Enhancing Biomedical Knowledge Representation Through Knowledge Graphs

Résumé

There is a plethora of information related to the biomedical
domain on the internet. Unfortunately, retrieving this
information online is challenging because of insufficient
semantic metadata embedded within the web documents that
help search engines interpret the biomedical information.
Semantic annotators have partially bridged this gap, yet
these tools frequently need to catch up in accuracy, speed,
and the ability to dynamically represent knowledge. We
initially developed "Semantically," a biomedical semantic
content authoring platform to streamline and enhance
biomedical annotations through a social-technical approach.
Even so, the current system stores data in a relational
schema, which lacks machine-readable content that allows
search engines to parse the meanings to annotation
recommendations. There is still the need for the
amalgamation and contextually rich representation of
annotation recommendation information to enhance
navigation and exploration of data. Therefore, we propose a
knowledge graph-based recommendation system with an
nlp-enhanced search query to provide an environment for
easy and quick access to optimal recommendations in a
machine-readable knowledge graph format. We obtain
results for the knowledge graph through an evaluation
survey that substantiates the efficacy of our knowledge
graph-based recommendation system, highlighting its role in
advancing dynamic knowledge representation and semantic
annotation in the biomedical domain. A demo is available at
SebC750/Semantically at Knowledge_Graph_Branch
(github.com)