Paper Info
Title
Text Representation, Retrieval, and Understanding with Knowledge Graphs
Abstract
This dissertation aims to improve text representation, retrieval, and understanding with knowledge graphs. Previous information retrieval systems were mostly built upon bag-ofwords representations and frequency-based retrieval models. Effective as they are, wordbased representations and frequency signals only provide shallow text understanding and have various intrinsic challenges. Utilizing entities and their structured semantics from knowledge graphs, this dissertation goes beyond bag-of-words and improves search with richer text representations, customized semantic structures, sophisticated ranking models and neural networks.
Year
DOI
Venue
2018
10.1145/3308774.3308808
ACM SIGIR Forum
Field
DocType
Volume
Knowledge graph,Ranking,Information retrieval,Computer science,Artificial neural network,Semantics
Journal
52
Issue
ISSN
Citations 
2
0163-5840
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Chen-Yan Xiong140530.82