22 research outputs found

    Description Logics of Context with Rigid Roles Revisited

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    To represent and reason about contextualized knowledge often two-dimensional Description Logics (DLs) are employed, where one DL is used to describe contexts (or possible worlds) and the other DL is used to describe the objects, i.e. the relational structure of the specific contexts. Previous approaches for DLs of context that combined pairs of DLs resulted in undecidability in those cases where so-called rigid roles are admitted, i.e. if parts of the relational structure are the same in all contexts. In this paper, we present a novel combination of pairs of DLs and show that reasoning stays decidable even in the presence of rigid roles. We give complexity results for various combinations of DLs involving ALC, SHOQ, and EL

    Event Detection and Modelling for Security Application

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    PhD thesisThis thesis focuses on the design and implementation of a novel security domain surveillance system framework that incorporates multimodal information sources to assist the task of event detection from video and social media sources. The comprehensive framework consists of four modules including Data Source, Content Extraction, Parsing and Semantic Knowledge. The security domain ontology conceptual model is proposed for event representation and tailored in conformity with elementary aspects of event description. The adaptation of DOLCE foundational ontology promotes flexibility for heterogeneous ontologies to interoperate. The proposed mapping method using eXtensible Stylesheet Language Transformation (XSLT) stylesheet approach is presented to allow ontology enrichment and instance population to be executed efficiently. The dataset for visual semantic analysis utilizes video footage of 2011 London Riots obtained from Scotland Yard. The concepts person, face, police, car, fire, running, kicking and throwing are chosen to be analysed. The visual semantic analysis results demonstrate successful persons, actions and events detection in the video footage of riot events. For social semantic analysis, a collection of tweets from twitter channels that was actively reporting during the 2011 London Riots was compiled to create a Twitter corpus. The annotated data are mapped in the ontology based on six concepts: token, location, organization, sentence, verb, and noun. Several keywords related to the event that has been presented in the visual and social media sources are chosen to examine the correlation between both sources and to draw supplementary information regarding the event. The chosen keywords describe actions running, throwing, and kicking; activity attack, smash and loot; event fire; and location Hackney and Croydon. An experiment in respect to concept-noun relations are also been executed. The ontology-based visual and social media analysis yields a promising result in analysing long content surveillance videos and lengthy text corpus of social media user-generated content. Adopting ontology-based approach, the proposed novel security domain surveillance system framework enables a large amount of visual and social media data to be analysed systematically and automatically, and promotes a better method for event detection and understanding

    Pseudo-contractions as Gentle Repairs

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    Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

    Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

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    In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems

    Ontological Model for Xhosa Beadwork in Marginalised Rural Communities: A Case of the Eastern Cape

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    In South Africa, computational ontologies have gained traction and are increasingly viewed as one of the viable solutions to address the problem of fragmented and unstructured nature of indigenous knowledge (IK) particularly in the marginalized rural communities. The continual existence of IK in tacit form has impeded the use of IK as a potential resource that can catalyze socio-economic and cultural development in South Africa. This study was, therefore, designed to address part of this challenge by developing a Xhosa Beadwork Ontology (XBO) with the goal of structuring the domain knowledge into a reusable body of knowledge. Such a reusable body of knowledge promotes efficient sharing of a common understanding of Xhosa Beadwork in a computational form. The XBO is in OWL 2 DL. The development of the XBO was informed by the NeOn methodology and the iterativeincremental ontology development life cycle within the ambit of Action Research (AR). The XBO was developed around personal ornamentation Xhosa Beadwork consisting of Necklace, Headband, Armlet, Waistband, Bracelet, and Anklet. In this study, the XBO was evaluated focused on ascertaining that the created ontology is a comprehensive representation of the Xhosa Beadwork and is of the required standard. In addition, the XBO was documented into a human understandable and readable resource and was published. The outcome of the study has indicated that the XBO is an adequate, shareable and reusable semantic artifact that can indeed support the formalization and preservation of IK in the domain of Xhosa Beadwor

