We propose a system for automated essay grading using ontologies and textual entailment. The process of textual entailment is guided by hypotheses, which are extracted from a domain ontology. Textual entailment checks if the truth of the hypothesis follows from a given text. We enact textual entailment to compare students answer to a model answer obtained from ontology. We validated the solution against various essays written by students in the chemistry domain.
The difficult task of recognising textual entailment aims to check if a natural language text T entails a smaller statement H. Current methods rely on machine learning and various lexical resources. Our aim is to include domain knowledge when searching for entailment or non-entailment. As most available knowledge comes in form of ontologies, we focused on translating description logic axioms into lexical rules suitable for existing textual entailment algorithms. We apply the developed system in the climate change domain, where many pro and counter arguments do exist. The performed experiments indicate an increasing of performance when including domain knowledge into the existing textual entailment algorithms.
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