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An Explainable Knowledge Completion Model Using Explanation Segments
Min-Ho Lee, Wan-Gon Lee, Batselem Jagvaral, Young-Tack Park
http://doi.org/10.5626/JOK.2021.48.6.680
Recently, a large number of studies that used deep learning have been conducted to predict new links in incomplete knowledge graphs. However, link prediction using deep learning has a major limitation as the inferred results cannot be explained. We propose a high-utility knowledge graph prediction model that yields explainable inference paths supporting the inference results. We define paths to the object from the knowledge graph using a path ranking algorithm and define them as the explanation segments. Then, the generated explanation segments are embedded using a Convolutional neural network (CNN) and a Bidirectional Long short-term memory (BiLSTM). The link prediction model is then trained by applying an attention mechanism, based on the calculation of the semantic similarity between the embedded explanation segments and inferred candidate predicates to be inferred. The explanation segment suitable for link prediction explanation is selected based on the measured attention scores. To evaluate the performance of the proposed method, a link prediction comparison experiment and an accuracy verification experiment are performed to measure the proportion of the explanation segments suitable to explain the link prediction results. We used the benchmark datasets NELL-995, FB15K-237, and countries for the experiment, and accuracy verification experiments showed the accuracies of 89%, 44%, and 97%, respectively. Compared with the existing method, the NELL-995, FB15K-237 data exhibited 35%p and 21%p higher performance on average.
A Knowledge Completion Approach using Rule Generation based on Neuro-Symbolic Method
Jea-Seung Roh, Won-Chul Shin, Hyun-Kyu Park, Young-Tack Park
http://doi.org/10.5626/JOK.2021.48.4.425
A knowledge graph is a structured representation of real-world knowledge and is designed by collecting information from various sources. These knowledge graphs are networks that represent relationships between data and are applied in various fields of artificial intelligence; however, there exists problems related to incomplete knowledge due to the omission of entities or omission links between the entities. To resolve the problems, research on automatic knowledge completion techniques is necessitated. Consequently, various studies have been examined including embedding techniques, deep learning or symbolic rule inference using ontology. Although automatic knowledge completion can be efficiently performed through the above-mentioned methods, deep learning methods require a large amount of learning data due to data-driven processing methods, and there exist problems related to the results that are hard to explain. Futhermore, ontology-based methods require ontology and rules that are defined by the experts. To overcome this limitation, in this study, we propose an automatic knowledge completion method by explicitly extracting the implicit rules from the data based on the Neuro-Symbolic method. For rule extraction, we have implemented a symbolic unification based embedding path and defined a cost function for it to automatically generate the rules. Compared with the approaches presented in previous embedding studies, the proposed method demonstrates the superiority of the Neuro-Symbolic method concerning speed and performance. To assess the performance of the proposed method, for datasets like Nations, UMLS, and Kinship, experiments were conducted in comparison with the approach of the state-of-the-art knowledge completion studies. Consequently, an immense reduction in the training time and 37.5%p increase in the average performance were observed.
An Effective Detection Method of Anomalous Sequences Considering the Occurrence Order and Time Interval of the Elements
http://doi.org/10.5626/JOK.2021.48.4.469
Recently, a rapid generation of sequence data consisting of elements in various applications has been witnessed over time. Although various methods for detecting anomalous sequences among the given sequences have been actively studied, most of them mainly consider only the occurrence order of the elements. In this paper, we propose an effective anomalous sequence detection method considering not only the occurrence order of the elements but also the time interval between the elements. Apparently, the proposed method uses a model that combines two autoencoders. The first is an LSTM autoencoder, which learns the features of the occurrence order of elements, and the second is a graph autoencoder, which learns the features of the time interval between the elements. After completion of the training, each sequence is input to the trained model and reconstructed by the trained model. If the occurrence order and time interval of elements in the reconstructed sequence greatly differ from those in the original sequence, the corresponding sequence is determined as an anomalous sequence. Through various experiments using synthetic data, we confirmed that the proposed method can detect anomalous sequences more effectively than the method that uses an RNN autoencoder to learn the occurrence order of the elements, the methods that use a single LSTM autoencoder and the method that doesn’t use deep learning model.
Hyperbolic Graph Transformer Networks for non-Euclidean Data Analysis on Heterogeneous Graphs
Seunghun Lee, Hyeonjin Park, Hyunwoo J Kim
http://doi.org/10.5626/JOK.2021.48.2.217
Convolution Neural Networks (CNNs), which are based on convolution operations, are used for various tasks in image classification, image generation, time series analysis, etc. Since the convolution operations are not directly applicable to non-Euclidean spaces such as graphs and manifolds, a variety of Graph Neural Networks (GNNs) have extended convolutional neural networks to homogeneous graphs, which has a single type of edges and nodes. However, in real-world applications, heterogeneous and hierarchical graph data often occur. To expand the operating range of GNNs to the graphs that have multiple types of nodes and edges with the hierarchy, herein, we propose a new model that integrates Hyperbolic Graph Convolution Networks (HGCNs) and Graph Transformer Networks (GTNs).
An Embedding Technique for Weighted Graphs using LSTM Autoencoders
http://doi.org/10.5626/JOK.2021.48.1.13
Graph embedding is the representation of graphs as vectors in a low-dimensional space. Recently, research on graph embedding using deep learning technology have been conducted. However, most research to date has focused mainly on the topology of nodes, and there are few studies on graph embedding for weighted graphs, which has an arbitrary weight on the edges between the nodes. Therefore, in this paper, we proposed a new graph embedding technique for weighted graphs. Given weighted graphs to be embedded, the proposed technique first extracts node-weight sequences that exist inside the graphs, and then encodes each node-weight sequence into a fixed-length vector using an LSTM (Long Short-Term Memory) autoencoder. Finally, for each graph, the proposed technique combines the encoding vectors of node-weight sequences extracted from the graph to generate one final embedding vector. The embedding vectors of the weighted graphs obtained by the proposed technique can be used for measuring the similarity between weighted graphs or classifying weighted graphs. Experiments on synthetic and real datasets consisting of groups of similar weighted graphs showed that the proposed technique provided more than 94% accuracy in finding similar weighted graphs.
Query-based Abstractive Summarization Model Using Sentence Ranking Scores and Graph Techniques
http://doi.org/10.5626/JOK.2020.47.12.1172
The purpose of the fundamental abstractive summarization model is to generate a short summary document that includes all important contents within the document. Conversely, in the query-based abstractive summarization model, information related to the query should be selected and summarized within the document. The existing query-based summarization models calculates the importance of sentences using only the weight of words through an attention mechanism between words in the document and the query. This method has a disadvantage in that it is difficult to reflect the entire context information of the document to generate an abstractive summary. In this paper, we resolve this problems by calculating the sentence ranking scores and a sentence-level graph structure. Our proposed model shows higher performance than the previous research model, 1.44%p in ROUGE-1 and 0.52%p in ROUGE-L.
Visual Commonsense Reasoning with Vision-Language Co-embedding and Knowledge Graph Embedding
http://doi.org/10.5626/JOK.2020.47.10.985
In this paper, we proposed a novel model for Visual Commonsense Reasoning (VCR). The proposed model co-embeds multi-modal input data together using a pre-trained vision-language model to effectively cope with the problem of visual grounding, which requires mutual alignment between an image, a natural language question, and the corresponding answer list. In addition, the proposed model extracts the common conceptual knowledge necessary for Visual Commonsense Reasoning from ConceptNet, an open knowledge base, and then embeds it using a Graph Convolutional neural Network (GCN). In this paper, we introduced the design details of the proposed model, VLKG_VCR, and verified the performance of the model through various experiments using an enhanced VCR benchmark data set.
Incorrect Triple Detection Using Knowledge Graph Embedding and Adaptive Clustering
Won-Chul Shin, Jea-Seung Roh, Young-Tack Park
http://doi.org/10.5626/JOK.2020.47.10.958
Recently, with the increase in the amount of information from the development of the Internet, research using large-capacity knowledge graphs is being actively conducted. Additionally, as knowledge graphs are used for various research and services, there is a need to secure quality knowledge graphs. However, there is a lack of research to detect errors within the knowledge graphs to obtain quality knowledge graphs. Previous studies using the embedding and clustering for error triple detection showed good performance. However, in the process of the cluster optimization, there was a problem that the characteristics of each cluster could not be factored using the same threshold collectively. In this paper, to resolve these problems, we propose an adaptive clustering model in which clustering is conducted by finding and applying the optimum threshold for each cluster with the embedding for knowledge graph for error triple detection in the knowledge graph. To evaluate the performance of the method proposed in this paper, the existing error triple detection studies and comparative experiments were conducted on three datasets, DBpeida, Frebase and WiseKB, and the high performance was confirmed by an average of 5.3% based on the F1-Score.
Open Domain Question Answering using Knowledge Graph
http://doi.org/10.5626/JOK.2020.47.9.853
In this paper, we propose a novel knowledge graph inference model called KGNet for answering the open domain complex questions. This model addresses the problem of knowledge base incompleteness. In this model, two different types of knowledge resources, knowledge base and corpus, are integrated into a single knowledge graph. Moreover, to derive answers to complex multi-hop questions effectively, this model adopts a new knowledge embedding and reasoning module based on Graph Neural Network (GNN). We demonstrate the effectiveness and performance of the proposed model through various experiments over two large question answering benchmark datasets, WebQuestionsSP and MetaQA.
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.
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