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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.
Knowledge Completion System through Learning the Relationship between Query and Knowledge Graph
Min-Sung Kim, Min-Ho Lee, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2021.48.6.649
The knowledge graph is a network comprising of relationships between the entities. In a knowledge graph, there exists a problem of missing or incorrect relationship connection with the specific entities. Numerous studies have proposed learning methods using artificial neural networks based on natural language embedding to solve the problems of the incomplete knowledge graph. Various knowledge graph completion systems are being studied using these methods. In this paper, a system that infers missing knowledge using specific queries and knowledge graphs is proposed. First, a topic is automatically extracted from a query, and topic embedding is obtained from the knowledge graph embedding module. Next, a new triple is inferred by learning the relationship between the topic from the knowledge graph and the query by using Query embedding and knowledge graph embedding. Through this method, the missing knowledge was inferred and the predicate embedding of the knowledge graph related to a specific query was used for good performance. Also, an experiment was conducted using the MetaQA dataset to prove the better performance of the proposed method compared with the existing methods. For the experiment, we used a knowledge graph having movies as a domain. Based on the assumption of the entire knowledge graph and the missing knowledge graph, we experimented on the knowledge graph in which 50% of the triples were randomly omitted. Apparently, better performance than the existing method was obtained.
An Integrated System for Large-Scale Knowledge Graph Inference Using the Spark DataFrame
Min-Ho Lee, Min-Sung Kim, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2020.47.12.1162
Recently, there has been an active study of large-scale ontology reasoning methods using big data obtained from the Web. However, when the amount of data increases, there is a problem with inference performance and processing speed decreasing. In this paper, we propose a two-step integrated system to perform inference using the Spark DataFrame in a cloud computing environment for effective inference. The first step is to perform rule inference on the OWL through a previous study inference engine. The second step, as in the previous study, performs inference on the user-defined rules through the SWRL inference engine using the Spark DataFrame.
OWL-Horst Ontology Inference Engine Using Distributed Table Structure in Cloud Computing Environment
Min-Sung Kim, Min-Ho Lee, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2020.47.7.674
Recently, many machine learning methods that extend ontology through data obtained from the web are being studied. As data from the web continues to increase, interest in large-capacity ontology inference methods is also increasing. However, the increasing amount of data decreases processing speeds. This paper describes how to improve the performance of large-scale OWL-Horst inference using distributed table structured data frames to solve the problem of the slow processing speed of large-capacity data. Also, a distributed parallel inference algorithm and optimization method used to improve the inference performance is described. To evaluate the performance of the inference system using the distributed table structured data frame proposed in this paper, experiments were conducted with LUBM1000, LUBM2000, LUBM3000, and LUBM4000. Our reasoning system showed the best performance.
CAAM - Model for National-level Cyber Attack Attribution
Min-ho Lee, Chang-wook Park, Wan-ju Kim, Jae-sung Lim
http://doi.org/10.5626/JOK.2020.47.1.19
Recently, security companies have been reporting that some organizations engaging in carry out cyber attacks are suspected of receiving state-sponsored support. To effectively respond to these cyber-attack groups, it is critical to detect and quickly analyze the characteristics of the attacks to identify the countries responsible first for such terroristic acts. This paper presents the attribution model (CAAM) for state-sponsored cyber attacks, and CAAM analyzes the characteristics of such cyber attacks through the four-step process of detection and collection, analysis, evaluation and visualization. The detailed elements for analyzing the characteristics of cyber attacks were divided into five categories: Tools and technology of attack organizations, Infrastructure of attack organizations, Structure of malicious codes, Motivation of attacks, and External factors. Five factors were assessed by country to identify those that support cyber attacks. The application of CAAM is expected to enable rapid analysis of state-sponsored cyber attacks and has validated the effectiveness of the CAAM model through comparison with the existing attack group analysis model.
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