to participate in the creation of an open semantic knowledge base, Wikipedia has the chance to become a resource of semantic statements, hitherto unknown regarding size, scope, openness, and internationalisation. These semantic enhancements bring to Wikipedia benefits of today’s semantic technologies: more

Abstract: Enhancing Wikipedia by means of semantic representations seems to be a promising issue. From a formal or technical point of view there are no major obstacles in the way. Nevertheless, a close look at Wikipedia, its structure and contents reveals that some questions have to be answered

Wikipedia is the world’s largest collaboratively edited source of encyclopaedic knowledge. But its contents are barely machineinterpretable. Structural knowledge, e. g. about how concepts are interrelated, can neither be formally stated nor automatically processed. Also the wealth of numerical data

Abstract: Enhancing Wikipedia by means of semantic representations seems to be a promising issue. From a formal or technical point of view there are no major obstacles in the way. Nevertheless, a close look at Wikipedia, its structure and contents reveals that some questions have to be answered

Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from

based on human generated arguments, such as deliberative democracy, business negotiation, or self-care. The ARGNET system was developed based on ther Semantic MediaWiki framework and on the Argument Interchange Format (AIF) ontology.

WonPrize). The facts have been automatically extracted from the unification of Wikipedia and WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like

This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles

Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking

This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process