Knowledge tv

Lino, Natasha Correia Queiroz; Siebra, Clauirton; Araújo, Jonatas; Anabuki, Davi; Patrício, José; Batista, Marcelle; Nóbrega, Ramon; Amaro, Manoel; Lemos, Guido

doi:10.1145/2000119.2000126

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…O projeto Knowledge TV (KTV) [7] propõe uma camada semântica para prover serviços na plataforma da TV Digital, estendendo a visão da Web Semântica para o domínio da TV Digital, adaptandose as suas restrições e apresentando novos desafios na busca de um ambiente de convergência digital. Essa extensão permite outras possibilidades: (i) o compartilhamento e reutilização de dados em aplicações; (ii) processamento automatizado de informações compartilhadas; (iii) conexões semânticas entre dados e objetos do mundo real, etc.…”

Section: Arquitetura Knowledge Tvunclassified

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MDM-KTV

Martins

Lino

2012

Proceedings of the 18th Brazilian Symposium on Multimedia and the Web

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For developing applications and services in the Knowledge TV platform there is a need in structuring, organising and standardising data according to consolidating academic and industry models, however inserted in the context of interactive Digital TV (iDTV). This work presents research carried on the implementation of the Conceptual Framework MDM-KTV, which means Multi Data Models KTV, considering operational, analytical, semantic and Linked Data based data, in the context of the Knowledge TV project.

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Section: Arquitetura Knowledge Tvunclassified

“…A Figura 1 mostra a arquitetura conceitual do projeto Knowledge TV, com os principais módulos da Camada Semântica. Na referência [7] são encontradas informações detalhadas sobre a arquitetura e projeto KTV.…”

Section: Arquitetura Knowledge Tvunclassified

MDM-KTV

Martins

Lino

2012

Proceedings of the 18th Brazilian Symposium on Multimedia and the Web

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First Workshop on Artificial Intelligence in Education to Support the Social Inclusion of Communities (AIEDSIC)

Akhras¹,

Brna

2011

Lecture Notes in Computer Science

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Summary. About 20 years ago, in a paper entitled "Computational Mathetics: the Missing Link of Artificial Intelligence in Education", John Self argued that AI in Education has missed its connection with formal AI, its theoretical side. Some people argued that this was necessary so that AI in Education (AIED) could be able to deliver real world applications. However, in the real world, half of the population lives with less than 3 dollars a day with many socially excluded from education, health and other basic services. Social inclusion seeks to address the needs of this population, mostly living in underdeveloped countries, and also combat factors that are socially problematic in developed countries such as poor educational attainment, unemployment, poor health/special needs, low income, crime and poor housing/local environment.The AI in Education community has spent more than 30 years researching the design of adaptive technologies to support learning. However, the issue of supporting social inclusion has never been directly addressed. Has AI in Education also missed an important connection with the real world? We argue that AI in Education systems have a challenging role to play in helping to transform communities but we also accept that much has to be done to establish the ways in which work on AI in Education supports such activities indirectly, and to determine what future work needs to be done.The European Union made 2010 the European Year For Combating Poverty and Social Exclusion. The key objectives were to improve public awareness and commitment at the political level to fight poverty and social exclusion while some key challenges are: to eradicate child poverty by breaking the vicious circle of intergenerational inheritance, to promote the active inclusion in the society and the labour market of the most vulnerable groups, to overcome discrimination and increase the integration of people with disabilities, ethnic minorities and immigrants and other vulnerable groups. We can start by focusing on AIED's capacity to support these aims. Therefore, the main purpose of this workshop is to identify and discuss the challenges that arise in addressing issues of supporting the social inclusion of communities in the context of AI in Education research and lay the groundwork for future workshops in this area.

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