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Improving Performance of Recurrent Neural Network based Recommendations by Utilizing Personal Preferences
Dong Shin Lim, Yong Jun Yang, Shin Cho
http://doi.org/10.5626/JOK.2021.48.11.1211
As the amount of content provided on the platform surged, a recommendation system became an essential element of the platform. The collaborative filtering technique is a widely used recommendation system in academia and industry, but it also has a limitation because it relies on quantitative information from consumers such as ratings and purchase history. To overcome this shortcoming, various studies have been done in a bid to improve its performance by collecting qualitative information such as review data in a model. Recently, some studies that applied recurrent neural networks showed better performance than the existing recommendation system by using time-series behavioral data only, but studies that reflect each customer"s preference in the recommendation model have not yet been made. In this paper, an improved recommendation model was presented by calculating a preference matrix based on customer log data and learning it in a recurrent neural network through an embedding vector. It was confirmed that the prediction performance was improved compared to the existing recurrent neural network recommendation model.
An Efficient Document Clustering Method using Space Transformation based on LDA and WMD
http://doi.org/10.5626/JOK.2021.48.9.1052
The existing TF-IDF-based document clustering methods do not properly exploit the contextual information of documents, i.e., co-occurence and word-order, and tend to degrade the performance due to the curse of dimensionality. To overcome these problems, the techniques such as a weighted average of word embedding vectors or Word Mover"s Distance (WMD) have been proposed. The performance of the techniques is good at document classification, but not a document clustering that needs to group documents. In this study, we define a document group as a topic document using LDA, the document group"s representative document, and solve the existing problem by calculating the WMD based on the topic document. However, since WMD requires a large amount of computation, we propose a space transformation method that shows a good performance while reducing the computation cost by mapping each document to a low-dimensional space in which each axis means WMD value from each topic document.
EFA-DTI: Prediction of Drug-Target Interactions Using Edge Feature Attention
Erkhembayar Jadamba, Sooheon Kim, Hyeonsu Lee, Hwajong Kim
http://doi.org/10.5626/JOK.2021.48.7.825
Drug discovery is a high-level field of research requiring the coordination of disciplines ranging from medicinal chemistry, systems biology, structural biology, and increasingly, artificial intelligence. In particular, drug-target interaction (DTI) prediction is central to the process of screening for and optimizing candidate substances to treat disease from a nearly infinite set of compounds. Recently, as computer performance has developed dramatically, studies using artificial intelligence neural networks have been actively conducted to reduce the cost and increase the efficiency of DTI prediction. This paper proposes a model that predicts an interaction value between a given molecule and protein using a learned molecule representation via Edge Feature Attention-applied Graph Net Embedding with Fixed Fingerprints and a protein representation using pre-trained protein embeddings. The paper describes architectures, experimental methods, and findings. The model demonstrated higher performance than DeepDTA and GraphDTA, which had previously demonstrated the best performance in DTI studies.
A Comparative Study on the Performance of Named Entity Recognition in Materials and Chemistry Fields through Multiple Embedding Combination Based on a Pre-trained Neural Network Language Model
Myunghoon Lee, Hyeonho Shin, Hong-Woo Chun, Jae-Min Lee, Taehyun Ha, Sung-Pil Choi
http://doi.org/10.5626/JOK.2021.48.6.696
Recently, with the rapid development of materials and chemistry fields, the academic literature has increased exponentially. Accordingly, studies are being conducted to extract meaningful information from the existing accumulated data, and Named Entity Recognition (NER) is being utilized as one of the methodologies. NER in materials and chemistry fields is a task of extracting standardized entities such as materials, material property information, and experimental conditions from academic literature and classifying types of the entities. In this paper, we studied the NER in materials and chemistry fields using a combination of embedding and a Bi-direction LSTM-CRF model with an existing published language model without pre-training a neural network language model. As a result, we found the best performing embedding combinations and analyzed their performance. Additionally, the pre-trained language model was used as a NER model to compare performance through fine-tuning. The process showed that the use of a public pre-trained language model for embedding combinations could derive meaningful results in NER in the materials and chemistry fields.
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.
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.
Improvement in Network Intrusion Detection based on LSTM and Feature Embedding
Hyeokmin Gwon, Chungjun Lee, Rakun Keum, Heeyoul Choi
http://doi.org/10.5626/JOK.2021.48.4.418
Network Intrusion Detection System (NIDS) is an essential tool for network perimeter security. NIDS inspects network traffic packets to detect network intrusions. Most of the existing works have used machine learning techniques for building the system. While the reported works demonstrated the effectiveness of various artificial intelligence algorithms, only a few of them have utilized the time-series information of network traffic data. Also, categorical information of network traffic data has not been included in neural network-based approaches. In this paper, we propose network intrusion detection models based on sequential information using the long short-term memory (LSTM) network and categorical information using the embedding technique. We have conducted experiments using models with UNSW-NB15, which is a comprehensive network traffic dataset. The experiment results confirm that the proposed method improves the performance, with a binary classification accuracy rate of 99.72%.
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.
An Embedding Method of Emotes for the Detection of Popular Clips on Twitch.tv
Hyeonho Song, Kunwoo Park, Meeyoung Cha
http://doi.org/10.5626/JOK.2020.47.12.1153
This study presents an embedding method that effectively learns emote’s meaning in Twitch.tv to understand the audience reaction in live streaming. The proposed method first trains an embedding matrix for text and emotes, respectively, and merges the two matrices into one. Using 2,220,761 clips shared on Twitch.tv, this study conducted two experiments: clustering and clip popularity prediction. Results showed that the approach identifies emote clusters that express a similar emotion and detects popular clips. Future studies could utilize the proposed emote embedding method for the highlight prediction of a live stream.
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