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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Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.
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