and documented as part of the integration of WordNet 1.7 into the cooperatively updatable knowledge base of WebKB-2, most projects claiming to re-use WordNet for knowledge-based applications (typically, knowledge-oriented information retrieval) simply re-use it directly. WordNet has also been converted to a formal specification, by means of a hybrid bottom-up top-down methodology to automatically extract association relations from WordNet, and interpret these associations in terms of a set of conceptual relations, formally defined in the DOLCE foundational ontology. In most works that claim to have integrated WordNet into ontologies, the content of WordNet has not simply been corrected when it seemed necessary; instead, WordNet has been heavily re-interpreted and updated whenever suitable. This was the case when, for example, the top-level ontology of WordNet was re-structured according to the OntoClean based approach or when WordNet was used as a primary source for constructing the lower classes of the SENSUS ontology. Limitations The most widely discussed limitation of WordNet (and related resources like ImageNet) is that some of the semantic relations are more suited to concrete concepts than to abstract concepts. For example, it is easy to create hyponyms/hypernym relationships to capture that a "conifer" is a type of "tree", a "tree" is a type of "plant", and a "plant" is a type of "organism", but it is difficult to classify emotions like "fear" or "happiness" into equally deep and well-defined hyponyms/hypernym relationships. Many of the concepts in WordNet are specific to certain languages and the most accurate reported mapping between languages is 94%. Synonyms, hyponyms, meronyms, and antonyms occur in all languages with a WordNet so far, but other semantic relationships are language-specific. This limits the interoperability across languages. However, it also makes WordNet a resource for highlighting and studying the differences between languages, so it is not necessarily a limitation for all use cases. WordNet does not include information about the etymology or the pronunciation of words and it contains only limited information about usage. WordNet aims to cover most everyday words and does not include much domain-specific terminology. WordNet is the most commonly used computational lexicon of English for word-sense disambiguation (WSD), a task aimed to assigning the context-appropriate meanings (i.e. synset members) to words in a text. However, it has been argued that WordNet encodes sense distinctions that are too fine-grained. This issue prevents WSD systems from achieving a level of performance comparable to that of humans,