A kernel-based SVM for semantic relations extraction from biomedical literature Online publication date: Wed, 01-Mar-2023
by U. Kanimozhi; D. Manjula
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 24, No. 3/4, 2023
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com