Ontology-based Classifier Research Articles

Ontology-based framework for a multi-domain spoken dialogue system

Multi-domain spoken dialogue is a challenging field where the objective of the most proposed ideas is to mimic the human–human dialogue. This paper proposes to tackle the domain selection problem in the context of multi-domain spoken dialogue as a set theory problem to resolve. First, we built each dialogue domain as an ontology following an architecture with some rules to respect. Second, each ontology is considered as a set and its concepts are the elements. Third, an ontology-based classifier is used to map the user sentence into a set of ontologies concepts and to generate an intersection between these concepts. Finally, a new turn analysis and domain selection algorithm is proposed to infer the intended domain from the user sentence using the intersection set and three techniques, namely Domain Rewards, Dominant Concept, and Current Domain. To evaluate the proposed approach, a corpus of 120 simulated dialogues was built to cover four application domains. In our experiment, the assessment of the system is performed by considering all possibilities of a natural verbal interaction where a changing of semantic context occurs during the dialogue. The obtained results show that the system accuracy reaches a satisfactory performance of 83.13% while the average number of turns by dialogue is 6.79.

Improving ontology-based text classification: An occupational health and security application

Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm [28] for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.