143 resultados para FOAF Ontology


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The city and the urban condition, popular subjects of art, literature, and film, have been commonly represented as fragmented, isolating, violent, with silent crowds moving through the hustle and bustle of a noisy, polluted cityspace. Included in this diverse artistic field is children’s literature—an area of creative and critical inquiry that continues to play a central role in illuminating and shaping perceptions of the city, of city lifestyles, and of the people who traverse the urban landscape. Fiction’s textual representations of cities, its sites and sights, lifestyles and characters have drawn on traditions of realist, satirical, and fantastic writing to produce the protean urban story—utopian, dystopian, visionary, satirical—with the goal of offering an account or critique of the contemporary city and the urban condition. In writing about cities and urban life, children’s literature variously locates the child in relation to the social (urban) space. This dialogic relation between subject and social space has been at the heart of writings about/of the flâneur: a figure who experiences modes of being in the city as it transforms under the influences of modernism and postmodernism. Within this context of a changing urban ontology brought about by (post)modern styles and practices, this article examines five contemporary picture books: The Cows Are Going to Paris by David Kirby and Allen Woodman; Ooh-la-la (Max in love) by Maira Kalman; Mr Chicken Goes to Paris and Old Tom’s Holiday by Leigh Hobbs; and The Empty City by David Megarrity. I investigate the possibility of these texts reviving the act of flânerie, but in a way that enables different modes of being a flâneur, a neo-flâneur. I suggest that the neo-flâneur retains some of the characteristics of the original flâneur, but incorporates others that take account of the changes wrought by postmodernity and globalization, particularly tourism and consumption. The dual issue at the heart of the discussion is that tourism and consumption as agents of cultural globalization offer a different way of thinking about the phenomenon of flânerie. While the flâneur can be regarded as the precursor to the tourist, the discussion considers how different modes of flânerie, such as the tourist-flâneur, are an inevitable outcome of commodification of the activities that accompany strolling through the (post)modern urban space.

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We report and reflect upon the early stages of a research project that endeavours to establish a culture of critical design thinking in a tertiary game design course. We first discuss the current state of the Australian game industry and consider some perceived issues in game design courses and graduate outcomes. The second sec-tion presents our response to these issues: a project in progress which uses techniques originally exploited by Augusto Boal in his work, Theatre of the Oppressed. We appropriate Boal’s method to promote critical design thinking in a games design class. Finally, we reflect on the project and the ontology of design thinking from the perspective of Bruce Archer’s call to reframe design as a ‘third academic art’.

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Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aim The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.

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The Australian e-Health Research Centre and Queensland University of Technology recently participated in the TREC 2011 Medical Records Track. This paper reports on our methods, results and experience using a concept-based information retrieval approach. Our concept-based approach is intended to overcome specific challenges we identify in searching medical records. Queries and documents are transformed from their term-based originals into medical concepts as de ned by the SNOMED-CT ontology. Results show our concept-based approach performed above the median in all three performance metrics: bref (+12%), R-prec (+18%) and Prec@10 (+6%).

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The five articles appearing in this issue reflect the depth of project management research in terms of delineating and clarifying the different philosophical positions, advancing the concepts, and applying innovative research methods. These articles focus on the ontology of project management research (“Foundations of Project Management Research: An Explicit and Six-Facet Ontological Framework” by Gauthier and Ika), project management practices relevant to different types of projects from a practitioner’s perspective (“An Empirical Identification of Project Management Toolsets and a Comparison Among Project Types” by Besner and Hobbs), the effect of project management processes on project performance (“Project Management Knowledge and Effects on Construction Project Outcomes: An Empirical Study” by Chou and Yang), determining the success metrics at different stages of a project (“A Perspective Based Understanding of Project Success” by McLeod, Doolin, and MacDonell), and identifying project success parameters and critical success factors from the point of view of different project actors in largescale projects (“Forecasting Success on Large Projects: Developing Reliable Scales to Predict Multiple Perspectives by Multiple Stakeholders Over Multiple Time Frames” by Turner and Zolin), and understanding project success from the points of view of different project stakeholders...

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This thesis reports on an interview study with 17 international students about their experiences of coming to belong in an Australian university. All used English as an additional language (EAL). The students’ narratives of ‘coming to belong’ are conceptualised through the theory of Bourdieu, in particular the concepts of field, capital, habitus and legitimation; and the methodological premises of critical realism’s layered ontology. The literature review argues that access to and accrual of a range of capital is critical to successful adaptation to a new educational system. This, and processes of legitimation by others in the fields, affects the senses of belonging for students of various linguistic backgrounds, of different countries of origin, studying from primary to higher education in diverse parts of the world. Data were collected by semi-structured interviews and email dialogues at three points during the students’ first year of study in Australia. The analysis shows how the students’ empirical experiences were ordered in terms of narrative structure—orientation, complication, evaluation, resolution and coda—and highlight the emotions generated by the sequence of events. The findings show that EAL international students sought new field positions through legitimation in multiple senses across (sub-)fields. They also show that academic, social and linguistic legitimacy granted by others produced a spectrum of belonging: in the centre, at the margin, and/or to meaningful intercultural encounters. This study makes a contribution to the growing literature around the experience of international students in higher education, and to empirical literature using Bourdieu to understand educational relations.

