Learning Joint Structural and Temporal Contextualized Knowledge Embeddings for Temporal Knowledge Graph Completion Article Swipe
Yifu Gao
,
Yongquan He
,
Zhigang Kan
,
Yi Han
,
Linbo Qiao
,
Dongsheng Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.findings-acl.28
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.findings-acl.28
Temporal knowledge graph completion that predicts missing links for incomplete temporal knowledge graphs (TKG) is gaining increasing attention.Most existing works have achieved good results by incorporating time information into static knowledge graph embedding methods.However, they ignore the contextual nature of the TKG structure, i.e., query-specific subgraph contains both structural and temporal neighboring facts.This paper presents the SToKE, a novel method that employs the pre-trained language model (PLM) to learn joint Structural and Temporal Contextualized Knowledge Embeddings.Specifically, we first construct an event evolution tree (EET) for each query to enable PLMs to handle the TKG, which can be seen as a structured event sequence recording query-relevant structural and temporal contexts. We then propose a novel temporal embedding and structural matrix to learn the time information and structural dependencies of facts in EET.Finally, we formulate TKG completion as a mask prediction problem by masking the missing entity of the query to fine-tune pre-trained language models.Experimental results on three widely used datasets show the superiority of our model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2023.findings-acl.28
- https://aclanthology.org/2023.findings-acl.28.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385571206
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- OpenAlex ID
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https://openalex.org/W4385571206Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2023.findings-acl.28Digital Object Identifier
- Title
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Learning Joint Structural and Temporal Contextualized Knowledge Embeddings for Temporal Knowledge Graph CompletionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Yifu Gao, Yongquan He, Zhigang Kan, Yi Han, Linbo Qiao, Dongsheng LiList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2023.findings-acl.28Publisher landing page
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https://aclanthology.org/2023.findings-acl.28.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://aclanthology.org/2023.findings-acl.28.pdfDirect OA link when available
- Concepts
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Computer science, Knowledge graph, Embedding, Graph, Artificial intelligence, Construct (python library), Temporal database, Event (particle physics), Natural language processing, Theoretical computer science, Data mining, Programming language, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 7Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 129 |
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| abstract_inverted_index.on | 154 |
| abstract_inverted_index.to | 67, 87, 90, 119, 148 |
| abstract_inverted_index.we | 76, 131 |
| abstract_inverted_index.TKG | 41, 133 |
| abstract_inverted_index.and | 49, 71, 106, 116, 124 |
| abstract_inverted_index.can | 95 |
| abstract_inverted_index.for | 8, 84 |
| abstract_inverted_index.our | 163 |
| abstract_inverted_index.the | 36, 40, 55, 62, 92, 121, 142, 146, 160 |
| abstract_inverted_index.PLMs | 89 |
| abstract_inverted_index.TKG, | 93 |
| abstract_inverted_index.both | 47 |
| abstract_inverted_index.each | 85 |
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| abstract_inverted_index.seen | 97 |
| abstract_inverted_index.show | 159 |
| abstract_inverted_index.that | 4, 60 |
| abstract_inverted_index.then | 110 |
| abstract_inverted_index.they | 34 |
| abstract_inverted_index.time | 26, 122 |
| abstract_inverted_index.tree | 82 |
| abstract_inverted_index.used | 157 |
| abstract_inverted_index.(EET) | 83 |
| abstract_inverted_index.(PLM) | 66 |
| abstract_inverted_index.(TKG) | 13 |
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| abstract_inverted_index.facts | 128 |
| abstract_inverted_index.first | 77 |
| abstract_inverted_index.graph | 2, 31 |
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| abstract_inverted_index.learn | 68, 120 |
| abstract_inverted_index.links | 7 |
| abstract_inverted_index.model | 65 |
| abstract_inverted_index.novel | 58, 113 |
| abstract_inverted_index.paper | 53 |
| abstract_inverted_index.query | 86, 147 |
| abstract_inverted_index.three | 155 |
| abstract_inverted_index.which | 94 |
| abstract_inverted_index.works | 19 |
| abstract_inverted_index.SToKE, | 56 |
| abstract_inverted_index.enable | 88 |
| abstract_inverted_index.entity | 144 |
| abstract_inverted_index.graphs | 12 |
| abstract_inverted_index.handle | 91 |
| abstract_inverted_index.ignore | 35 |
| abstract_inverted_index.matrix | 118 |
| abstract_inverted_index.method | 59 |
| abstract_inverted_index.model. | 164 |
| abstract_inverted_index.nature | 38 |
| abstract_inverted_index.static | 29 |
| abstract_inverted_index.widely | 156 |
| abstract_inverted_index.employs | 61 |
| abstract_inverted_index.gaining | 15 |
| abstract_inverted_index.masking | 141 |
| abstract_inverted_index.missing | 6, 143 |
| abstract_inverted_index.problem | 139 |
| abstract_inverted_index.propose | 111 |
| abstract_inverted_index.results | 23, 153 |
| abstract_inverted_index.Temporal | 0, 72 |
| abstract_inverted_index.achieved | 21 |
| abstract_inverted_index.contains | 46 |
| abstract_inverted_index.datasets | 158 |
| abstract_inverted_index.existing | 18 |
| abstract_inverted_index.language | 64, 151 |
| abstract_inverted_index.predicts | 5 |
| abstract_inverted_index.presents | 54 |
| abstract_inverted_index.sequence | 102 |
| abstract_inverted_index.subgraph | 45 |
| abstract_inverted_index.temporal | 10, 50, 107, 114 |
| abstract_inverted_index.Knowledge | 74 |
| abstract_inverted_index.construct | 78 |
| abstract_inverted_index.contexts. | 108 |
| abstract_inverted_index.embedding | 32, 115 |
| abstract_inverted_index.evolution | 81 |
| abstract_inverted_index.fine-tune | 149 |
| abstract_inverted_index.formulate | 132 |
| abstract_inverted_index.knowledge | 1, 11, 30 |
| abstract_inverted_index.recording | 103 |
| abstract_inverted_index.Structural | 70 |
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| abstract_inverted_index.contextual | 37 |
| abstract_inverted_index.facts.This | 52 |
| abstract_inverted_index.incomplete | 9 |
| abstract_inverted_index.increasing | 16 |
| abstract_inverted_index.prediction | 138 |
| abstract_inverted_index.structural | 48, 105, 117, 125 |
| abstract_inverted_index.structure, | 42 |
| abstract_inverted_index.structured | 100 |
| abstract_inverted_index.information | 27, 123 |
| abstract_inverted_index.neighboring | 51 |
| abstract_inverted_index.pre-trained | 63, 150 |
| abstract_inverted_index.superiority | 161 |
| abstract_inverted_index.EET.Finally, | 130 |
| abstract_inverted_index.dependencies | 126 |
| abstract_inverted_index.incorporating | 25 |
| abstract_inverted_index.Contextualized | 73 |
| abstract_inverted_index.attention.Most | 17 |
| abstract_inverted_index.query-relevant | 104 |
| abstract_inverted_index.query-specific | 44 |
| abstract_inverted_index.methods.However, | 33 |
| abstract_inverted_index.models.Experimental | 152 |
| abstract_inverted_index.Embeddings.Specifically, | 75 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.91199132 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |