WordNet: a lexical database for English
Communications of the ACM
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
International Journal of Human-Computer Studies
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Hypernym discovery based on distributional similarity and hierarchical structures
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
BabelNet: building a very large multilingual semantic network
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning word-class lattices for definition and hypernym extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
MENTA: inducing multilingual taxonomies from wikipedia
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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In the last years, the vision of the Semantic Web has led to many approaches that aim to automatically derive knowledge bases from Wikipedia. These approaches rely mostly on the English Wikipedia as it is the largest Wikipedia version and have lead to valuable knowledge bases. However, each Wikipedia version contains socio-cultural knowledge, i.e. knowledge with specific relevance for a culture or language. One difficulty of the application of existing approaches to multiple Wikipedia versions is the use of additional corpora. In this paper, we describe the adaptation of existing heuristics that make the extraction of large sets of hyponymy relations from multiple Wikipedia versions with little information about each language possible. Further, we evaluate our approach with Wikipedia versions in four different languages and compare results with GermaNet for German and WordNet for English.