Entity-based Stochastic Analysis of Search Results for Query Expansion and Results Re-Ranking

Abstract

In this paper we introduce a method for exploiting entities from the emerging Web of Data for enhancing various Information Retrieval (IR) services. The approach is based on named-entity recognition applied in a set of search results, and on a graph of documents and identified entities that is constructed dynamically and analyzed stochastically using a Random Walk method. The result of this analysisis exploited in two different contexts: for automatic query expansion and for re-ranking a set of retrieved results. Evaluation results in the 2015 TREC Clinical Decision Support Track illustrate that query expansion can increase recall by retrieving more relevant hits, while re-ranking can notably improve the ranked list of results by moving relevant but low-ranked hits in higher positions.

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Document Details

Document Type
Technical Report
Publication Date
Nov 20, 2015
Accession Number
AD1004754

Entities

People

  • Pavlos Fafalios
  • Yannis Tzitzikas

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Applied Computer Science
  • Artificial Intelligence
  • Computer Science
  • Hard Copy
  • Mathematics
  • Named Entity Recognition
  • Probability
  • Random Walk
  • Recognition
  • Stochastic Processes
  • Test And Evaluation
  • Transitions

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Information Retrieval

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval