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One of the main problems that emerges in the classic approach to semantics isthe difficulty in acquisition and maintenance of ontologies and semanticannotations. On the other hand, the Internet explosion and the massivediffusion of mobile smart devices lead to the creation of a worldwide system,which information is daily checked and fueled by the contribution of millionsof users who interacts in a collaborative way. Search engines, continuallyexploring the Web, are a natural source of information on which to base amodern approach to semantic annotation. A promising idea is that it is possibleto generalize the semantic similarity, under the assumption that semanticallysimilar terms behave similarly, and define collaborative proximity measuresbased on the indexing information returned by search engines. The PMINGDistance is a proximity measure used in data mining and information retrieval,which collaborative information express the degree of relationship between twoterms, using only the number of documents returned as result for a query on asearch engine. In this work, the PMINIG Distance is updated, providing a novelformal algebraic definition, which corrects previous works. The novel point ofview underlines the features of the PMING to be a locally normalized linearcombination of the Pointwise Mutual Information and Normalized Google Distance.The analyzed measure dynamically reflects the collaborative change made on theweb resources.

Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining