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  • Open Access


    Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

    Anitha Velu*, Menakadevi Thangavelu

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4707-4724, 2022, DOI:10.32604/cmc.2022.020095

    Abstract The drastic growth of coastal observation sensors results in copious data that provide weather information. The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems. The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval. This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes. This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine. The developed Oceanographic Weather Ontology helps to enhance data… More >

  • Open Access


    Word Embedding Based Knowledge Representation with Extracting Relationship Between Scientific Terminologies

    Mucheol Kim*, Junho Kim, Mincheol Shin

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 141-147, 2020, DOI:10.31209/2019.100000135

    Abstract With the trends of big data era, many people want to acquire the reliable and refined information from web environments. However, it is difficult to find appropriate information because the volume and complexity of web information is increasing rapidly. So many researchers are focused on text mining and personalized recommendation for extracting users’ interests. The proposed approach extracted semantic relationship between scientific terminologies with word embedding approach. We aggregated science data in BT for supporting users’ wellness. In our experiments, query expansion is performed with relationship between scientific terminologies with user’s intention. More >

  • Open Access


    Knowledge Graph Representation Reasoning for Recommendation System

    Tao Li, Hao Li*, Sheng Zhong, Yan Kang, Yachuan Zhang, Rongjing Bu, Yang Hu

    Journal of New Media, Vol.2, No.1, pp. 21-30, 2020, DOI:10.32604/jnm.2020.09767

    Abstract In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS model. KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm, then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning, then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning. Finally, through the full connection layer and sigmoid function to get the prediction ratings, and the items are sorted… More >

  • Open Access


    Sox Compliance with OEE, Enterprise Modeling and Temporal-ABC

    K. Donald Thama, Asad M. Madnib

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 17-26, 2018, DOI:10.1080/10798587.2017.1284411

    Abstract The Sarbanes-Oxley (SOX) Act 2002 resulted from the mounting accounting and corporate scandals in the late 1990s and early 2000s. Since the passage of the SOX Act, companies are facing even greater challenges to meet raised expectations to provide accurate, visible, and timely information for SOX compliance. This research puts forth a systems design framework to achieve a real time, accurate, consistently traceable and easily verifiable SOX compliant technology. Our multidisciplinary and integrative systems design incorporates Overall Equipment Effectiveness (OEE) to ensure effective business performance within a knowledge represented company modeled as Enveloped Activity Based Enterprise Model (EABEM) that facilitates… More >

  • Open Access


    Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

    Syed Falahuddin Quadri1, Xiaoyu Li1,*, Desheng Zheng2, Muhammad Umar Aftab1, Yiming Huang3

    Journal on Big Data, Vol.1, No.1, pp. 1-7, 2019, DOI:10.32604/jbd.2019.05899

    Abstract Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the… More >

  • Open Access


    An Improved Method for Web Text Affective Cognition Computing Based on Knowledge Graph

    Bohan Niu1,*, Yongfeng Huang2

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 1-14, 2019, DOI:10.32604/cmc.2019.06032

    Abstract The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions, emotions, evaluations and attitudes that people hold about the entities and their attributes from the text. The word level affective cognition becomes an important topic in sentiment analysis. Extracting the (attribute, opinion word) binary relationship by word segmentation and dependency parsing, and labeling those by existing emotional dictionary combined with webpage information and manual annotation, this paper constitutes a binary relationship knowledge base. By using knowledge embedding method, embedding each element in (attribute, opinion, opinion word) as a word vector into the… More >

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