Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets Article Swipe
Esma Balkır
,
Masha Naslidnyk
,
Dave Palfrey
,
Arpit Mittal
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.11583
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1910.11583
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this paper we use occurrences of entity-relation pairs in the dataset to construct a joint learning model and to increase the quality of sampled negatives during training. We show on three standard datasets that when these two techniques are combined, they give a significant improvement in performance, especially when the batch size and the number of generated negative examples are low relative to the size of the dataset. We then apply our techniques to a dataset containing 2 million entities and demonstrate that our model outperforms the baseline by 2.8% absolute on hits@1.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1910.11583
- https://arxiv.org/pdf/1910.11583
- OA Status
- green
- References
- 17
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2982191902
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2982191902Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1910.11583Digital Object Identifier
- Title
-
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale DatasetsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-10-25Full publication date if available
- Authors
-
Esma Balkır, Masha Naslidnyk, Dave Palfrey, Arpit MittalList of authors in order
- Landing page
-
https://arxiv.org/abs/1910.11583Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1910.11583Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1910.11583Direct OA link when available
- Concepts
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Computer science, Pairwise comparison, Bottleneck, Graph, Data mining, Scale (ratio), Machine learning, Artificial intelligence, Theoretical computer science, Physics, Quantum mechanics, Embedded systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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17Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.absolute | 127 |
| abstract_inverted_index.baseline | 124 |
| abstract_inverted_index.dataset. | 104 |
| abstract_inverted_index.datasets | 31, 69 |
| abstract_inverted_index.entities | 116 |
| abstract_inverted_index.examples | 95 |
| abstract_inverted_index.increase | 56 |
| abstract_inverted_index.learning | 52 |
| abstract_inverted_index.negative | 94 |
| abstract_inverted_index.relative | 98 |
| abstract_inverted_index.standard | 68 |
| abstract_inverted_index.training | 27 |
| abstract_inverted_index.combined, | 76 |
| abstract_inverted_index.construct | 49 |
| abstract_inverted_index.effective | 8 |
| abstract_inverted_index.generated | 93 |
| abstract_inverted_index.knowledge | 11 |
| abstract_inverted_index.negatives | 61 |
| abstract_inverted_index.training. | 63 |
| abstract_inverted_index.bottleneck | 25 |
| abstract_inverted_index.containing | 113 |
| abstract_inverted_index.especially | 84 |
| abstract_inverted_index.techniques | 74, 109 |
| abstract_inverted_index.completion. | 14 |
| abstract_inverted_index.demonstrate | 118 |
| abstract_inverted_index.improvement | 81 |
| abstract_inverted_index.occurrences | 41 |
| abstract_inverted_index.outperforms | 122 |
| abstract_inverted_index.performance | 24 |
| abstract_inverted_index.significant | 80 |
| abstract_inverted_index.constraints. | 35 |
| abstract_inverted_index.performance, | 83 |
| abstract_inverted_index.entity-relation | 43 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |