What is it about?
In this study a new approach is proposed, to guide navigation over a collaborative concept network, in order to discover path between concepts. The method uses a semantic heuristic based on proximity measures, which reflects the collective knowledge embedded in search engines. The experiments were held on the Wikipedia network and Bing search.
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Why is it important?
Path search between concepts over a semantic network is an issue of great interest for many applications, such as explanation generation and query expansion. In this study a new approach is proposed, to guide navigation over a collaborative concept network, in order to discover path between concepts.
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This page is a summary of: Heuristic Semantic Walk, January 2013, Springer Science + Business Media,
DOI: 10.1007/978-3-642-39649-6_46.
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Resources
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 are therefore the natural source of information on which to base a modern approach to semantics.
PMING Distance: A Collaborative Semantic Proximity Measure
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 reflects the collaborative change.
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