Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

OBDI System for Fuzzy Web Data Table Integration Using an Ontological and Terminological Resource

When finding new product innovations or filling new patents, inventors have necessary to retrieve all the relevant pre-existing know-how or to exploit and enforce patents in the technological area. Since the OTR is at the important and heart of Semantic Ontology system, this team works on the ontology construction and evolution. Author present system architecture relies on an Ontological and the Terminological Resource (OTR) which is made up of two parts: on the one end, a generic set of concepts dedicated to data integration task, on the other hand, a specific set of concepts and terminology, to a given domain of application. The important objective of the semantic annotation method here is to identify which relations of OTR are represented in data table that simple concepts are called in the given simple target concepts. In order to annotate a column by a simple target concept, a score is computed for each of the simple target concept of the OTR, on a generic OTR expressed in OWL. Here the system allows XML data tables that have been taken from Web documents, to be annotated with fuzzy RDF descriptions and to be flexibly Ontology search engine. Ontology search engine allows for retrieve not only to exact answers compared with selection criteria but also semantically close answers and compare the this selection criteria expressed as fuzzy sets representing preferences with fuzzy annotations of data. DOI: 10.17762/ijritcc2321-8169.15072

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

A document management methodology based on similarity contents

The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain