A conceptual investigation of the ontological commensurability of spatial data infrastructures among different cultures

Humans think and communicate in very flexible and schematic ways, and a Spatial Data Infrastructure (SDI) for the Amazon and associated information system ontologies should reflect this flexibility and the adaptive nature of human cognition in order to achieve semantic interoperability. In this paper I offer a conceptual investigation of SDI and explore the nature of cultural schemas as expressions of indigenous ontologies and the challenges of semantic interoperability across cultures. Cultural schemas are, in essence, our ontologies, but they are markedly different than classical formal ontologies. They shape our ontological commitments to what exists in the world as well as the ways in which we approach and engage the world. And while they help structure our understanding of the world in which we are embedded, they are associative and flexible. They help to focus our attention to particular details of our experiences and give them salience, yet they cannot be simply reduced to a series of extracted features. They allow us to make meaning of the contextualized, cultural experience in which we are always immersed. An SDI is a shared social-technological-informational structure that, if it is to be useful and successful for sustainability in the Amazon, must incorporate and use indigenous cultural schemas. Indigenous communities must have the ability to contribute to the collection of geospatial data and their contributions recognized as legitimate forms of knowledge. In order for the SDI to work, it must recognize the larger cultural landscape to which cultural schemas can connect to the ready-to-hand elements of salient cultural experiences

Modelling biochemical pathways through enhanced π-calculus

AbstractWe use the π-calculus to model the evolution of biochemical systems, taking advantage of their similarities with global computation applications. First, we present a reduction semantics for the π-calculus from which causality and concurrency can be mechanically derived. We prove that our semantics agrees with the causal definitions presented in the literature. We also extend our semantics to model biological compartments. Then, we show the applicability of our proposal on a couple of biological examples

A light-weight concept ontology for annotating digital music.

In the recent time, the digital music items on the internet have been evolving to an enormous information space where we try to find/locate the piece of information of our choice by means of search engine. The current trend of searching for music by means of music consumers' keywords/tags is unable to provide satisfactory search results; and search and retrieval of music may be potentially improved if music metadata is created from semantic information provided by association of end-users' tags with acoustic metadata which is easy to extract automatically from digital music items. Based on this observation, our research objective was to investigate how music producers may be able to annotate music against MPEG-7 description (with its acoustic metadata) to deliver meaningful search results. In addressing this question, we investigated the potential of multimedia ontologies to serve as backbone for annotating music items and prospective application scenarios of semantic technologies in the digital music industry. We achieved with our main contribution under this thesis is the first prototype of mpeg-7Music annotation ontology that establishes a mapping of end-users tags with MPEG-7 acoustic metadata as well as extends upper level multimedia ontologies with end-user tags. Additionally, we have developed a semi-automatic annotation tool to demonstrate the potential of the mpeg-7Music ontology to serve as light weight concept ontology for annotating digital music by music producers. The proposed ontology has been encoded in dominant semantic web ontology standard OWL1.0 and provides a standard interoperable representation of the generated semantic metadata. Our innovations in designing the semantic annotation tool were focussed on supporting the music annotation vocabulary (i.e. the mpeg-7Music) in an attempt to turn the music metadata information space to a knowledgebase

Project Final Report Final Publishable Summary Report

This document is the final publishable summary report, part of the CONNECT final report

Approximate Query Answering Based on Topological Neighborhood and Semantic Similarity in OpenStreetMap

In this paper we focus on a pictorial query language, referred to as Geographical Pictorial Query Language (GeoPQL), and we revise its formal semantics by considering the polygon-polyline, polyline-polyline, and polygon-polygon topological relationships. This work proposes the Approximate Answering Engine (AAE) within a Distributed System, referred to as GeoPQLJSON (GeoPQLJ). The AAE provides approximate answers to query with empty results by following two directions: the Operator Conceptual Neighborhood (OCN) graph, and the OpenStreetMap (OSM) attribute hierarchy, giving maximum flexibility to the user choices. According to the former, the geo-operators of the queries can be replaced with the ones labeling the adjacent nodes of the OCN graph. By following the latter, the system evaluates the OSM attribute semantic similarity according to the information content approach, and proposes possible attribute replacements to the user. Note that the presence of OSM attributes allows the quick and direct access to large amount of geographical data, without requiring in our case the use of the topological elements. The functionalities of the Distributed GeoPQLJ System are illustrated by several query examples

Semantics and planning based workflow composition and execution for video processing

Traditional workflow systems have several drawbacks, e.g. in their inabilities to rapidly react to changes, to construct workflow automatically (or with user involvement) and to improve performance autonomously (or with user involvement) in an incremental manner according to specified goals. Overcoming these limitations would be highly beneficial for complex domains where such adversities are exhibited. Video processing is one such domain that increasingly requires attention as larger amounts of images and videos are becoming available to persons who are not technically adept in modelling the processes that are involved in constructing complex video processing workflows. Conventional video and image processing systems, on the other hand, are developed by programmers possessing image processing expertise. These systems are tailored to produce highly specialised hand-crafted solutions for very specific tasks, making them rigid and non-modular. The knowledge-based vision community have attempted to produce more modular solutions by incorporating ontologies. However, they have not been maximally utilised to encompass aspects such as application context descriptions (e.g. lighting and clearness effects) and qualitative measures. This thesis aims to tackle some of the research gaps yet to be addressed by the workflow and knowledge-based image processing communities by proposing a novel workflow composition and execution approach within an integrated framework. This framework distinguishes three levels of abstraction via the design, workflow and processing layers. The core technologies that drive the workflow composition mechanism are ontologies and planning. Video processing problems provide a fitting domain for investigating the effectiveness of this integratedmethod as tackling such problems have not been fully explored by the workflow, planning and ontological communities despite their combined beneficial traits to confront this known hard problem. In addition, the pervasiveness of video data has proliferated the need for more automated assistance for image processing-naive users, but no adequate support has been provided as of yet. A video and image processing ontology that comprises three sub-ontologies was constructed to capture the goals, video descriptions and capabilities (video and image processing tools). The sub-ontologies are used for representation and inference. In particular, they are used in conjunction with an enhanced Hierarchical Task Network (HTN) domain independent planner to help with performance-based selection of solution steps based on preconditions, effects and postconditions. The planner, in turn, makes use of process models contained in a process library when deliberating on the steps and then consults the capability ontology to retrieve a suitable tool at each step. Two key features of the planner are the ability to support workflow execution (interleaves planning with execution) and can perform in automatic or semi-automatic (interactive) mode. The first feature is highly desirable for video processing problems because execution of image processing steps yield visual results that are intuitive and verifiable by the human user, as automatic validation is non trivial. In the semiautomaticmode, the planner is interactive and prompts the user tomake a tool selection when there is more than one tool available to perform a task. The user makes the tool selection based on the recommended descriptions provided by the workflow system. Once planning is complete, the result of applying the tool of their choice is presented to the user textually and visually for verification. This plays a pivotal role in providing the user with control and the ability to make informed decisions. Hence, the planner extends the capabilities of typical planners by guiding the user to construct more optimal solutions. Video processing problems can also be solved in more modular, reusable and adaptable ways as compared to conventional image processing systems. The integrated approach was evaluated on a test set consisting of videos originating from open sea environment of varying quality. Experiments to evaluate the efficiency, adaptability to user’s changing needs and user learnability of this approach were conducted on users who did not possess image processing expertise. The findings indicate that using this integrated workflow composition and execution method: 1) provides a speed up of over 90% in execution time for video classification tasks using full automatic processing compared to manual methods without loss of accuracy; 2) is more flexible and adaptable in response to changes in user requests (be it in the task, constraints to the task or descriptions of the video) than modifying existing image processing programs when the domain descriptions are altered; 3) assists the user in selecting optimal solutions by providing recommended descriptions