Group items tagged

Augmented Wordnets These lexical resources (and the method of their construction) are described in Semantic Taxonomy Induction from Heterogenous Evidence (ACL-06). They are automatically augmented versions of WordNet 2.1

The YAGO-NAGA project started in 2006 with the goal of building a conveniently searchable, large-scale, highly accurate knowledge base of common facts in a machine-processible representation. We have already harvested knowledge about millions of entities and facts about their relationships, from Wikipedia and WordNet with careful integration of these two sources. The resulting knowledge base, coined YAGO, has very high precision and is freely available. The facts are represented as RDF triples, and we have developed methods and prototype systems for querying, ranking, and exploring knowledge. Our search engine NAGA provides ranked answers to queries based on statistical models.

Andreas Hotho's talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches: 1. Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce "siblings") 2. Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)

YAGO is a huge semantic knowledge base. Currently, YAGO knows more than 2 million entities (like persons, organizations, cities, etc.). It knows 20 million facts about these entities. Unlike many other automatically assembled knowledge bases, YAGO has a manually confirmed accuracy of 95%

Visuwords™ online graphical dictionary - Look up words to find their meanings and associations with other words and concepts. Produce diagrams reminiscent of a neural net. Learn how words associate.