ISSN: 0973-7510

E-ISSN: 2581-690X

P. Madhubala1 and K. Murugesan2
1Tagore Institute of Engineering and Technology, Department of Computer Science and Engineering, Salem, Tamilnadu, India.
2Sree Sastha Institute of Engineering and Technology, Department of Electronics and Communication Engineering, Chennai, Tamilnadu, India.
J Pure Appl Microbiol. 2015;9(Spl. Edn. Aug.):79-86
© The Author(s). 2015
Received: 03/02/2015 | Accepted: 25/04/2015 | Published: 31/08/2015

Lack of large semantic variability patterns is a major obstacle to progress in semantic inferencefor medical data available online. Prominent inference knowledge representation includes entailment rules. Large-scale inference based knowledge systems have initiatedwork on automatic paraphrase and entailment rules acquisition. This work identifiesHypernym of medical terms and clubs them with entailment rule acquisition. A hyponym word tree in the document is created and used with the dependency tree. Features extraction is achieved through weighted TF-IDF where word weight is computed based on hyponyms present in a radix tree. The proposed system was evaluated using k-Nearest neighbour (kNN) algorithm with good results.


Hypernyms, Hyponym, TF-IDF, k-Nearest neighbour, Rule acquisition, Ontology

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© The Author(s) 2015. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.