There are two significant impedances to the realization of the potential of ontologies. First, many ontology designers lack the necessary background in formal logics to express their intuitions clearly and precisely, resulting in the proliferation of ontologies with low expressivity. Concurrently, developing semantic mappings between existing ontologies is difficult, because much of the semantics is external to the representation. This thesis uses the idea of metaphor to develop architectures for ontology repositories to serve as bottom-up reusable resources. Moreover, an ontology design algorithm has been developed that allows designers to communicate their ideas at the semantic level, simply by generating and vetting models. Finally, a semantic mapping algorithm has been developed that uses an ontology repository to determine the similarities and differences between any number of target ontologies. An ontology for partial orders has been elaborated to demonstrate the proof of concept and populate the first iteration of the repository.

In this thesis, semantic mapping is understood to be the process of putting a tag or label on objects or regions in a map. This label should be interpretable by and have a meaning for a human. The use of semantic information has several application areas in mobile robotics. The largest area is in human-robot interaction where the semantics is necessary for a common understanding between robot and human of the operational environment. Other areas include localization through connection of human spatial concepts to particular locations, improving 3D models of indoor and outdoor environments, and model validation. This thesis investigates the extraction of semantic information for mobile robots in outdoor environments and the use of semantic information to link ground-level occupancy maps and aerial images. The thesis concentrates on three related issues: i) recognition of human spatial concepts in a scene, ii) the ability to incorporate semantic knowledge in a map, and iii) the ability to connect information collected by a mobile robot with information extracted from an aerial image. The first issue deals with a vision-based virtual sensor for classification of views (images). The images are fed into a set of learned virtual sensors, where each virtual sensor is trained for classification of a particular type of human spatial concept. The virtual sensors are evaluated with images from both ordinary cameras and an omni-directional camera, showing robust properties that can cope with variations such as changing season. In the second part a probabilistic semantic map is computed based on an occupancy grid map and the output from a virtual sensor. A local semantic map is built around the robot for each position where images have been acquired. This map is a grid map augmented with semantic information in the form of probabilities that the occupied grid cells belong to a particular class. The local maps are fused into a global probabilistic semantic map covering the area along the trajectory of the mobile robot. In the third part information extracted from an aerial image is used to improve the mapping process. Region and object boundaries taken from the probabilistic semantic map are used to initialize segmentation of the aerial image. Algorithms for both local segmentation related to the borders and global segmentation of the entire aerial image, exemplified with the two classes ground and buildings, are presented. Ground-level semantic information allows focusing of the segmentation of the aerial image to desired classes and generation of a semantic map that covers a larger area than can be built using only the onboard sensors.

This thesis examines the role of function in representations of space by robots - that is, dealing directly and explicitly with those aspects of space and objects in space that serve some purpose for the robot. It is suggested that taking function into account helps increase the generality and robustness of solutions in an unpredictable and complex world, and the suggestion is affirmed by several instantiations of functionally conceived spatial models. These include perceptual models for the "on" and "in" relations based on support and containment; context-sensitive segmentation of 2-D maps into regions distinguished by functional criteria; and, learned predictive models of the causal relationships between objects in physics simulation. Practical application of these models is also demonstrated in the context of object search on a mobile robotic platform. / QC 20111125

With the growing demand for information in various domains, sharing of information from heterogeneous data sources is now a necessity. Data integration approaches promise to combine data from these different sources and present to the user a single, unified view of these data. However, although these approaches offer high quality services for the managing and integrating of data, they come with a high cost. This is because a great amount of manual effort to form relationships across data sources is needed to set up the data integration system. A newer variant of data integration, known as dataspaces, aims to spread the large manual effort spent at the start of the data integration system to the rest of the system's phases. This is achieved by soliciting from the user their feedback on a chosen artefact of a dataspace, either by explicit ways or implicitly. This practice is known as pay-as-you-go, where a user continuously pays to the data integration system, by providing feedback, to gain improvements in the quality of data integration. This PhD addresses two challenges in data integration by using pay-as-you-go approaches. The first is to identify instances relevant to a user's information need, calling for semantic mappings to be closely considered. Our contribution is a technique that ranks mappings with the help of implicit user feedback (i.e., terms found in query logs). Our evaluation shows that to produce stable rankings, our technique does not require large-sized query logs, and that our generated ranking is able to respond satisfactorily to the amount of terms inclined towards a particular data source, where we describe it as skew. The second challenge that we address is the identification of duplicate instances from disparate data sources. We contribute a strategy that uses explicitly-obtained user feedback to drive an evolutionary search algorithm to find suitable parameters for an underlying clustering algorithm. Our experiments show that optimising the algorithm's parameters and introducing attribute weights produces fitter clusters than clustering alone. However, our strategy to improve on integration quality can be quite expensive. Therefore, we propose a pruning technique to select from a dataset any records that are informative. Our experiment shows that on most of the datasets, our pruner produce comparably fit clusters with more feedback received.

