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A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article presents the results of a systematic review of machine translation approaches that rely on Semantic Web technologies for translating texts. Overall, our survey suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.

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Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.

Product-related information can be found in various data sources and formats across the product lifecycle. Effectively exploiting this information requires the federation of these sources, the extraction of implicit information, and the efficient access to this comprehensive knowledge base. Existing solutions for product information management (PIM) are usually restricted to structured information, but most of the business-critical information resides in unstructured documents. We present a generic architecture for federating heterogeneous information from various sources, including the Internet of Things, and argue how this process benefits from using semantic representations. A reference implementation tailor-made to business users is explained and evaluated. We also discuss several issues we experienced that we believe to be valuable for researchers and implementers of semantic information systems, as well as the information retrieval community.