Title: A kernel-based SVM for semantic relations extraction from biomedical literature

Authors: U. Kanimozhi; D. Manjula

Addresses: Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India ' Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India

Abstract: Recognising and extracting semantic relationships among named entities; relation extraction is a significant methodology for knowledge representation. In order to capture the semantic as well as syntactic structures in text and to enable deep understanding of biomedical literature, relation extraction becomes essential. The automatic extraction of disease-gene relations is presented in this paper by utilising shallow linguistic features of global and local word sequence context with string kernel-based support vector machine (SVM) for efficient disease-gene relation extraction. The performance of the proposed work shows that the bag-of-features kernel-based SVM classification is a promising resolution for specific disease-gene association mining. The initial results obtained using shallow linguistic kernel methods on an annotated Huntington disease corpora suggested the global tri-grams context surrounding related entities are critical and essential for disease-gene relation extraction, which is in the pact with PPI relation extraction evaluation using AImed corpora.

Keywords: biomedical relation extraction; natural language processing; machine learning; biomedical literature.

DOI: 10.1504/IJAIP.2023.129182

International Journal of Advanced Intelligence Paradigms, 2023 Vol.24 No.3/4, pp.341 - 354

Received: 16 Dec 2016
Accepted: 07 Dec 2017
Published online: 01 Mar 2023 *

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