Visual navigation is a ubiquitous yet complex task which is performed by many species for the purpose of survival. Although visual navigation is actively being studied within the robotics community, the determination of elemental constituents of a robust visual navigation system remains a challenge. Motion estimation is mistakenly considered as the sole ingredient to make a robust autonomous visual navigation system and therefore efforts are made to improve the accuracy of motion estimations. On the contrary, there are other factors which are as important as motion and whose absence could result in inability to perform seamless visual navigation such as the one exhibited by humans. Therefore, it is needed that a general model for a visual navigation system be devised which would describe it in terms of a set of elemental units. In this regard, a set of visual navigation elements (i.e. spatial memory, motion memory, scene geometry, context and scene semantics) are suggested as building blocks of a visual navigation system in this thesis. A set of methods are proposed which investigate the existence and role of visual navigation elements in a visual navigation system. A quantitative research methodology in the form of a series of systematic experiments is conducted on these methods. The thesis formulates, implements and analyzes the proposed methods in the context of visual navigation elements which are arranged into three major groupings; a) Spatial memory b) Motion Memory c) Manhattan, context and scene semantics. The investigations are carried out on multiple image datasets obtained by robot mounted cameras (2D/3D) moving in different environments. Spatial memory is investigated by evaluation of proposed place recognition methods. The recognized places and inter-place associations are then used to represent a visited set of places in the form of a topological map. Such a representation of places and their spatial associations models the concept of spatial memory. It resembles the humans’ ability of place representation and mapping for large environments (e.g. cities). Motion memory in a visual navigation system is analyzed by a thorough investigation of various motion estimation methods. This leads to proposals of direct motion estimation methods which compute accurate motion estimates by basing the estimation process on dominant surfaces. In everyday world, planar surfaces, especially the ground planes, are ubiquitous. Therefore, motion models are built upon this constraint. Manhattan structure provides geometrical cues which are helpful in solving navigation problems. There are some unique geometric primitives (e.g. planes) which make up an indoor environment. Therefore, a plane detection method is proposed as a result of investigations performed on scene structure. The method uses supervised learning to successfully classify the segmented clusters in 3D point-cloud datasets. In addition to geometry, the context of a scene also plays an important role in robustness of a visual navigation system. The context in which navigation is being performed imposes a set of constraints on objects and sections of the scene. The enforcement of such constraints enables the observer to robustly segment the scene and to classify various objects in the scene. A contextually aware scene segmentation method is proposed which classifies the image of a scene into a set of geometric classes. The geometric classes are sufficient for most of the navigation tasks. However, in order to facilitate the cognitive visual decision making process, the scene ought to be semantically segmented. The semantic of indoor scenes as well as semantic of the outdoor scenes are dealt with separately and separate methods are proposed for visual mapping of environments belonging to each type. An indoor scene consists of a corridor structure which is modeled as a cubic space in order to build a map of the environment. A “flash-n-extend” strategy is proposed which is responsible for controlling the map update frequency. The semantics of the outdoor scenes is also investigated and a scene classification method is proposed. The method employs a Markov Random Field (MRF) based classification framework which generates a set of semantic maps.

