<![CDATA[Most studies on why-question answering system usually used the keyword-based approaches. They rarely involved domain ontology in capturing the semantic of the document contents, especially in detecting the presence of the causal relations. Consequently, the word mismatch problem usually occurs and the system often retrieves not relevant answers. For solving this problem, we propose an answer extraction method by involving the semantic similarity measure, with selective causality detection. The selective causality detection is applied because not all sentences belonging to an answer contain causality. Moreover, the motivation of the use of semantic similarity measure in scoring function is to get more moderate results about the presence of the semantic annotations in a sentence, instead of 0/1. The semantic similarity measure employed is based on the shortest path and the maximum depth of the ontology graph. The evaluation is conducted by comparing the proposed method against the comparable ontology-based methods, i.e., the sentence extraction with Monge-Elkan with 0/1 internal similarity function. The proposed method shows the improvements in term of MRR (16%, 0.79-0.68), P@1 (15%, 0.76-0.66), P@5 (14%, 0.8-0.7), and Recall (19%, 0.86-0.72).]]>
