Learning to rank from distant supervision: Exploiting noisy redundancy for relational entity search

Mianwei Zhou, Hongning Wang, Kevin Chen Chuan Change

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent information relevant to the desired relation. To explore useful information from such noisy redundancy, we abstract the task as a distantly supervised ranking problem - based on coarse entity-level annotations, deriving a relation-specific ranking function for the purpose of online searching. As the key challenge, without detailed snippet-level annotations, we have to learn an entity ranking function that can effectively filter noise; furthermore, the ranking function should also be online executable. We develop Pattern-based Filter Network (PFNet), a novel probabilistic graphical model, as our solution. To balance the accuracy and efficiency requirements, PFNet selects a limited size of indicative patterns to filter noisy snippets, and inverted indexes are utilized to retrieve required features. Experiments on the large scale CuleWeb09 data set for six different relations confirm the effectiveness of the proposed PFNet model, which outperforms five state-of-the-art relational entity ranking methods.

Original languageEnglish (US)
Title of host publicationICDE 2013 - 29th International Conference on Data Engineering
Pages829-840
Number of pages12
DOIs
StatePublished - Aug 15 2013
Event29th International Conference on Data Engineering, ICDE 2013 - Brisbane, QLD, Australia
Duration: Apr 8 2013Apr 11 2013

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other29th International Conference on Data Engineering, ICDE 2013
CountryAustralia
CityBrisbane, QLD
Period4/8/134/11/13

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Zhou, M., Wang, H., & Change, K. C. C. (2013). Learning to rank from distant supervision: Exploiting noisy redundancy for relational entity search. In ICDE 2013 - 29th International Conference on Data Engineering (pp. 829-840). [6544878] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2013.6544878