Cannabis_TREATS_cancer: Incorporating Fine-Grained Ontological Relations in Medical Document Ranking

The previous work has justified the assumption that document ranking can be improved by further considering the coarse-grained relations in various linguistic levels (e.g., lexical, syntactical and semantic). To the best of our knowledge, little work is reported to incorporate the fine-grained ontological relations (e.g., ) in document ranking. Two contributions are worth noting in this work. First, three major combination models (i.e., summation, multiplication, and amplification) are designed to re-calculate the querydocument relevance score considering both the term-level Okapi BM25 relevance score and the relation-level relevance score. Second, a vectorbased scoring algorithm is proposed to calculate the relation-level relevance score. A few experiments on medical document ranking with CLEF2013 eHealth Lab medical information retrieval dataset show that the proposed document ranking algorithms can be further improved by incorporating the fine-grained ontological relations.