title: Ranking biomedical annotations with annotator's semantic relevancy.
abstract: Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator's knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user's vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large.
authors.affiliation: Department of Computer Science, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Aihua
authors.identifier: 0000-0003-0532-5579
authors.initials: A
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D016208:Databases, Factual; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012660:Semantics; D012984:Software