the contextual perspective as “contextualised semantics”. The deliverable presents two complementary modelling approaches, based respectively on (a) ontologies and logics, and, (b) multivariate statistics.PERICLE