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The Australian e-Health Research Centre and Queensland University of Technology recently participated in the TREC 2012 Medical Records Track. This paper reports on our methods, results and experience using an approach that exploits the concept and inter-concept relationships defined in the SNOMED CT medical ontology. Our concept-based approach is intended to overcome specific challenges in searching medical records, namely vocabulary mismatch and granularity mismatch. Queries and documents are transformed from their term-based originals into medical concepts as defined by the SNOMED CT ontology, this is done to tackle vocabulary mismatch. In addition, we make use of the SNOMED CT parent-child `is-a' relationships between concepts to weight documents that contained concept subsumed by the query concepts; this is done to tackle the problem of granularity mismatch. Finally, we experiment with other SNOMED CT relationships besides the is-a relationship to weight concepts related to query concepts. Results show our concept-based approach performed significantly above the median in all four performance metrics. Further improvements are achieved by the incorporation of weighting subsumed concepts, overall leading to improvement above the median of 28% infAP, 10% infNDCG, 12% R-prec and 7% Prec@10. The incorporation of other relations besides is-a demonstrated mixed results, more research is required to determined which SNOMED CT relationships are best employed when weighting related concepts.

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BACKGROUND: Broccoli consumption has been associated with a reduced risk of prostate cancer. Isothiocyanates (ITCs) derived from glucosinolates that accumulate in broccoli are dietary compounds that may mediate these health effects. Sulforaphane (SF, 4-methylsulphinylbutyl ITC) derives from heading broccoli (calabrese) and iberin (IB, 3-methylsulphinypropyl ITC) from sprouting broccoli. While there are many studies regarding the biological activity of SF, mainly undertaken with cancerous cells, there are few studies associated with IB. METHODS: Primary epithelial and stromal cells were derived from benign prostatic hyperplasia tissue. Affymetrix U133 Plus 2.0 whole genome arrays were used to compare global gene expression between these cells, and to quantify changes in gene expression following exposure to physiologically appropriate concentrations of SF and IB. Ontology and pathway analyses were used to interpret results. Changes in expression of a subset of genes were confirmed by real-time RT-PCR. RESULTS: Global gene expression profiling identified epithelial and stromal-specific gene expression profiles. SF induced more changes in epithelial cells, whereas IB was more effective in stromal cells. Although IB and SF induced different changes in gene expression in both epithelial and stromal cells, these were associated with similar pathways, such as cell cycle and detoxification. Both ITCs increased expression of PLAGL1, a tumor suppressor gene, in stromal cells and suppressed expression of the putative tumor promoting genes IFITM1, CSPG2, and VIM in epithelial cells. CONCLUSION: These data suggest that IB and SF both alter genes associated with cancer prevention, and IB should be investigated further as a potential chemopreventative agent.

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This paper presents a graph-based method to weight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations between concepts. This representation naturally captures dependencies between concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations. We apply existing graph-based term weighting methods to weight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as well as encapsulates terms belonging to a single medical entity into a single concept. In addition, we further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain. Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.

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Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.

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Building information modeling (BIM) is an emerging technology and process that provides rich and intelligent design information models of a facility, enabling enhanced communication, coordination, analysis, and quality control throughout all phases of a building project. Although there are many documented benefits of BIM for construction, identifying essential construction-specific information out of a BIM in an efficient and meaningful way is still a challenging task. This paper presents a framework that combines feature-based modeling and query processing to leverage BIM for construction. The feature-based modeling representation implemented enriches a BIM by representing construction-specific design features relevant to different construction management (CM) functions. The query processing implemented allows for increased flexibility to specify queries and rapidly generate the desired view from a given BIM according to the varied requirements of a specific practitioner or domain. Central to the framework is the formalization of construction domain knowledge in the form of a feature ontology and query specifications. The implementation of our framework enables the automatic extraction and querying of a wide-range of design conditions that are relevant to construction practitioners. The validation studies conducted demonstrate that our approach is significantly more effective than existing solutions. The research described in this paper has the potential to improve the efficiency and effectiveness of decision-making processes in different CM functions.

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Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

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In order to comprehend user information needs by concepts, this paper introduces a novel method to match relevance features with ontological concepts. The method first discovers relevance features from user local instances. Then, a concept matching approach is developed for matching these features to accurate concepts in a global knowledge base. This approach is significant for the transition of informative descriptor and conceptional descriptor. The proposed method is elaborately evaluated by comparing against three information gathering baseline models. The experimental results shows the matching approach is successful and achieves a series of remarkable improvements on search effectiveness.

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Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.

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In recent years, there has been a growing interest from the design and construction community to adopt Building Information Models (BIM). BIM provides semantically-rich information models that explicitly represent both 3D geometric information (e.g., component dimensions), along with non-geometric properties (e.g., material properties). While the richness of design information offered by BIM is evident, there are still tremendous challenges in getting construction-specific information out of BIM, limiting the usability of these models for construction. In this paper, we describe our approach for extracting construction-specific design conditions from a BIM model based on user-defined queries. This approach leverages an ontology of features we are developing to formalize the design conditions that affect construction. Our current implementation analyzes the component geometry and topological relationships between components in a BIM model represented using the Industry Foundation Classes (IFC) to identify construction features. We describe the reasoning process implemented to extract these construction features, and provide a critique of the IFC’s to support the querying process. We use examples from two case studies to illustrate the construction features, the querying process, and the challenges involved in deriving construction features from an IFC model.