TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE
Abstract
:1. Introduction
- For complex optimization tasks, we first propose multi-translation principles (MTPs). Unlike some existing methods, such as TransE, MTPs can focus on different relationship types and establish corresponding translation principles.
- To produce accurate reasoning and prediction, multiple score functions are constructed based on MTPs for model training. Subsequently, the loss function is optimized with the gradient descent algorithm to minimize the score function.
- Representation learning of a knowledge graph with MTPs aims to optimize multiple objectives by applying translation principles to the four types of relationships: one-to-one, one-to-many, many-to-one, and many-to-many. Furthermore, we introduce TransE-MTP (combining MTPs with TransE). Extensive experiments on two widely used knowledge graph datasets, Freebase and WordNet, demonstrate the efficiency and competitiveness of our proposed model.
2. Related Work
2.1. Knowledge Graphs
2.2. Reasoning and Complement for Knowledge Graphs
2.3. Knowledge Representation Learning
3. The Proposed Method
- For one-to-one and many-to-one relations, the translation principle is defined as h + r = t;
- For one-to-many relations, the translation principle is defined as t − r = h;
- For many-to-many relations, the translation principle is defined as t − h = r.
4. Experiment and Analysis
4.1. Dataset and Experimental Setup
4.2. Link Prediction
4.3. Triplet Classification
5. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | #Rel | #Ent | #Train | #Valid | #Test |
---|---|---|---|---|---|
WN11 | 11 | 38,696 | 112,581 | 2609 | 10,544 |
WN18 | 18 | 40,493 | 141,442 | 5000 | 5000 |
FB13 | 13 | 75,043 | 316,232 | 5908 | 23,733 |
FB15K | 1345 | 14,951 | 483,142 | 50,000 | 59,071 |
Model | #Parameters | #Operations (Time Complexity) |
---|---|---|
SLM | ||
NTN | O (((m2 + m) + 2mk+ k)Nt) | |
TransE | ||
TransH | ||
TransE-MTP |
Dataset | Model | Λ | γ | n, m | B | D.S |
---|---|---|---|---|---|---|
WN18 | TransE-MTP | 0.01 | 3.5 | 50 | 1440 | L1 |
FB15K | TransE-MTP | 0.001 | 1 | 200 | 4800 | L1 |
Dataset | WN18 | FB15K | |||||||
---|---|---|---|---|---|---|---|---|---|
Model | |||||||||
Metric | Mean Rank | Hits@10 | Mean Rank | Hits@10 | |||||
Raw | Filt | Raw | Filt | Raw | Filt | Raw | Filt | ||
RESCAL | 1180 | 1163 | 37.2 | 52.8 | 828 | 683 | 28.4 | 44.1 | |
SE | 1011 | 985 | 68.5 | 80.5 | 273 | 162 | 28.8 | 39.8 | |
SME | 542/526 | 533/509 | 65.1/54.7 | 74.1/61.3 | 274/284 | 154/158 | 30.7/31.3 | 40.8/41.3 | |
LFM | 469 | 456 | 71.4 | 81.6 | 283 | 164 | 26.0 | 33.1 | |
TransE | 263 | 251 | 75.4 | 89.2 | 243 | 125 | 34.9 | 47.1 | |
TransH (unif/bern) | 318/401 | 303/388 | 75.4/73.0 | 86.7/82.3 | 211/212 | 84/87 | 42.5/45.7 | 58.5/64.4 | |
TransE-MTP (unif/bern) | 261/238 | 249/226 | 73.2/76.6 | 85.2/87.3 | 237/241 | 83/160 | 50.8/52.5 | 78.6/75.2 |
Dataset | Model | α | λ | n, m | B | D.S |
---|---|---|---|---|---|---|
WN11 | TransE-MTP | 0.01 | 4 | 20 | 120 | L1 |
FB13 | TransE-MTP | 0.01 | 2 | 100 | 480 | L1 |
FB15K | TransE-MTP | 0.001 | 1 | 200 | 4800 | L1 |
Dataset | WN11 | FB13 | FB15K | |
---|---|---|---|---|
Model | ||||
SE | 53.0 | 75.2 | - | |
SME (bilinear) | 70.0 | 63.7 | - | |
SLM | 69.9 | 85.3 | - | |
LFM | 73.8 | 84.3 | - | |
NTN | 70.4 | 87.1 | 68.2 | |
TransE (unif/bern) | 75.9/75.9 | 70.9/81.5 | 77.3/79.8 | |
TransH (unif/bern) | 77.7/78.8 | 76.5/83.3 | 74.2/79.9 | |
TransE-MTP (unif/bern) | 81.7/81.3 | 79.8/84.86 | 84.2/83.6 |
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Li, Y.; Zhu, C. TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE. Electronics 2024, 13, 3171. https://doi.org/10.3390/electronics13163171
Li Y, Zhu C. TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE. Electronics. 2024; 13(16):3171. https://doi.org/10.3390/electronics13163171
Chicago/Turabian StyleLi, Yongfang, and Chunhua Zhu. 2024. "TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE" Electronics 13, no. 16: 3171. https://doi.org/10.3390/electronics13163171
APA StyleLi, Y., & Zhu, C. (2024). TransE-MTP: A New Representation Learning Method for Knowledge Graph Embedding with Multi-Translation Principles and TransE. Electronics, 13(16), 3171. https://doi.org/10.3390/electronics13163171