Data Conceptualization for Semantic Search Diversification over XML Data

Deciding the context of a search query at an earlier stage is an important process in keyword searches. The proposed Semantic Search Diversification Model (SSDM) measures the relevance and novelty of the search on account of Smallest Lowest Common Ancestor (SLCA) nodes for the query terms and their corresponding semi features available in the given XML Data tree. The Probabilistic weight of the search query is calculated based on the obtained relevancy and novelty after the SLCA extraction and Mutual evaluation of query features. The proposed Anchor based Semantic Preservation (AESP) algorithm extracts the weighted SLCA nodes for the original and generated queries from the given XML data and determines the anchor nodes to initiate tree pruning. It assigns the node lists to multiple processors in order to achieve parallelism. The fuzzy set of the popular terms in the given data set is used for creating semantic table for the proposed system to perform fuzzy semantic matching and hence to achieve fast computation. The Normalized Discounted Cumulative Gain (nDCG) measures of the proposed work show usefulness of the query suggestions and decides a point of time in search to go for an annotation process to conduct.