%0 Thesis %1 hessthesis %A Heß, Andreas %C Dublin, Ireland %D 2006 %K machine-learning semantic-web hess %T Supervised and Unsupervised Ensemble Learning for the Semantic Web %U http://www.few.vu.nl/~andreas/publications/hess-thesis.pdf %X The World Wide Web offers an ocean of information and services for everyone, and many of us rely on it on a daily basis. One of the reasons for the Web's success is that the threshold for creating content on the Web was always very low, because comfortable and easy to use HTML editors are available. However, today's Web is limited by the fact that it is machine-readable, but not machine-understandable. The Semantic Web promises a solution to this problem by adding explicit semantics with the goal of making the Web machine-understandable by using description logics and ontologies. However, the threshold for creating this extra markup is very high, and no comfortable tools exist at present. The goal of this work is to develop such tools. Machine Learning techniques have been used in literature for various classification tasks. The central claim of this thesis is that such algorithms, supervised and unsupervised, can be used for this purpose.

@phdthesis{hessthesis, abstract = {The World Wide Web offers an ocean of information and services for everyone, and many of us rely on it on a daily basis. One of the reasons for the Web's success is that the threshold for creating content on the Web was always very low, because comfortable and easy to use HTML editors are available. However, today's Web is limited by the fact that it is machine-readable, but not machine-understandable. The Semantic Web promises a solution to this problem by adding explicit semantics with the goal of making the Web machine-understandable by using description logics and ontologies. However, the threshold for creating this extra markup is very high, and no comfortable tools exist at present. The goal of this work is to develop such tools. Machine Learning techniques have been used in literature for various classification tasks. The central claim of this thesis is that such algorithms, supervised and unsupervised, can be used for this purpose.}, added-at = {2006-10-05T21:40:04.000+0200}, address = {Dublin, Ireland}, author = {He{\ss}, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2e6825edaf58e6c6f2e6273f23f610b04/gromgull}, interhash = {2c519a186bc6983f14b3d442f6cff8e0}, intrahash = {e6825edaf58e6c6f2e6273f23f610b04}, keywords = {machine-learning semantic-web hess}, month = {February}, school = {School of Computer Science and Informatics, University College Dublin}, timestamp = {2006-10-05T21:40:04.000+0200}, title = {Supervised and Unsupervised Ensemble Learning for the Semantic Web}, url = {http://www.few.vu.nl/~andreas/publications/hess-thesis.pdf}, year = 2006 }