Fusing Automatically Extracted Semantic Annotations
This event took place on Wednesday 26 July 2006 at 11:30
Andriy Nikolov Computing Research Centre, The Open University, UK
The necessary precondition of the Semantic Web initiative is the availability of semantic data. Information, which at the moment is intended for human users, must be translated into a machine-readable format (RDF). Such a translation process is called semantic annotation. The amount of information on the Web makes it impossible to solve the annotation task manually. So the usage of automatic information extraction algorithms is essential. These algorithms use various natural language processing and machine learning techniques to extract information from text. The information extracted from different sources must then be integrated in a knowledge base, so that it can be queried in a uniform way. This integration process is called knowledge fusion. However, performing knowledge fusion encounters a number of problems. The origins of these problems are the following: 1. Inaccuracy of existing information extraction algorithms leads to appearance of incorrect annotations. 2. Information contained on the web pages can be imprecise, incomplete or vague. 3. Multiple sources can contradict each other. Thus, in order to perform large-scale automatic annotation it is necessary to implement a knowledge fusion procedure, which is able to deal with these problems.
This event took place on Wednesday 26 July 2006 at 11:30
The necessary precondition of the Semantic Web initiative is the availability of semantic data. Information, which at the moment is intended for human users, must be translated into a machine-readable format (RDF). Such a translation process is called semantic annotation. The amount of information on the Web makes it impossible to solve the annotation task manually. So the usage of automatic information extraction algorithms is essential. These algorithms use various natural language processing and machine learning techniques to extract information from text. The information extracted from different sources must then be integrated in a knowledge base, so that it can be queried in a uniform way. This integration process is called knowledge fusion. However, performing knowledge fusion encounters a number of problems. The origins of these problems are the following: 1. Inaccuracy of existing information extraction algorithms leads to appearance of incorrect annotations. 2. Information contained on the web pages can be imprecise, incomplete or vague. 3. Multiple sources can contradict each other. Thus, in order to perform large-scale automatic annotation it is necessary to implement a knowledge fusion procedure, which is able to deal with these problems.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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