@inproceedings{sudhahar-etal-2019-reasoning,
title = "Reasoning Over Paths via Knowledge Base Completion",
author = "Sudhahar, Saatviga and
Pierleoni, Andrea and
Roberts, Ian",
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Jansen, Peter and
Glava\v s, Goran and
Riedl, Martin and
Surdeanu, Mihai and
Vazirgiannis, Michalis",
booktitle = "Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5320/",
doi = "10.18653/v1/D19-5320",
pages = "164--171",
abstract = "Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60\% of the time within the top 10 ranked paths and achieve 49\% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sudhahar-etal-2019-reasoning">
<titleInfo>
<title>Reasoning Over Paths via Knowledge Base Completion</title>
</titleInfo>
<name type="personal">
<namePart type="given">Saatviga</namePart>
<namePart type="family">Sudhahar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andrea</namePart>
<namePart type="family">Pierleoni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ian</namePart>
<namePart type="family">Roberts</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2019-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dmitry</namePart>
<namePart type="family">Ustalov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Swapna</namePart>
<namePart type="family">Somasundaran</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peter</namePart>
<namePart type="family">Jansen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Goran</namePart>
<namePart type="family">Glavaš</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Martin</namePart>
<namePart type="family">Riedl</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mihai</namePart>
<namePart type="family">Surdeanu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michalis</namePart>
<namePart type="family">Vazirgiannis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hong Kong</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.</abstract>
<identifier type="citekey">sudhahar-etal-2019-reasoning</identifier>
<identifier type="doi">10.18653/v1/D19-5320</identifier>
<location>
<url>https://aclanthology.org/D19-5320/</url>
</location>
<part>
<date>2019-11</date>
<extent unit="page">
<start>164</start>
<end>171</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Reasoning Over Paths via Knowledge Base Completion
%A Sudhahar, Saatviga
%A Pierleoni, Andrea
%A Roberts, Ian
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Jansen, Peter
%Y Glavaš, Goran
%Y Riedl, Martin
%Y Surdeanu, Mihai
%Y Vazirgiannis, Michalis
%S Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F sudhahar-etal-2019-reasoning
%X Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.
%R 10.18653/v1/D19-5320
%U https://aclanthology.org/D19-5320/
%U https://doi.org/10.18653/v1/D19-5320
%P 164-171
Markdown (Informal)
[Reasoning Over Paths via Knowledge Base Completion](https://aclanthology.org/D19-5320/) (Sudhahar et al., TextGraphs 2019)
ACL
- Saatviga Sudhahar, Andrea Pierleoni, and Ian Roberts. 2019. Reasoning Over Paths via Knowledge Base Completion. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 164–171, Hong Kong. Association for Computational Linguistics.