A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects Article Swipe
Jiapu Wang
,
Boyue Wang
,
Meikang Qiu
,
Shirui Pan
,
Bo Xiong
,
Heng Liu
,
Linhao Luo
,
Tengfei Liu
,
Yongli Hu
,
Baocai Yin
,
Wen Gao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02457
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.02457
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC.
Related Topics
Concepts
Computer science
Extrapolation
Knowledge graph
Graph
Data mining
Knowledge extraction
Temporal database
Missing data
Set (abstract data type)
Data science
Information retrieval
Machine learning
Artificial intelligence
Theoretical computer science
Mathematics
Programming language
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.02457
- https://arxiv.org/pdf/2308.02457
- OA Status
- green
- Cited By
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385645262
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385645262Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.02457Digital Object Identifier
- Title
-
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and ProspectsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-04Full publication date if available
- Authors
-
Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, Wen GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.02457Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.02457Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2308.02457Direct OA link when available
- Concepts
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Computer science, Extrapolation, Knowledge graph, Graph, Data mining, Knowledge extraction, Temporal database, Missing data, Set (abstract data type), Data science, Information retrieval, Machine learning, Artificial intelligence, Theoretical computer science, Mathematics, Programming language, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 10, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 32, 47, 120 |
| abstract_inverted_index.has | 72 |
| abstract_inverted_index.how | 155 |
| abstract_inverted_index.new | 40 |
| abstract_inverted_index.set | 139 |
| abstract_inverted_index.the | 13, 36, 42, 45, 55, 60, 64, 83, 112, 125, 135, 143, 179, 186 |
| abstract_inverted_index.TKGC | 95, 115, 151 |
| abstract_inverted_index.TKGs | 27, 166 |
| abstract_inverted_index.both | 22 |
| abstract_inverted_index.from | 30, 51 |
| abstract_inverted_index.lack | 56 |
| abstract_inverted_index.loss | 117 |
| abstract_inverted_index.main | 34 |
| abstract_inverted_index.role | 15 |
| abstract_inverted_index.task | 65 |
| abstract_inverted_index.that | 131 |
| abstract_inverted_index.then | 172 |
| abstract_inverted_index.they | 181 |
| abstract_inverted_index.this | 87, 101 |
| abstract_inverted_index.well | 123 |
| abstract_inverted_index.Graph | 69 |
| abstract_inverted_index.TKGC. | 194 |
| abstract_inverted_index.Thus, | 63 |
| abstract_inverted_index.based | 81, 153, 177 |
| abstract_inverted_index.data, | 53 |
| abstract_inverted_index.items | 80 |
| abstract_inverted_index.often | 28 |
| abstract_inverted_index.paper | 102 |
| abstract_inverted_index.their | 98 |
| abstract_inverted_index.three | 33, 106 |
| abstract_inverted_index.which | 11, 110, 161 |
| abstract_inverted_index.(TKGC) | 71 |
| abstract_inverted_index.(TKGs) | 20 |
| abstract_inverted_index.Graphs | 19 |
| abstract_inverted_index.aiming | 76 |
| abstract_inverted_index.covers | 111 |
| abstract_inverted_index.future | 169, 190 |
| abstract_inverted_index.mainly | 103 |
| abstract_inverted_index.paper, | 88 |
| abstract_inverted_index.review | 93 |
| abstract_inverted_index.source | 61 |
| abstract_inverted_index.suffer | 29 |
| abstract_inverted_index.volume | 8 |
| abstract_inverted_index.dataset | 126 |
| abstract_inverted_index.discuss | 189 |
| abstract_inverted_index.events, | 170 |
| abstract_inverted_index.evident | 4 |
| abstract_inverted_index.focuses | 163 |
| abstract_inverted_index.further | 148, 184 |
| abstract_inverted_index.methods | 96, 152, 176 |
| abstract_inverted_index.missing | 79, 136 |
| abstract_inverted_index.namely, | 108 |
| abstract_inverted_index.pivotal | 14 |
| abstract_inverted_index.predict | 78 |
| abstract_inverted_index.process | 157 |
| abstract_inverted_index.provide | 90 |
| abstract_inverted_index.related | 150 |
| abstract_inverted_index.through | 142 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.Temporal | 0, 17, 67 |
| abstract_inverted_index.academia | 23 |
| abstract_inverted_index.consists | 104 |
| abstract_inverted_index.dataset. | 62 |
| abstract_inverted_index.details. | 99 |
| abstract_inverted_index.elements | 137, 141 |
| abstract_inverted_index.methods, | 116 |
| abstract_inverted_index.pinpoint | 185 |
| abstract_inverted_index.predicts | 134, 168 |
| abstract_inverted_index.reasons: | 35 |
| abstract_inverted_index.relevant | 144 |
| abstract_inverted_index.required | 119 |
| abstract_inverted_index.research | 191 |
| abstract_inverted_index.temporal | 158 |
| abstract_inverted_index.utilize. | 182 |
| abstract_inverted_index.weakness | 43 |
| abstract_inverted_index.Knowledge | 18, 68 |
| abstract_inverted_index.algorithm | 46 |
| abstract_inverted_index.attracted | 73 |
| abstract_inverted_index.available | 84, 145 |
| abstract_inverted_index.emergence | 38 |
| abstract_inverted_index.estimates | 132 |
| abstract_inverted_index.functions | 118 |
| abstract_inverted_index.industry. | 25 |
| abstract_inverted_index.protocol; | 129 |
| abstract_inverted_index.training, | 121 |
| abstract_inverted_index.typically | 162 |
| abstract_inverted_index.Completion | 70 |
| abstract_inverted_index.algorithms | 180 |
| abstract_inverted_index.attention, | 75 |
| abstract_inverted_index.challenges | 187 |
| abstract_inverted_index.classifies | 173 |
| abstract_inverted_index.continuous | 37, 165 |
| abstract_inverted_index.directions | 192 |
| abstract_inverted_index.evaluation | 128 |
| abstract_inverted_index.extracting | 48 |
| abstract_inverted_index.increasing | 74 |
| abstract_inverted_index.knowledge, | 10, 41 |
| abstract_inverted_index.structured | 49 |
| abstract_inverted_index.categorizes | 149 |
| abstract_inverted_index.components, | 107 |
| abstract_inverted_index.information | 50, 58 |
| abstract_inverted_index.prominently | 3 |
| abstract_inverted_index.substantial | 7 |
| abstract_inverted_index.underscores | 12 |
| abstract_inverted_index.information. | 85, 146 |
| abstract_inverted_index.information; | 159 |
| abstract_inverted_index.unstructured | 52 |
| abstract_inverted_index.1)Background, | 109 |
| abstract_inverted_index.Specifically, | 100 |
| abstract_inverted_index.comprehensive | 92 |
| abstract_inverted_index.extrapolation | 175 |
| abstract_inverted_index.preliminaries | 113 |
| abstract_inverted_index.incompleteness | 31 |
| abstract_inverted_index.characteristics | 1 |
| abstract_inverted_index.2)Interpolation, | 130 |
| abstract_inverted_index.3)Extrapolation, | 160 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |