Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

A Creative Data Ontology for the Moving Image Industry

The moving image industry produces an extremely large amount of data and associated metadata for each media creation project, often in the range of terabytes. The current methods used to organise, track, and retrieve the metadata are inadequate, with metadata often being hard to find. The aim of this thesis is to explore whether there is a practical use case for using ontologies to manage metadata in the moving image industry and to determine whether an ontology can be designed for such a purpose and can be used to manage metadata more efficiently to improve workflows. It presents a domain ontology, hereby referred to as the Creative Data Ontology, engineered around a set of metadata fields provided by Evolutions, Double Negative (DNEG), and Pinewood Studios, and four use cases. The Creative Data Ontology is then evaluated using both quantitative methods and qualitative methods (via interviews) with domain and ontology experts.Our findings suggest that there is a practical use case for an ontology-based metadata management solution in the moving image industry. However, it would need to be presented carefully to non-technical users, such as domain experts, as they are likely to experience a steep learning curve. The Creative Data Ontology itself meets the criteria for a high-quality ontology for the sub-sectors of the moving image industry domain that it provides coverage for (i.e. scripted film and television, visual effects, and unscripted television) and it provides a good foundation for expanding into other sub-sectors of the industry, although it cannot yet be considered a ``standard'' ontology. Finally, the thesis presents the methodological process taken to develop the Creative Data Ontology and the lessons learned during the ontology engineering process which can be valuable guidance for designers and developers of future metadata ontologies. We believe such guidance could be transferable across many domains where an ontology of metadata is required, which are unrelated to the moving image industry. Future research may focus on assisting non-technical users to overcome the learning curve, which may also also applicable to other domains that may choose to use ontologies in the future

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

Ontology-based knowledge management for technology intensive industries

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Building web service ontologies

Harmelen, F.A.H. van [Promotor]Stuckenschmidt, H. [Copromotor

Análisis de los criterios de relevancia documental mediante consultas de información en el entorno web

La búsqueda de información no se entiende sin los motores de búsqueda web. Ante una demanda de información los buscadores web ordenan los resultados de forma que las páginas web más relevantes para la consulta aparezcan en las primeras posiciones. Esto genera un alto grado de competitividad entre las páginas web por obtener mejores asignaciones de relevancia por parte de los buscadores. Por norma general, los usuarios suelen consultar sólo los primeros resultados que devuelve un motor de búsqueda, en consecuencia ocupar estos puestos se traduce en mayor prestigio y visibilidad. Por tanto, la percepción de relevancia documental web por parte de los usuarios está intrínsecamente unida a los motores de búsqueda. En este trabajo se propone y desarrolla una metodología para determinar la relevancia documental web de forma automática, que se puede interpretar como: predicción automática de la posición que otorgaría un motor de búsqueda a un documento web entre los resultados de una consulta. La investigación se completa identificando los factores considerados en el posicionamiento web, a partir del estudio de herramientas empleadas en la optimización y promoción de páginas web. También se analiza el peso de cada uno de estos factores en los algoritmos de ordenación de los buscadores. Finalmente, en relación a las capacidades adquiridas para emular el comportamiento de los motores de búsqueda se propone un método de optimización web que estima previamente la rentabilidad del proceso. De esta forma no se invertirá en una campaña de promoción si los pronósticos de mejora del posicionamiento no se juzgan adecuados