Since information technology has become a central part of businesses and organizations, the move to the cyber domain has benefitted them and endangered them with new threats through vulnerabilities. To minimize risks and prevent and alleviate cyber-attacks, using standards is common to ensure an organization's cybersecurity. With this increased focus on cybersecurity, new legal and regulatory requirements are created and published, mandatory for organizations to comply with. However, even if one is certified with a cybersecurity standard and complies with necessary legal and regulatory requirements, security breaches do occur, and mitigating vulnerabilities cannot be fully accomplished. With this, ontologies have increased in popularity to visualize and simplify how multiple entities within the domain are interconnected. However, none has interconnected vulnerabilities, standards, legal and regulatory requirements in one and studies propose new, unifying ontologies to be created to aid the domain in building new knowledge. Thus, this study aims to develop a security ontology to understand the relationship between vulnerabilities, standards, legal and regulatory requirements. The research question is written as: What is the relationship between vulnerabilities, standards, legal and regulatory requirements? Design science methodology is applied to the study, in which data is collected through document study and interviews and analyzed using document and content analysis. Based on the data collected, a security ontology presenting and visualizing the relationships between the different subjects implemented has been created. The artefact can be useful for security practitioners and newcomers to more in-depth understanding of how vulnerabilities are connected to controls and which controls can aid in being compliant with legal and regulatory requirements.

In Web service composition, service discovery and combining suitable services through determination of interoperability among different services are important operations. Utilizing semantics improves the quality and facilitates automation of these operations. There are several previous approaches for semantic service discovery and service matching. In this work, we exploit and extend these semantic approaches in order to make Web service composition process more facilitated, less error prone and more automated. This work includes a service discovery and service interoperability checking technique which extends the previous semantic matching approaches. In addition to this, as a guidance system for the user, a new semantic domain model is proposed that captures semantic relations between concepts in various ontologies.

abstract: Despite incremental improvements over decades, academic planning solutions see relatively little use in many industrial domains despite the relevance of planning paradigms to those problems. This work observes four shortfalls of existing academic solutions which contribute to this lack of adoption. To address these shortfalls this work defines model-independent semantics for planning and introduces an extensible planning library. This library is shown to produce feasible results on an existing benchmark domain, overcome the usual modeling limitations of traditional planners, and accommodate domain-dependent knowledge about the problem structure within the planning process. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-15T14:19:43Z No. of bitstreams: 2 Dissertação - Jáderson Araújo Gonçalves da Cruz - 2015.pdf: 7065271 bytes, checksum: e80c34d6de2772da64d2a3631fadcb3f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-10-15T14:21:28Z (GMT) No. of bitstreams: 2 Dissertação - Jáderson Araújo Gonçalves da Cruz - 2015.pdf: 7065271 bytes, checksum: e80c34d6de2772da64d2a3631fadcb3f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-10-15T14:21:28Z (GMT). No. of bitstreams: 2 Dissertação - Jáderson Araújo Gonçalves da Cruz - 2015.pdf: 7065271 bytes, checksum: e80c34d6de2772da64d2a3631fadcb3f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-06-15 / This paper proposes a database mapping to a semantic domain. This process consists of mapping a set of database, relational or NoSQL, for a pre-existing user-defined ontology. Subsequently the elements of these databases are linked to semantic repositories in order to produce a representation as linked open data. / Este trabalho apresenta uma proposta de mapeamento de bancos de dados para um domínio semântico. Esse processo consiste em mapear um conjunto de banco de dados, relacional ou NoSQL, para uma ontologia preexistente e definida pelo usuário. Subsequentemente os elementos desses bancos de dados são ligados a repositórios semânticos, a fim de produzir uma representação em formato de dado aberto ligado. Palavras–chave Repositório Semântico,

Managing agricultural processes with significant logistics sub-processes is a challenge because coordinating a distributed fleet in a dynamic environment is difficult without proper oversight in terms of qualitative and quantitative process information. Digital assistance systems are thought to aid agricultural practitioners by providing process-related information and thus support operational decision-making or even control the logistic flow (semi-)automatically. However, their development is currently stifled by a lack of monitoring capabilities during process execution. This thesis concerns the topic of online process monitoring for ongoing agricultural logistic processes. It discusses how to extract process knowledge from the telemetry of agricultural machines by applying spatio-semantic reasoning techniques. Our method combines spatial analysis for identifying spatial relationships between machines and their environment with semantic inference to derive formal process knowledge through ontological and rule-based reasoning. To test our concepts, we implemented a domain-agnostic semantic mapping framework and applied it in the context of forage maize harvesting. We present custom-made ontological models and rules to represent agricultural environments and to reason about machine actors and their process states. Based on our prototype, we demonstrate how to implement automated process and service tracking in near-real-time. Finally, we discuss the role of online process analytics systems in the context of other agricultural assistance systems for farm and fleet management.