Towards a core ontology for information integration

In this paper, we argue that a core ontology is one of the key building blocks necessary to enable the scalable assimilation of information from diverse sources. A complete and extensible ontology that expresses the basic concepts that are common across a variety of domains and can provide the basis for specialization into domain-specific concepts and vocabularies, is essential for well-defined mappings between domain-specific knowledge representations (i.e., metadata vocabularies) and the subsequent building of a variety of services such as cross-domain searching, browsing, data mining and knowledge extraction. This paper describes the results of a series of three workshops held in 2001 and 2002 which brought together representatives from the cultural heritage and digital library communities with the goal of harmonizing their knowledge perspectives and producing a core ontology. The knowledge perspectives of these two communities were represented by the CIDOC/CRM [31], an ontology for information exchange in the cultural heritage and museum community, and the ABC ontology [33], a model for the exchange and integration of digital library information. This paper describes the mediation process between these two different knowledge biases and the results of this mediation - the harmonization of the ABC and CIDOC/CRM ontologies, which we believe may provide a useful basis for information integration in the wider scope of the involved communities

A semantic-based probabilistic approach for real-time video event recognition

This is the author’s version of a work that was accepted for publication in Journal Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Computer Vision and Image Understanding, 116, 9 (2012) DOI: 10.1016/j.cviu.2012.04.005This paper presents an approach for real-time video event recognition that combines the accuracy and descriptive capabilities of, respectively, probabilistic and semantic approaches. Based on a state-of-art knowledge representation, we define a methodology for building recognition strategies from event descriptions that consider the uncertainty of the low-level analysis. Then, we efficiently organize such strategies for performing the recognition according to the temporal characteristics of events. In particular, we use Bayesian Networks and probabilistically-extended Petri Nets for recognizing, respectively, simple and complex events. For demonstrating the proposed approach, a framework has been implemented for recognizing human-object interactions in the video monitoring domain. The experimental results show that our approach improves the event recognition performance as compared to the widely used deterministic approach.This work has been partially supported by the Spanish Administration agency CDTI (CENIT-VISION 2007- 1007), by the Spanish Government (TEC2011-25995 EventVideo), by the Consejería de Educación of the Comunidad de Madrid and by The European Social Fund

A semantic concept for the mapping of low-level analysis data to high-level scene descriptions

Zusammen mit dem wachsenden Bedarf an Sicherheit wird eine zunehmende Menge an Überwachungsinhalten geschaffen. Um eine schnelle und zuverlässige Suche in den Aufnahmen hunderter oder tausender in einer einzelnenEinrichtung installierten Überwachungssensoren zu ermöglichen, istdie Indizierung dieses Inhalts im Voraus unentbehrlich. Zu diesem Zweckermöglicht das Konzept des Smart Indexing & Retrieval (SIR) durch dieErzeugung von high-level Metadaten kosteneffiziente Suchen. Da es immerschwieriger wird, diese Daten manuell mit annehmbarem Zeit- und Kostenaufwandzu generieren, muss die Erzeugung dieser Metadaten auf Basis vonlow-level Analysedaten automatisch erfolgen.Während bisherige Ansätze stark domänenabhängig sind, wird in dieserArbeit ein generisches Konzept für die Abbildung der Ergebnisse von lowlevelAnalysedaten auf semantische Szenenbeschreibungen präsentiert. Diekonstituierenden Elemente dieses Ansatzes und die ihnen zugrunde liegendenBegriffe werden vorgestellt, und eine Einführung in ihre Anwendungwird gegeben. Der Hauptbeitrag des präsentierten Ansatzes sind dessen Allgemeingültigkeit und die frühe Stufe, auf der der Schritt von der low-levelauf die high-level Repräsentation vorgenommen wird. Dieses Schließen in derMetadatendomäne wird in kleinen Zeitfenstern durchgeführt, während dasSchließen auf komplexeren Szenen in der semantischen Domäne ausgeführtwird. Durch die Verwendung dieses Ansatzes ist sogar eine unbeaufsichtigteSelbstbewertung der Analyseergebnisse möglich

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

state of the art analysis ; working packages in project phase II

In this report, we introduce our goals and present our requirement analysis for the second phase of the Corporate Semantic Web project. Corporate ontology engineering will improve the facilitation of agile ontology engineering to lessen the costs of ontology development and, especially, maintenance. Corporate semantic collaboration focuses the human-centered aspects of knowledge management in corporate contexts. Corporate semantic search is settled on the highest application level of the three research areas and at that point it is a representative for applications working on and with the appropriately represented and delivered background knowledge

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Semantic interoperability in ad-hoc computing environments

This thesis introduces a novel approach in which multiple heterogeneous devices collaborate to provide useful applications in an ad-hoc network. This thesis proposes a smart home as a particular ubiquitous computing scenario considering all the requirements given by the literature for succeed in this kind of systems. To that end, we envision a horizontally integrated smart home built up from independent components that provide services. These components are described with enough syntactic, semantic and pragmatic knowledge to accomplish spontaneous collaboration. The objective of these collaboration is domestic use, that is, the provision of valuable services for home residents capable of supporting users in their daily activities. Moreover, for the system to be attractive for potential customers, it should offer high levels of trust and reliability, all of them not at an excessive price. To achieve this goal, this thesis proposes to study the synergies available when an ontological description of home device functionality is paired with a formal method. We propose an ad-hoc home network in which components are home devices modelled as processes represented as semantic services by means of the Web Service Ontology (OWL-S). In addition, such services are specified, verified and implemented by means of the Communicating Sequential Processes (CSP), a process algebra for describing concurrent systems. The utilisation of an ontology brings the desired levels of knowledge for a system to compose services in a ad-hoc environment. Services are composed by a goal based system in order to satisfy user needs. Such system is capable of understaning, both service representations and user context information. Furthermore, the inclusion of a formal method contributes with additional semantics to check that such compositions will be correctly implemented and executed, achieving the levels of reliability and costs reduction (costs derived form the design, development and implementation of the system) needed for a smart home to succeed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Ensuring Cyber-Security in Smart Railway Surveillance with SHIELD

Modern railways feature increasingly complex embedded computing systems for surveillance, that are moving towards fully wireless smart-sensors. Those systems are aimed at monitoring system status from a physical-security viewpoint, in order to detect intrusions and other environmental anomalies. However, the same systems used for physical-security surveillance are vulnerable to cyber-security threats, since they feature distributed hardware and software architectures often interconnected by ‘open networks’, like wireless channels and the Internet. In this paper, we show how the integrated approach to Security, Privacy and Dependability (SPD) in embedded systems provided by the SHIELD framework (developed within the EU funded pSHIELD and nSHIELD research projects) can be applied to railway surveillance systems in order to measure and improve their SPD level. SHIELD implements a layered architecture (node, network, middleware and overlay) and orchestrates SPD mechanisms based on ontology models, appropriate metrics and composability. The results of prototypical application to a real-world demonstrator show the effectiveness of SHIELD and justify its practical applicability in industrial settings