Department of Information and Communication Engineering, Inha University, Incheon 22212, Korea.
Department of Information Systems, Benha University, Banha 13518, Egypt.
Sensors (Basel). 2019 Jan 9;19(2):234. doi: 10.3390/s19020234.
Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.
智能交通系统 (ITSs) 利用基于传感器网络的系统来收集和解释交通信息。此外，移动性用户利用移动应用程序收集运输信息以安全出行。然而，这些类型的信息不足以检查交通网络的所有方面。因此，ITSs 和移动性用户都需要一种智能方法和社交媒体数据，这可以帮助 ITSs 检查运输服务、支持交通和控制管理，并帮助移动性用户安全出行。人们利用社交网络分享他们对交通的想法和意见，这对 ITSs 和旅行者都很有用。然而，社交媒体上的用户生成文本长度短、结构不完整且涵盖广泛的动态主题。最近的机器学习 (ML) 方法的应用在从非结构化数据中提取相关特征、检测特征的词极性以及正确分类特征的情感方面效率低下。此外，ML 分类器始终会错过单词含义的语义特征。提出了一种基于模糊本体的语义知识与 Word2vec 模型相结合的方法，利用双向长短期记忆 (Bi-LSTM) 方法改进运输特征提取和文本分类任务。所提出的模糊本体描述了运输领域中实体和特征及其关系的语义知识。模糊本体和智能方法分别在 Web 本体语言和 Java 中开发。通过利用带有模糊本体的词嵌入作为文本的表示，Bi-LSTM 显示出在提取特征和分类社交媒体的非结构化文本方面都有令人满意的改进。
