Efficient query processing techniques for next-page retrieval

Abstract

In top-k ranked retrieval the goal is to efficiently compute an ordered list of the highest scoring k documents according to some stipulated similarity function such as the well-known BM25 approach. In most implementation techniques a min-heap of size k is used to track the top scoring candidates. In this work we consider the question of how best to retrieve the second page of search results, given that a first page has already been computed; that is, identification of the documents at ranks $$k+1$$ k + 1 to 2k for some query. Our goal is to understand what information is available as a by-product of the first-page scoring, and how it can be employed to accelerate the second-page computation, assuming that the second-page of results is required for only a fraction of the query load. We propose a range of simple, yet efficient, next-page retrieval techniques which are suitable for accelerating Document-at-a-Time mechanisms, and demonstrate their performance on three large text collections.

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

“…For instance, the metadata of a document can be extracted using this approach [22][23][24][25][26][27]. A text extractor can be built using the AI technique for the automated extraction of key terms from a document [28][29][30][31][32][33][34]. An ontology-based dynamic information extraction framework identifies a wide range of document resources published in the scientific community and extracts the whole structural information [35][36][37][38][39][40][41].…”

“…In the Kingdom of Saudi Arabia (KSA), most organizations offer a sophisticated application for employees and stakeholders to share the information and valuable documents. The internal communication between employees generates a larger amount of documents [ 31 – 35 ]. Organizations demand an artificial intelligence (AI) based system to generate knowledge from the documents [ 36 – 41 ].…”

“…e internal communication between employees generates a larger amount of documents [31][32][33][34][35]. Organizations demand an arti cial intelligence (AI) based system to generate knowledge from the documents [36][37][38][39][40][41].…”