@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.",
}
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%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.