Tenth ACM International Conference on Web Search and Data Mining , 2017-02-06

Title : ( Document Retrieval Model Through Semantic Linking )

Authors: F Ensan , Ebrahim Bagheri ,

Access to full-text not allowed by authors

Citation: BibTeX | EndNote

This paper addresses the task of document retrieval based on the degree of document relatedness to the meanings of a query by presenting a semantic-enabled language model. Our model relies on the use of semantic linking systems for forming a graph representation of documents and queries, where nodes represent concepts extracted from documents and edges represent semantic relatedness between concepts. Based on this graph, our model adopts a probabilistic reasoning model for calculating the conditional probability of a query concept given values assigned to document concepts. We present an integration framework for interpolating other retrieval systems with the presented model in this paper. Our empirical experiments on a number of TREC collections show that the semantic retrieval has a synergetic impact on the results obtained through state of the art keyword-based approaches, and the consideration of semantic information obtained from entity linking on queries and documents can complement and enhance the performance of other retrieval models.

Keywords

, Semantic Search, Language models, Semantic
برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@inproceedings{paperid:1062202,
author = {Ensan, F and Ebrahim Bagheri},
title = {Document Retrieval Model Through Semantic Linking},
booktitle = {Tenth ACM International Conference on Web Search and Data Mining},
year = {2017},
location = {Cambridge, ENGLAND},
keywords = {Semantic Search; Language models; Semantic linking},
}

[Download]

%0 Conference Proceedings
%T Document Retrieval Model Through Semantic Linking
%A Ensan, F
%A Ebrahim Bagheri
%J Tenth ACM International Conference on Web Search and Data Mining
%D 2017

[Download]