Linking PubMed Abstracts to Gene Ontology Using Semantics
Author Information
Author(s): Vanteru Bhanu C, Shaik Jahangheer S, Yeasin Mohammed
Primary Institution: University of Memphis
Hypothesis
Can a semantic-sensitive search engine improve the retrieval of relevant PubMed abstracts linked to Gene Ontology?
Conclusion
The proposed SEGOPubmed technique outperformed traditional keyword-based methods in associating relevant abstracts to Gene Ontology terms.
Supporting Evidence
- SEGOPubmed incorporates semantics into the ontology-based searching of PubMed.
- The analysis using well-referenced keywords shows that the proposed technique outperformed string comparison methods.
- SEGOPubmed extracted abstracts where keywords appeared in combination with other terms, which traditional methods missed.
Takeaway
This study created a smarter way to find research papers by understanding the meaning of words, not just matching them exactly.
Methodology
The study used Latent Semantic Analysis to semantically link PubMed abstracts to Gene Ontology terms and compared its performance with existing methods.
Limitations
The study is limited to a few well-referenced keywords and does not address the concept of polysemy.
Digital Object Identifier (DOI)
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