Modeling Paths for Explainable Knowledge Base Completion
Open accessAbstract
A common approach in knowledge base completion (KBC) is to learn representations for entities and relations in order to infer missing facts by generalizing existing ones.A shortcoming of standard models is that they do not explain their predictions to make them verifiable easily to human inspection.In this paper, we propose the context path model (CPM) which generates explanations for new facts in KBC by providing sets of context paths as supporting evidence for these triples.For example, a new triple (Theresa May, nationality, Britain) may be explained by the path (Theresa May, born in, Eastbourne, contained in, Britain).The CPM is formulated as a wrapper that can be applied on top of various existing KBC models.We evaluate it for the well-established TransE model.We observe that its performance remains very close despite the added complexity, and that most of the paths proposed as explanations provide meaningful evidence to assess the correctness.
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93.8% of people in Freebase do not have place of birth, and 78.5% of people do not have nationality.
“Besides, according to the statistics in (Stadelmaier and Padó, 2019), 93.8% of people in Freebase do not have place of birth, and 78.5% of people do not have nationality”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Stadelmaier and Padó, 2019
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Cites
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“Explain-ability of system outputs is increasingly recognized as an important component in the practical use of NLP, and more generally, AI systems (Holzinger et al., 2017; Ras et al., 2018; Ribeiro et al., 2018).”
BackgroundWhat do we need to build explainable AI systems for the medical domain?
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Cited by
Sorted by citations
Implicit Relation Linking for Question Answering over Knowledge Graph
Zhao, Y., Huang, J., Hu, W., Chen, Q., Qiu, X., Huo, C., & Ren, W. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.312 · Cited by 5 · see citing works
- Authors
- Stadelmaier, J., & Padó, S.
- Affiliations
- University of Stuttgart (DE)
- Venue
- Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
- Published
- 2019
- Pages
- 147-157
- OpenAlex
- W2972330249
- Cited by
- 12
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 1 · 2020: 3
- FWCI
- 0.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- knowledge base completion, explainable AI, explanation generation, TransE, entity and relation representation
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