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Path Embedding-Based Knowledge Graph Completion Approach
Batselem Jagvaral, Min-Sung Kim, Young-Tack Park
http://doi.org/10.5626/JOK.2020.47.8.722
Knowledge graphs are widely used in question answering systems. However, in these circumstances most of the relations between the entities in the knowledge graph tend to be missing. To solve this issue, we propose a CNN(Convolutional Neural Network) + BiLSTM(Bidirectional LSTM) based approach to infer missing links in the knowledge graphs. Our method embeds paths connecting two entities into a low-dimensional space via CNN + BiLSTM. Then, an attention operation is used to attentively combine path embeddings to represent two entities. Finally, we measure the similarity between the target relation and representation of the entities to predict whether or not the relation connects those entities. By combining a CNN and BiLSTM, we are able to take advantage of the CNN’s ability to recognize local patterns and the LSTM’s ability to produce entity and relation ordering. In this way, it is possible to effectively identify low-dimensional path features and predict the relationships between entities using the learned features. In our experiments, we performed link prediction tasks on 4 different knowledge graphs and showed that our method achieves comparable results to state-of-the-art methods.
Approach for Managing Multiple Class Membership in Knowledge Graph Completion Using Bi-LSTM
Jae-Seung Roh, Batselem Jagvaral, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2020.47.6.559
Knowledge graphs that represent real world information in a structured way are widely used in areas, such as Web browsing and recommendation systems. But there is a problem of missing links between entities in knowledge graphs. To resolve this issue, various studies using embedding techniques or deep learning have been proposed. Especially, the recent study combining CNN and Bidirectional-LSTM has shown high performance compared to previous studies. However, in the previous study, if multiple class types are defined for single entity, the amount of training data exponentially increases with the training time. Also, if class type information for an entity is not defined, training data for that entity cannot be generated. Thus, to enable the generation of training data for such entities and manage multiple class membership in knowledge graph completion, we propose two approaches using pre-trained embedding vectors of knowledge graph and the concept of vector addition. To evaluate the performance of the methods proposed in this paper, we conducted comparative experiments with the existing knowledge completion studies on NELL-995 and FB15K-237 datasets, and obtained MAP 1.6%p and MRR 1.5%p higher than that of the previous studies.
A Knowledge Graph Embedding-based Ensemble Model for Link Prediction
http://doi.org/10.5626/JOK.2020.47.5.473
Knowledge bases often suffer from their limited applicability due to missing information in their entities and relations. Link prediction has been investigated to complete the missing information and makes a knowledge base more useful. The existing studies on link prediction often rely on knowledge graph embedding and have shown trade-off in their performance. In this paper, we propose an ensemble model for knowledge graph embedding to improve quality of link prediction. The proposed model combines multiple knowledge graph embeddings that have unique characteristics. In this way, the ensemble model is able to consider various aspects of the entries within a knowledge base and reduce the variation of accuracy depending on hyper-parameters. Our experiment shows that the proposed model outperforms other knowledge graph embedding methods by 13.5% on WN18 and FB15K dataset.
Partial Embedding Approach for Knowledge Completion
Wan-Gon Lee, Batselem Jagvaral, Ji-Hun Hong, Hyun-Young Choi, Young-Tack Park
http://doi.org/10.5626/JOK.2018.45.11.1168
Knowledge graphs are large networks that describe real world entities and their relationships with triples. Most of the knowledge graphs are far from being complete, and many previous studies have addressed this problem using low dimensional graph embeddings. Such methods assume that knowledge graphs are fixed and do not change. However, real-world knowledge graphs evolve at a rapid pace with the addition of new triples.Repeated retraining of embedding models for the entire graph is computationally expensive and impractical. In this paper, we propose a partial embedding method for partial completion of evolving knowledge graphs. Our method employs ontological axioms and contextual information to extract relations of interest and builds entity and relation embedding models based on instances of such relations. Our experiments demonstrated that the proposed partial embedding method can produce comparable results on knowledge graph completion with state-of-the-art methods while significantly reducing the computation time of entity and relation embeddings by 49%–90% for the Freebase and WiseKB datasets.
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