7,365 research outputs found

    Automatic tagging and geotagging in video collections and communities

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    Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

    Automatic domain ontology extraction for context-sensitive opinion mining

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    Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

    A novel Big Data analytics and intelligent technique to predict driver's intent

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    Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

    Enriching ontological user profiles with tagging history for multi-domain recommendations

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    Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
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