Relations expansion: Extracting relationship instances from the web

Haibo Li; Yutaka Matsuo; Mitsuru Ishizuka

doi:10.1109/WI-IAT.2010.269

Relations expansion: Extracting relationship instances from the web

Haibo Li^*, Yutaka Matsuo, Mitsuru Ishizuka

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.

Original language	English
Title of host publication	Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Pages	184-187
Number of pages	4
Volume	1
DOIs	https://doi.org/10.1109/WI-IAT.2010.269
Publication status	Published - 2010
Externally published	Yes
Event	2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 - Toronto, ON Duration: 2010 Aug 31 → 2010 Sept 3

Other

Other	2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
City	Toronto, ON
Period	10/8/31 → 10/9/3

Keywords

Relation extraction
Semi-supervised learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Software

Access to Document

10.1109/WI-IAT.2010.269

Cite this

@inproceedings{942240a1293348849e00be1654bcd155,

title = "Relations expansion: Extracting relationship instances from the web",

abstract = "In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.",

keywords = "Relation extraction, Semi-supervised learning",

author = "Haibo Li and Yutaka Matsuo and Mitsuru Ishizuka",

year = "2010",

doi = "10.1109/WI-IAT.2010.269",

language = "English",

isbn = "9780769541914",

volume = "1",

pages = "184--187",

booktitle = "Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010",

note = "2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 ; Conference date: 31-08-2010 Through 03-09-2010",

}

TY - GEN

T1 - Relations expansion

T2 - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010

AU - Li, Haibo

AU - Matsuo, Yutaka

AU - Ishizuka, Mitsuru

PY - 2010

Y1 - 2010

N2 - In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.

AB - In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models the entire data set as a weighted graph and the label score is propagated on this graph. We test the proposed framework with four relationships, the results show that the label propagation is quite competitive comparing with existing methods.

KW - Relation extraction

KW - Semi-supervised learning

UR - http://www.scopus.com/inward/record.url?scp=78649824397&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649824397&partnerID=8YFLogxK

U2 - 10.1109/WI-IAT.2010.269

DO - 10.1109/WI-IAT.2010.269

M3 - Conference contribution

AN - SCOPUS:78649824397

SN - 9780769541914

VL - 1

SP - 184

EP - 187

BT - Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010

Y2 - 31 August 2010 through 3 September 2010

ER -

Relations expansion: Extracting relationship instances from the web

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this