What is it about?
In particular, in this work we focus on concept-based query expansion schemes where, in order to effectively increase the precision of web document retrieval and to decrease the users’ browsing time, the main goal is to quickly provide users with the most suitable query expansion. Proximity measures as NGD, PMI, Confidence, PMING are used.
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Why is it important?
several semantic approaches to concept-based query expansion and re-ranking schemes are studied and compared with different ontology-based expansion methods in web document search and retrieval.
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This page is a summary of: Collective Evolutionary Concept Distance Based Query Expansion for Effective Web Document Retrieval, January 2013, Springer Science + Business Media,
DOI: 10.1007/978-3-642-39649-6_47.
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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.
Heuristic semantic walk for concept chaining in collaborative networks
The proposed framework is based on the novel notion of heuristic semantic walk (HSW). In the HSW framework, a semantic proximity measure among concepts, reflecting the collective knowledge embedded in search engines or other statistical sources, is used as a heuristic in order to guide the search in the collaborative network. Different search strategies, information sources and proximity measures, can be used to adapt HSW to the collaborative semantic network under consideration.
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