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Automatic Term recognition (ATR) is a fundamental processing step preceding more complex tasks such as semantic search and ontology learning. From a large number of methodologies available in the literature only a few are able to handle both single and multi-word terms. In this paper we present a comparison of five such algorithms and propose a combined(More)
Natural Language is a mean to express and discuss about concepts, objects, events, i.e. it carries semantic contents. One of the ultimate aims of Natural Language Processing techniques is to identify the meaning of the text, providing effective ways to make a proper linkage between textual references and their referents, that is real world objects. This(More)
Linked Data is a gigantic, constantly growing and extremely valuable resource, but its usage is still heavily dependent on (i) the familiarity of end users with RDF’s graph data model and its query language, SPARQL, and (ii) knowledge about available datasets and their contents. Intelligent keyword search over Linked Data is currently being investigated as(More)
Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks. Exiting methods exploit semantic evidence from a single knowledge source, and are predominantly evaluated only in the general domain. This paper introduces a method of harnessing different knowledge sources under a uniform model for(More)
Natural Language is a mean to express and discuss about concepts, objects, events, i.e. it carries semantic contents. The Semantic Web aims at tightly coupling contents with their precise meanings. One of the ultimate roles of Natural Language Processing techniques is identifying the meaning of the text, providing effective ways to make a proper linkage(More)