    Context Reasoning for Role-Based Models

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    In a modern world software systems are literally everywhere. These should cope with very complex scenarios including the ability of context-awareness and self-adaptability. The concept of roles provide the means to model such complex, context-dependent systems. In role-based systems, the relational and context-dependent properties of objects are transferred into the roles that the object plays in a certain context. However, even if the domain can be expressed in a well-structured and modular way, role-based models can still be hard to comprehend due to the sophisticated semantics of roles, contexts and different constraints. Hence, unintended implications or inconsistencies may be overlooked. A feasible logical formalism is required here. In this setting Description Logics (DLs) fit very well as a starting point for further considerations since as a decidable fragment of first-order logic they have both an underlying formal semantics and decidable reasoning problems. DLs are a well-understood family of knowledge representation formalisms which allow to represent application domains in a well-structured way by DL-concepts, i.e. unary predicates, and DL-roles, i.e. binary predicates. However, classical DLs lack expressive power to formalise contextual knowledge which is crucial for formalising role-based systems. We investigate a novel family of contextualised description logics that is capable of expressing contextual knowledge and preserves decidability even in the presence of rigid DL-roles, i.e. relational structures that are context-independent. For these contextualised description logics we thoroughly analyse the complexity of the consistency problem. Furthermore, we present a mapping algorithm that allows for an automated translation from a formal role-based model, namely a Compartment Role Object Model (CROM), into a contextualised DL ontology. We prove the semantical correctness and provide ideas how features extending CROM can be expressed in our contextualised DLs. As final step for a completely automated analysis of role-based models, we investigate a practical reasoning algorithm and implement the first reasoner that can process contextual ontologies

    Contribution to the elaboration of a decision support system based on modular ontologies for ecological labelling

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    With the rising concern of sustainability and environmental performance, eco-labeled products and services are becoming more and more popular. In addition to the financial costs, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. In this research work, we propose a decision support process and implement a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels. The decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to official eco-label criteria documentation. Traditional knowledge base in relational data model is low interoperable, lack of inference support and difficult to be reused. In our research, the knowledge base composed of interconnected ontologies modules covers various products and services, and allows reasoning and semantic querying. A domain-centric modularization scheme about EU Eco-label laundry detergent product criteria is introduced as an application case. This modularization scheme separates the entity knowledge and rule knowledge so that the ontology modules can be reused easily in other domains. We explore a reasoning methodology based on inference with SWRL (Semantic Web Rule Language) rules which allows decision making with explanation. Through standard RDF (Resource Description Framework) and OWL (Web Ontology Language) ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other application. In order to foster the reuse of ontology modules, we also proposed a usercentric approach for federate contextual ontologies (mapping and integration). This approach will create an ontology federation by a contextual configuration that avoid the “OWL:imports” disadvantages. Instead of putting mapping or new semantics in ontology modules, our approach will conserve the extra contextual information separately without impacting original ontologies or without importing all ontologies’ concepts. By introducing this contextualization, it becomes easier to support more expressive semantics in term of ontology integration itself, then it will also facilitate application agents to access and reuse ontologies. To realize this approach, we elaborate a new plug-in for the Protégé ontology editor

    Description logics of context

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    We introduce Description Logics of Context (DLCs)—an extension of Description Logics (DLs) for context-based reasoning. Our approach descends from J. McCarthy's tradition of treating contexts as formal objects over which one can quantify and express first-order properties. DLCs are founded in two-dimensional possible world semantics, where one dimension represents a usual object domain and the other a domain of contexts, and accommodate two interacting DL languages—the object and the context language—interpreted over their respective domains. Effectively, DLCs comprise a family of two-sorted , two-dimensional combinations of pairs of DLs. We argue that this setup ensures a well-grounded, generic framework for capturing and studying mechanisms of contextualization in the DL paradigm. As the main technical contribution, we prove 2ExpTime-completeness of the satisfiability problem in the maximally expressive DLC, based on the DL forumla . As an interesting corollary, we show that under certain conditions this result holds also for a range of two-dimensional DLs, including the prominent forumla
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