IEEE Systems, Man and Cybernetics Magazine - October 2022 - 51

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MRR
13 10
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FB-15K Dataset
Figure 2. The MRR and hit ratio for link prediction.
parameter. Obviously, the decoder turns the model into a
binary classification problem to train. For positive samples
in T, label the class as 1, and for negative samples in
T ,l label the class as −1 and keep training iteratively.
Ideally, for any triplet, the model can distinguish
between true and false. To verify the effect of the model,
the knowledge graph link prediction and triplet classification
experiments are carried out on FB15K and FB15K237,
respectively.
Experiment and Analysis
For link prediction, three evaluation metrics are used
1) mean reciprocal rank (MRR), which represents the
mean of the reciprocal ranks of the correct triples
2) mean rank (MR), which represents the correct triple and
the average of the tuple rankings
3) Hits@N, which represents the proportion of correct triples
in the top N (N = 1, 3, 10) prediction results.
The link prediction results on the two datasets are
shown in Table 1 and Figure 2. According to the experimental
results, it can be found that the XAI-CNN model
outperforms the other models on almost all metrics of the
two datasets, which proves that the combined model in
this article is very effective to indicate the text description
of the entity. Descriptive information, hierarchical-type
information, and graph structure information are all
important supplements to the original triples and can
improve the effect of knowledge representation learning.
Conclusion
The XAI-CNN model proposed in this work combines multisource
data. First, an encoder based on TransE is created,
which merges structured triple data, entity description
data, entity hierarchical-type data, and graph topology
data. The ConvKB model is then utilized as a decoder to
calculate global information in many dimensions while
maintaining the model's translation features. On two classic
datasets, FB15K and FB15K-237, experiments on link
prediction and triplet classification are conducted. The
results show that our method outperforms other baseline
models on these two types of tasks, demonstrating that
entity description information, entity level-type information,
and graph structure information are useful additions
to the original triple structure information of knowledge
graphs and that the combined model presented in this article
can significantly improve the effect of knowledge representation
learning.
About the Author
Lulwah M. Alkwai (l.algweie@uoh.edu.sa) is with the
School of Computer Science and Engineering, University of
Ha'il, Ha'il, 152485, Saudi Arabia.
References
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MRR
13 10
Hits
FB-15K-237 Dataset
October 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 51

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