Fuzzy Semantic Agent Based on Ontology Model for Chinese Lyrics Classification

Nowadays, social media is getting more and more popular so that many people choose to absorb the knowledge, share their moods, read news, listen to music, and appreciate the video on the Internet. The popular Chinese songs can be categorized according to their song style, their released decade, their singer, and so on. Currently, the song is always classified as a single category, such as inspiration, love, or family. However, when people listen to a song, they will have a different feeling according to their moods in the moment. This paper adopts the lyrics of the popular Chinese songs on the Internet as the experimental samples. Then, we classify the songs based on the natural language processing, ontology, Word2Vec, and fuzzy inference mechanism. The adopted natural language mechanism contains term comparison and term similarity to compute the different-category weights. Additionally, we also use predefined ontology, knowledge base, and rule base to classify the songs. Moreover, we also adopt the multilayer perceptron neural network with the backpropagation algorithm to train the data under a supervised learning. The learned results are better than the ones of the fuzzy inference mechanism. In the future, this study will enhance ontology, knowledge base, and rule base as well as enlarge the number of experimental samples to improve the performance. Finally, we will combine music appreciation with the robot to make children learn the knowledge more interesting.

[1]  Giancarlo Guizzardi,et al.  From reference ontologies to ontology patterns and back , 2017, Data Knowl. Eng..

[2]  Naoyuki Kubota,et al.  FML-based linguistic classification agent for social media application , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[3]  Yuandong Tian,et al.  Better Computer Go Player with Neural Network and Long-term Prediction , 2016, ICLR.

[4]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[5]  Xuejie Zhang,et al.  Refining Word Embeddings Using Intensity Scores for Sentiment Analysis , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[6]  Miki Haseyama,et al.  A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy , 2018, IEEE Access.

[7]  Hani Hagras,et al.  A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation , 2010, IEEE Transactions on Fuzzy Systems.

[8]  Xing Wang,et al.  Music Emotion Classification of Chinese Songs based on Lyrics Using TF*IDF and Rhyme , 2011, ISMIR.

[9]  Bob L. Sturm,et al.  Deep Learning and Music Adversaries , 2015, IEEE Transactions on Multimedia.

[10]  Hani Hagras,et al.  Knowledge structuring to support facet-based ontology visualization , 2010 .

[11]  Hani Hagras,et al.  Diet assessment based on type‐2 fuzzy ontology and fuzzy markup language , 2010, Int. J. Intell. Syst..

[12]  Chang-Shing Lee,et al.  Ontology-based Intelligent Decision Support Agent for CMMI Project Monitoring and Control , 2006, NAFIPS 2006.

[13]  Daniel Rodríguez,et al.  ON-SMMILE: Ontology Network-based Student Model for MultIple Learning Environments , 2018, Data Knowl. Eng..

[14]  François Chollet,et al.  Deep Learning with Python , 2017 .

[15]  Chang-Shing Lee,et al.  A genetic fuzzy agent using ontology model for meeting scheduling system , 2006, Inf. Sci..

[16]  Chang-Shing Lee,et al.  A fuzzy ontology and its application to news summarization , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Huiru Zheng,et al.  Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets , 2018, Comput. Biol. Medicine.

[18]  Zhiwu Lu,et al.  Zero-Shot Scene Classification for High Spatial Resolution Remote Sensing Images , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[20]  Nikhil Ketkar,et al.  Deep Learning with Python , 2017 .

[21]  Chang-Shing Lee,et al.  Adaptive Personalized Diet Linguistic Recommendation Mechanism Based on Type-2 Fuzzy Sets and Genetic Fuzzy Markup Language , 2015, IEEE Transactions on Fuzzy Systems.

[22]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[23]  Shiliang Sun,et al.  A review of natural language processing techniques for opinion mining systems , 2017, Inf. Fusion.

[24]  Bruno Cabral,et al.  Prototyping a GPGPU Neural Network for Deep-Learning Big Data Analysis , 2017, Big Data Res..