5,780 research outputs found
Developing an ontological sandbox : investigating multi-level modellingâs possible Metaphysical Structures
One of the central concerns of the multi-level modelling (MLM) community is the hierarchy of classifications that appear in conceptual models; what these are, how they are linked and how they should be organised into levels and modelled. Though there has been significant work done in this area, we believe that it could be enhanced by introducing a systematic way to investigate the ontological nature and requirements that underlie the frameworks and tools proposed by the community to support MLM (such as Orthogonal Classification Architecture and Melanee). In this paper, we introduce a key component for the investigation and understanding of the ontological requirements, an ontological sandbox. This is a conceptual framework for investigating and comparing multiple variations of possible ontologies â without having to commit to any of them â isolated from a full commitment to any foundational ontology. We discuss the sandbox framework as well as walking through an example of how it can be used to investigate a simple ontology. The example, despite its simplicity, illustrates how the constructional approach can help to expose and explain the metaphysical structures used in ontologies, and so reveal the underlying nature of MLM levelling
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The National Transport Data Framework
Report by Professor Peter Landshoff (Cambridge University) and
Professor John Polak (Imperial College London) on a project for
the Department for Transport.
emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions
into a central catalogue, so that people can search for data and find data
and understand their characteristics. The value of this is to individuals, to
commercial organizations, and to public bodies. For example, services that
provide better information to travellers will help to make their journey
less stressful and persuade them to make more use of public transport.
Transport operators need very diverse information to help them
plan developments to their services: demographic, geographical, economic etc.
And policy makers need a similar range of information to help them decide
how to divide their budget and afterwards to evaluate how valuable it has
been.This work was supported by the Department for Transport (DfT)
Building a Disciplinary, World-Wide Data Infrastructure
Sharing scientific data, with the objective of making it fully discoverable,
accessible, assessable, intelligible, usable, and interoperable, requires work
at the disciplinary level to define in particular how the data should be
formatted and described. Each discipline has its own organization and history
as a starting point, and this paper explores the way a range of disciplines,
namely materials science, crystallography, astronomy, earth sciences,
humanities and linguistics get organized at the international level to tackle
this question. In each case, the disciplinary culture with respect to data
sharing, science drivers, organization and lessons learnt are briefly
described, as well as the elements of the specific data infrastructure which
are or could be shared with others. Commonalities and differences are assessed.
Common key elements for success are identified: data sharing should be science
driven; defining the disciplinary part of the interdisciplinary standards is
mandatory but challenging; sharing of applications should accompany data
sharing. Incentives such as journal and funding agency requirements are also
similar. For all, it also appears that social aspects are more challenging than
technological ones. Governance is more diverse, and linked to the discipline
organization. CODATA, the RDA and the WDS can facilitate the establishment of
disciplinary interoperability frameworks. Being problem-driven is also a key
factor of success for building bridges to enable interdisciplinary research.Comment: Proceedings of the session "Building a disciplinary, world-wide data
infrastructure" of SciDataCon 2016, held in Denver, CO, USA, 12-14 September
2016, to be published in ICSU CODATA Data Science Journal in 201
A Configurable Matchmaking Framework for Electronic Marketplaces
E-marketplaces constitute a major enabler of B2B and B2C e-commerce activities. This paper proposes a framework for one of the central activities of e-marketplaces: matchmaking of trading intentions lodged by market participants. The framework identifies a core set of concepts and functions that are common to all types of marketplaces and can serve as the basis for describing the distinct styles of matchmaking employed within various market mechanisms. A prototype implementation of the framework based on Web services technology is presented, illustrating its ability to be dynamically configured to meet specific market needs and its potential to serve as a foundation for more fully fledged e-marketplace frameworks
An agent-based architecture for managing the provision of community care - the INCA (Intelligent Community Alarm) experience
Community Care is an area that requires extensive cooperation
between independent agencies, each of which needs to meet its own objectives and targets. None are engaged solely in the delivery of community care, and need to integrate the service with their other responsibilities in a coherent and efficient manner. Agent technology provides the means by which effective cooperation can take place without compromising the essential security of both the client and the
agencies involved as the appropriate set of responses can be generated through negotiation between the parties without the need for access to the main information repositories that would be necessary with conventional collaboration models. The autonomous nature of agents also means that a variety of agents can cooperate
together with various local capabilities, so long as they conform to the relevant messaging requirements. This allows a variety of agents, with capabilities tailored to the carers to which they are attached to be developed so that cost-effective solutions can be provided.
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A Semantically Enabled Service Architecture for Mashups over Streaming and Stored Data
Sensing devices are increasingly being deployed to monitor
the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. good emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a good emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor-Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application
An Ontological Basis for Design Methods
This paper presents a view of design methods as process artefacts that can be represented using the function-behaviour-structure (FBS) ontology. This view allows identifying five fundamental approaches to methods: black-box, procedural, artefact-centric, formal and managerial approaches. They all describe method structure but emphasise different aspects of it. Capturing these differences addresses common terminological confusions relating to methods. The paper provides an overview of the use of the fundamental method approaches for different purposes in designing. In addition, the FBS ontology is used for developing a notion of prescriptiveness of design methods as an aggregate construct defined along four dimensions: certainty, granularity, flexibility and authority. The work presented in this paper provides an ontological basis for describing, understanding and managing design methods throughout their life cycle.
Keywords:
Design Methods; Function-Behaviour-Structure (FBS) Ontology; Prescriptive Design Knowledge</p
An Ontology for Defect Detection in Metal Additive Manufacturing
A key challenge for Industry 4.0 applications is to develop control systems
for automated manufacturing services that are capable of addressing both data
integration and semantic interoperability issues, as well as monitoring and
decision making tasks. To address such an issue in advanced manufacturing
systems, principled knowledge representation approaches based on formal
ontologies have been proposed as a foundation to information management and
maintenance in presence of heterogeneous data sources. In addition, ontologies
provide reasoning and querying capabilities to aid domain experts and end users
in the context of constraint validation and decision making. Finally,
ontology-based approaches to advanced manufacturing services can support the
explainability and interpretability of the behaviour of monitoring, control,
and simulation systems that are based on black-box machine learning algorithms.
In this work, we provide a novel ontology for the classification of
process-induced defects known from the metal additive manufacturing literature.
Together with a formal representation of the characterising features and
sources of defects, we integrate our knowledge base with state-of-the-art
ontologies in the field. Our knowledge base aims at enhancing the modelling
capabilities of additive manufacturing ontologies by adding further defect
analysis terminology and diagnostic inference features
Ontology based semantic-predictive model for reconfigurable automation systems
Due to increasing product variety and complexity, capability to support reconfiguration is a key competitiveness indicator for current automation system within large enterprises. Reconfigurable manufacturing systems could efficiently reuse existing knowledge in order to decrease the required skills and design time to launch new products. However, most of the software tools developed to support design of reconfigurable manufacturing system lack integration of product, process and resource knowledge, and the design data is not transferred from domain-specific engineering tools to a collaborative and intelligent platform to capture and reuse design knowledge. The focus of this research study is to enable integrated automation systems design to support a knowledge reuse approach to predict process and resource changes when product requirements change. The proposed methodology is based on a robust semantic-predictive model supported by ontology representations and predictive algorithms for the integration of Product, Process, Resource and Requirement (PPRR) data, so that future automation system changes can be identified at early design stages
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
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