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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 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.
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.
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.
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.
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.
An Approach for Recognition of Elderly Living Patterns Based on Event Calculus Using Percept Sequence
Hyun-Kyu Park, Young-Tack Park
http://doi.org/10.5626/JOK.2019.46.11.1149
This paper proposes a method for recognizing the intentions of human activity based on percept sequences that represent the activities of daily living (ADL) in a residential space. Based on the activity intention ontology, which represents actions and poses related to human activity intentions, the proposed method identifies the intention of a human activity by using event calculus when a percept sequence is entered. Based on the action intent identified, frequency and pattern analysis of the action intention is used to characterize the lifestyle patterns of the elderly. The intentions of everyday behavior occurring in an elderly living space are complex, and it is difficult to recognize the pattern of life through these intentions, which makes it difficult to recognize the intention of a complex occurrence. To solve these problems, this paper constructs an ontology of percept sequences expressed as daily behavioral information, and makes inferences to help recognize activity intent based on event calculus. When evaluating the techniques proposed in this paper, the results of the act intention cognition experiment based on the perceptual information recorded showed 84% precision and 85% recall.
A UAV Situational Awareness Method through the Threat-Related Relation Reasoning between UAV and Surrounding Objects
Seok-Hyun Bae, Myung-Joong Jeon, Hyun-Kyu Park, Young-Tack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2019.46.2.141
As the technological capabilities of UAV(Unmanned Aerial Vehicles) improves, studies are being carried out to intelligently analyze and understand the situation of UAV in order to gain access to the target area while recognizing and avoiding various risks. To achieve the mission of UAV, it is necessary to judge the situation accurately and quickly. To do this, this paper proposes ways to infer the threat-related relationship between an UAV and perceived surrounding objects through a 3 step approach and provide abstract information about the situation of UAV. The first step is to instantiate the object data recognized by UAV to be utilized for ontology and rule-based reasoning. The second step is to define the priority of instantiated object data and to infer the threat-related relationship between them. Finally, recognizing the situation through the relationship inference that takes into account the association between current and past inferred relationships. To evaluation the performance of the proposed method, a virtual UAV environment simulator was built and tested the data 1,000 times that were randomly generated through five sequential UAV moving point paths. Eight kinds of objects could be recognized in UAV path and ten kinds of relationships can be inferred. Overall performance of situation Awareness was an average of 91 percent.
Incorrect Triple Detection Using Knowledge Base Embedding and Relation Model
Ji-Hun Hong, Hyun-Young Choi, Wan-Gon Lee, Young-Tack Park
http://doi.org/10.5626/JOK.2019.46.2.131
Recently, with the increase of the amount of information due to the development of the Internet, there has been an increased interest in research using a large-capacity knowledge base. Additionally, studies are being conducted to complete the knowledge base as it uses become widely used in various studies. However, there has been lack of research to detect error triples in the knowledge base. This paper, we proposes the embedding of an algorithm to detect the error triple in the knowledge base, the utilization of the clustered embedding model and the four relational models, which are typical algorithms of triple classification. Additionally, a relation ensemble model was generated using the results of the single embedding models and the embedding ensemble model similarly generated using the results of the single embedding models. The error triple detection results were then compared and measured through the model verification indexes.
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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