Non-monotonic generation of knowledge paths for context understanding

Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task....

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Main Authors: LO, Pei-chi, LIM, Ee-peng
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/8326
https://ink.library.smu.edu.sg/context/sis_research/article/9329/viewcontent/3627994_pvoa_cc_nc.pdf
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spelling sg-smu-ink.sis_research-93292024-04-08T07:27:59Z Non-monotonic generation of knowledge paths for context understanding LO, Pei-chi LIM, Ee-peng Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and path well-formedness. This paper designs a two-stage framework the comprising of the following: (1) a knowledge-enabled embedding matching and learning-to-rank with multi-head self attention context extractor to determine a set of context entities relevant to both the query entities and context document, and (2) a non-monotonic path generation method with pretrained transformer to generate high quality contextual paths. Our experiment results on two real-world datasets show that our best performing CPG model successfully recovers 84.13% of ground truth contextual paths, outperforming the context window baselines. Finally, we demonstrate that non-monotonic model generates more well-formed paths compared to the monotonic counterpart. 2024-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8326 info:doi/10.1145/3627994 https://ink.library.smu.edu.sg/context/sis_research/article/9329/viewcontent/3627994_pvoa_cc_nc.pdf http://creativecommons.org/licenses/by-nc-sa/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University information retrieval knowledge graph contextual path generation generation model Databases and Information Systems Management Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic information retrieval
knowledge graph
contextual path generation
generation model
Databases and Information Systems
Management Information Systems
Numerical Analysis and Scientific Computing
spellingShingle information retrieval
knowledge graph
contextual path generation
generation model
Databases and Information Systems
Management Information Systems
Numerical Analysis and Scientific Computing
LO, Pei-chi
LIM, Ee-peng
Non-monotonic generation of knowledge paths for context understanding
description Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and path well-formedness. This paper designs a two-stage framework the comprising of the following: (1) a knowledge-enabled embedding matching and learning-to-rank with multi-head self attention context extractor to determine a set of context entities relevant to both the query entities and context document, and (2) a non-monotonic path generation method with pretrained transformer to generate high quality contextual paths. Our experiment results on two real-world datasets show that our best performing CPG model successfully recovers 84.13% of ground truth contextual paths, outperforming the context window baselines. Finally, we demonstrate that non-monotonic model generates more well-formed paths compared to the monotonic counterpart.
format text
author LO, Pei-chi
LIM, Ee-peng
author_facet LO, Pei-chi
LIM, Ee-peng
author_sort LO, Pei-chi
title Non-monotonic generation of knowledge paths for context understanding
title_short Non-monotonic generation of knowledge paths for context understanding
title_full Non-monotonic generation of knowledge paths for context understanding
title_fullStr Non-monotonic generation of knowledge paths for context understanding
title_full_unstemmed Non-monotonic generation of knowledge paths for context understanding
title_sort non-monotonic generation of knowledge paths for context understanding
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/8326
https://ink.library.smu.edu.sg/context/sis_research/article/9329/viewcontent/3627994_pvoa_cc_nc.pdf
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