Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Semantic Brokering of Multimedia Contents for Smart Delivery of Ubiquitous Services in Pervasive Environments

With the proliferation of modern mobile devices having the capability to interact each other and with the environment in a transparent manner, there is an increase in the development of those applications that are specifically designed for pervasive and ubiquitous environments. Those applications are able to provide a service of interest for the user that depends on context information, such as the user's position, his preferences, the capability of the device and its available resources. Services have to respond in a rational way in many different situations choosing the actions with the best expected result by the user, so making environment not only more connected and efficient, but smarter. Here we present a semantic framework that provides the technology for the development of intelligent, context aware services and their delivery in pervasive and ubiquitous environments

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

Anotação semântica para recomendação de conteúdos educacionais

Orientador: Julio Cesar dos ReisDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sistemas de apoio à aprendizagem exploram diversos recursos multimídia para considerar individualidades do aluno bem com diferentes estilos de aprendizagem. Todavia, a crescente quantidade de conteúdos educacionais disponíveis em diferentes formatos e de maneira fragmentada di?culta o acesso e compreensão dos conceitos em estudo. Embora a literatura tenha proposto abordagens para explorar técnicas de recomendação que permitem representação explícita de semântica por meio de artefatos como ontologias, essa linha não foi totalmente explorada e ainda requer muitos esforços de pesquisa. Esta pesquisa objetiva conceber um método de recomendação de conteúdo educacional explorando o uso de anotações semânticas sobre transcrições textuais de videoaulas. As anotações servem como metadados que expressam o signi?cado de trechos das aulas. A técnica de recomendação, como principal contribuição esperada, fundamenta-se nas anotações disponíveis para de?nir estratégias de ranking de conteúdos disponíveis a partir da proximidade semântica dos conceitos combinadas com técnicas de aprendizagem de máquina. A contribuição envolve o desenvolvimento de protótipos funcionais de software para validação experimental com base em conteúdos de videoaulas reais e deve destacar as principais vantagens e limitações da abordagem. Os resultados obtidos permitirão o acesso à recomendações mais adequadas para melhorar o processo de aprendizagem apresentando a possibilidade de uma experiência mais satisfatória pelos alunosAbstract: Learning support systems explore several audio-visual resources to consider individual needs and learning styles aiming to stimulate learning experiences. However, the large amount of online educational content in di?erent formats and the possibility of making them available in a fragmented way turns di?cult the tasks of accessing these resources and understanding the concepts under study. Although literature has proposed approachestoexploreexplicitsemanticrepresentationthroughartifactssuchasontologies in learning support systems, this research line still requires further investigation e?orts. In this MS.c. dissertation, we propose a method for recommending educational content by exploring the use of semantic annotations over textual transcriptions from video lectures. Our investigation addresses the di?culties in extracting entities from natural language texts in subtitles of videos. Our work studies how to re?ne concepts in a domain ontology to support semantic annotation of video lecture subtitles. We report on the design of a video lecture recommendation system which explores the extracted semantic annotations. Our solution explored semantically annotated videos with an ontology in the Computer Science domain. Obtained results indicate our recommendation mechanism is suited to ?lter relevant video content in di?erent use scenariosMestradoCiência da ComputaçãoMestre em Ciência da Computação2017/02325-5; 2018/00313-2FAPES

prototypical implementations ; working packages in project phase II

In this technical report, we present the concepts and first prototypical imple- mentations of innovative tools and methods for personalized and contextualized (multimedia) search, collaborative ontology evolution, ontology evaluation and cost models, and dynamic access and trends in distributed (semantic) knowledge. The concepts and prototypes are based on the state of art analysis and identified requirements in the CSW report IV