PROO ontology development for learning feature specific sentiment relationship rules on reviews categorisation: a semantic data mining approach

Crucial data like product features were obtained from consumer online reviews and sentiment words were gathered in Resource Description Format RDF in order to use them in meaningful reviews based categorisation on sentiments of the feature. The meaningful relationships among these pieces of RDF data are to be engineered in a Product Review Opinion Ontology PROO. This serves as background knowledge to learn rule based sentiments expressed on product features. These semantic rules are learned on both taxonomical and non-taxonomical relations available in PROO Ontology. In order to verify the mined rules, Inductive Logic Programming ILP is applied on PROO. The learned ILP rules are found to be among the mined rules. The positively classified features are grouped to justify the goal of ILP for examples which are both complete and consistent. Left out negative examples are useful in knowing their count at the time of categorisation.

[1]  J. Keziya Rani,et al.  Mining Opinion Features in Customer Reviews. , 2016 .

[2]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[3]  Ian Horrocks,et al.  Ontologies and the semantic web , 2008, CACM.

[4]  Haishan Liu,et al.  Towards Semantic Data Mining , 2010 .

[5]  W. Hall,et al.  Using Protege for automatic ontology instantiation , 2004 .

[6]  Adem Karahoca,et al.  "Hello world", web mining for e-learning , 2011, WCIT.

[7]  Fernando Benites,et al.  Using Semantic Data Mining for Classification Improvement and Knowledge Extraction , 2014, LWA.

[8]  Katharina Morik,et al.  Knowledge Discovery in databases - An Inductive Logic Programming Approach , 1997, Foundations of Computer Science: Potential - Theory - Cognition.

[9]  Paweł Kapłański.  Controlled English interface for knowledge bases , 2011 .

[10]  Rafael Berlanga Llavori,et al.  Mining Association Rules from Semantic Web Data , 2010, IEA/AIE.

[11]  Víctor Hugo Menéndez-Domínguez,et al.  Using data mining techniques for exploring learning object repositories , 2011, Electron. Libr..

[12]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[13]  Nada Lavrac,et al.  Propositionalization Online , 2014, ECML/PKDD.

[14]  Wooju Kim,et al.  Development of semantic decision tree , 2011, The 3rd International Conference on Data Mining and Intelligent Information Technology Applications.

[15]  Cláudia Antunes.  MINING RULES IN THE ONTO4AR FRAMEWORK FOR UPDATING DOMAIN ONTOLOGIES , 2007 .

[16]  Juan D. Velásquez,et al.  Extracting significant Website Key Objects: A Semantic Web mining approach , 2011, Eng. Appl. Artif. Intell..

[17]  D. Teja Santosh,et al.  OBTAINING FEATURE- AND SENTIMENT-BASED LINKED INSTANCE RDF DATA FROM UNSTRUCTURED REVIEWS USING ONTOLOGY-BASED MACHINE LEARNING , 2015 .

[18]  Saso Dzeroski,et al.  Inductive Logic Programming and Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[19]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[20]  Justin Zhijun Zhan,et al.  Sentiment analysis using product review data , 2015, Journal of Big Data.

[21]  Nada Lavrac,et al.  Using Ontologies in Semantic Data Mining with SEGS and g-SEGS , 2011, Discovery Science.

[22]  Felix Naumann,et al.  Context and target configurations for mining RDF data , 2011, SMER '11.