PMING distance: A collaborative semantic proximity measure

One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the flow of data and documents which are accessible from the Web is continuously fueled by the contribution of millions of users who interact digitally in a collaborative way. Search engines, continually exploring the Web, are therefore the natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is possible to generalize the semantic similarity, under the assumption that semantically similar terms behave similarly, and define collaborative proximity measures based on the indexing information returned by search engines. In this work PMING, a new collaborative proximity measure based on search engines, which uses the information provided by search engines, is introduced as a basis to extract semantic content. PMING is defined on the basis of the best features of other state-of-the-art proximity distances which have been considered. It defines the degree of relatedness between terms, by using only the number of documents returned as result for a query, then the measure dynamically reflects the collaborative change made on the web resources. Experiments held on popular collaborative and generalist engines (e.g. Flickr, Youtube, Google, Bing, Yahoo Search) show that PMING outperforms state-of-the-art proximity measures (e.g. Normalized Google Distance, Flickr Distance etc.), in modeling contexts, modeling human perception, and clustering of semantic associations.