@article{alobaidi_linked_2018,
	title = {Linked open data-based framework for automatic biomedical ontology generation},
	volume = {19},
	issn = {1471-2105},
	url = {https://doi.org/10.1186/s12859-018-2339-3},
	doi = {10.1186/s12859-018-2339-3},
	abstract = {Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns.},
	number = {1},
	urldate = {2018-09-11},
	journal = {BMC Bioinformatics},
	author = {Alobaidi, Mazen and Malik, Khalid Mahmood and Sabra, Susan},
	month = sep,
	year = {2018},
	pages = {319},
}

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