@inproceedings{aglionby-teufel-2022-identifying,
title = "Identifying relevant common sense information in knowledge graphs",
author = "Aglionby, Guy and
Teufel, Simone",
editor = "Bosselut, Antoine and
Li, Xiang and
Lin, Bill Yuchen and
Shwartz, Vered and
Majumder, Bodhisattwa Prasad and
Lal, Yash Kumar and
Rudinger, Rachel and
Ren, Xiang and
Tandon, Niket and
Zouhar, Vil{\'e}m",
booktitle = "Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.csrr-1.1",
doi = "10.18653/v1/2022.csrr-1.1",
pages = "1--7",
abstract = "Knowledge graphs are often used to store common sense information that is useful for various tasks. However, the extraction of contextually-relevant knowledge is an unsolved problem, and current approaches are relatively simple. Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods. We additionally investigate methods to ensure that extracted triples form a connected graph. Graph connectivity is important for model interpretability, as paths are frequently used as explanations for the reasoning that connects question and answer.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="aglionby-teufel-2022-identifying">
<titleInfo>
<title>Identifying relevant common sense information in knowledge graphs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Guy</namePart>
<namePart type="family">Aglionby</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simone</namePart>
<namePart type="family">Teufel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Antoine</namePart>
<namePart type="family">Bosselut</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiang</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bill</namePart>
<namePart type="given">Yuchen</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vered</namePart>
<namePart type="family">Shwartz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bodhisattwa</namePart>
<namePart type="given">Prasad</namePart>
<namePart type="family">Majumder</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yash</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="family">Lal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rachel</namePart>
<namePart type="family">Rudinger</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiang</namePart>
<namePart type="family">Ren</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Niket</namePart>
<namePart type="family">Tandon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vilém</namePart>
<namePart type="family">Zouhar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Dublin, Ireland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Knowledge graphs are often used to store common sense information that is useful for various tasks. However, the extraction of contextually-relevant knowledge is an unsolved problem, and current approaches are relatively simple. Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods. We additionally investigate methods to ensure that extracted triples form a connected graph. Graph connectivity is important for model interpretability, as paths are frequently used as explanations for the reasoning that connects question and answer.</abstract>
<identifier type="citekey">aglionby-teufel-2022-identifying</identifier>
<identifier type="doi">10.18653/v1/2022.csrr-1.1</identifier>
<location>
<url>https://aclanthology.org/2022.csrr-1.1</url>
</location>
<part>
<date>2022-05</date>
<extent unit="page">
<start>1</start>
<end>7</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Identifying relevant common sense information in knowledge graphs
%A Aglionby, Guy
%A Teufel, Simone
%Y Bosselut, Antoine
%Y Li, Xiang
%Y Lin, Bill Yuchen
%Y Shwartz, Vered
%Y Majumder, Bodhisattwa Prasad
%Y Lal, Yash Kumar
%Y Rudinger, Rachel
%Y Ren, Xiang
%Y Tandon, Niket
%Y Zouhar, Vilém
%S Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F aglionby-teufel-2022-identifying
%X Knowledge graphs are often used to store common sense information that is useful for various tasks. However, the extraction of contextually-relevant knowledge is an unsolved problem, and current approaches are relatively simple. Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods. We additionally investigate methods to ensure that extracted triples form a connected graph. Graph connectivity is important for model interpretability, as paths are frequently used as explanations for the reasoning that connects question and answer.
%R 10.18653/v1/2022.csrr-1.1
%U https://aclanthology.org/2022.csrr-1.1
%U https://doi.org/10.18653/v1/2022.csrr-1.1
%P 1-7
Markdown (Informal)
[Identifying relevant common sense information in knowledge graphs](https://aclanthology.org/2022.csrr-1.1) (Aglionby & Teufel, CSRR 2022)
ACL