Human translation is based on linguistic and extralinguistic knowledge. Despite promising pioneering advances, knowledge-based machine translation has remained a tempting vision. The bottleneck has been the engineering of sufficiently comprehensive bodies of relevant knowledge The Semantic Web offers opportunities for the gradual evolution of a global heterogeneous knowledge base. The immediate target has been the modelling of certain knowledge domains by practical ontologies. In the talk we will demonstrate the utilization of ontological knowledge indifferent crosslingual applications reaching from crosslingual document retrieval via crosslingual question answering to complex information services involving several crosslingual functionalities, including machine translation. We will then discuss the ramifications of this development and of the evolution of the World Wide Web for future directions in both statistical and rule-based machine translation.

Natural Language is considered the friendliest way of man-machine communication. However the implementation of natural language interfaces faces often the problem of lack of linguistic and world-knowledge, especially when the application domain is not very specific. This is exactly the case of Web-based applications, which aim to serve for retrieval of information in every-day areas of work. The recent Semantic Web activities had as consequence the development of large ontologies for a broad spectrum of domains, as well as of mechanisms for annotating the resources with semantic information. In this paper we present a new architecture aiming to bring together the advantages of natural language querying and the power of semantic W eb. W e will show also how described application can be easily adapted for other domains.

In this paper we give an overview of Semantic Web technologies and the impact of these ones for multilingual Web. We present a possible solution for improving the quality of on-line translation systems, using mechanisms and standards from Semantic Web. We focus on Example based machine translation and the automatization of the translation examples extraction by means of RDF-repositories.

The extraction of lexical sets from a corpus in Digital Signal Processing (DSP) has been detailed before on general sets, with direct ELT applications. In this contribution, a more specialized set is investigated to illustrate the possibility of actually using the results in more "intelligent" Text-Processing.

In this paper we present the actions we made to prepare an EBMT system to be integrated into the Semantic Web. We also described briefly the developed EBMT tool for translators.