Machine Learning Techniques with Ontology for Subjective Answer Evaluation
This work addresses the challenge of automated essay grading for domain-specific subjects like Computer Graphics, but it appears incremental as it combines existing techniques with ontology.
The paper tackled the problem of computerized evaluation of English essays, specifically technical answers in Computer Science, by implementing machine learning techniques with and without ontology, and found that using ontology improved accuracy.
Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy. Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are implemented both with and without Ontology and tested on common input data consisting of technical answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The software used for implementation includes Java Programming Language and tools such as MATLAB, Protégé, etc. Ten questions from Computer Graphics with sixty answers for each question are used for testing. The results are analyzed and it is concluded that the results are more accurate with use of Ontology.