Taxonomy for search engines

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Taxonomy of entities for search engines are designed to improve relevance in vertical search. Taxonomies of entities are trees whose nodes are labelled with entities which expect to occur in a web search query. These trees are used to match keywords from search query with the keywords from answers (or snippets).

Taxonomies, thesauri and concept hierarchies are crucial components for many applications of Information Retrieval, Natural Language Processing and Knowledge Management. However, building, tuning and managing taxonomies and ontologies is rather costly since a lot of manual operations are required. A number of studies proposed the automated building of taxonomies based on linguistic resources and/or statistical machine learning [1]

Web mining is one of the approach to build search engine taxonomies for web search. The taxonomy construction process starts from the seed entities and mines available source domains for new entities associated with these seed entities. New entities are formed by applying the machine learning to the current web search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration [2]

References[edit]

  1. ^ Vicient C. , Sánchez D., Moreno A.. An automatic approach for ontology-based feature extraction from heterogeneous textual resources. Engineering Applications of Artificial Intelligence. 2013;26(3):1092–1106. doi:http://dx.doi.org/10.1016/j.engappai.2012.08.002.
  2. ^ Galitsky B. Transfer learning of syntactic structures for building taxonomies for search engines. Engineering Applications of Artificial Intelligence. 2013;26(10):2504–2515. doi:http://dx.doi.org/10.1016/j.engappai.2013.08.010.