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Author, Editor

Author(s):

Erdős, Dóra
Miettinen, Pauli

dblp
dblp

Not MPG Author(s):

Erdős, Dóra

Editor(s):

Nejdl, Wolfgang
Pei, Jian
Rastogi, Rajeev

dblp
dblp
dblp

Not MPII Editor(s):

Nejdl, Wolfgang
Pei, Jian
Rastogi, Rajeev

BibTeX cite key*:

erdos13discovering

Title, Booktitle

Title*:

Discovering Facts with Boolean Tensor Tucker Decomposition


p1569-erdos-2.pdf (1068.99 KB)

Booktitle*:

22nd ACM international conference on information & knowledge management (CIKM '13)

Event, URLs

URL of the conference:

http://www.cikm2013.org

URL for downloading the paper:


Event Address*:

Burlingame, USA

Language:

English

Event Date*
(no longer used):


Organization:

Association for Computing Machinery (ACM)

Event Start Date:

27 October 2013

Event End Date:

1 November 2013

Publisher

Name*:

ACM

URL:


Address*:

New York, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

1569-1572

Year*:

2013

VG Wort Pages:


ISBN/ISSN:

978-1-4503-2263-8

Sequence Number:


DOI:

10.1145/2505515.2507846



Note, Abstract, ©


(LaTeX) Abstract:

Open Information Extraction (Open IE) has gained increasing research
interest in recent years. The first step in Open IE is to extract raw subject--predicate--object triples from the data. These raw triples are rarely usable per se, and need additional post-processing. To that end, we proposed the use of Boolean Tucker tensor decomposition to simultaneously find the entity and relation synonyms and the facts connecting them from the raw triples. Our method represents the synonym sets and facts using (sparse) binary matrices and tensor that can be efficiently stored and manipulated.

We consider the presentation of the problem as a Boolean tensor decomposition as one of this paper's main contributions. To study the validity of this approach, we use a recent algorithm for scalable Boolean Tucker decomposition. We validate the results with empirical evaluation on a new semi-synthetic data set, generated to faithfully reproduce real-world data features, as well as with real-world data from existing Open IE extractor. We show that our method obtains high precision while the low recall can easily be remedied by considering the original data together with the decomposition.

Keywords:

Open Information Extraction, Tensor decomposition, Boolean tensor decomposition, Entity disambiguation, Tucker3 decomposition



Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Audience:

experts only

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{erdos13discovering,
AUTHOR = {Erdős, D{\"o}ra and Miettinen, Pauli},
EDITOR = {Nejdl, Wolfgang and Pei, Jian and Rastogi, Rajeev},
TITLE = {Discovering Facts with Boolean Tensor Tucker Decomposition},
BOOKTITLE = {22nd ACM international conference on information & knowledge management (CIKM '13)},
PUBLISHER = {ACM},
YEAR = {2013},
ORGANIZATION = {Association for Computing Machinery (ACM)},
PAGES = {1569--1572},
ADDRESS = {Burlingame, USA},
ISBN = {978-1-4503-2263-8},
DOI = {10.1145/2505515.2507846},
}


Entry last modified by Pauli Miettinen, 01/30/2014
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Editor(s)
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Created
01/24/2014 06:39:58 PM
Revision
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Editor
Pauli Miettinen
Pauli Miettinen


Edit Date
24.01.2014 18.49.13
24.01.2014 18.39.58


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