  Determining semantic similarity between academic documents is crucial to many
tasks such as plagiarism detection, automatic technical survey and semantic
search. Current studies mostly focus on semantic similarity between concepts,
sentences and short text fragments. However, document-level semantic matching
is still based on statistical information in surface level, neglecting article
structures and global semantic meanings, which may cause the deviation in
document understanding. In this paper, we focus on the document-level semantic
similarity issue for academic literatures with a novel method. We represent
academic articles with topic events that utilize multiple information profiles,
such as research purposes, methodologies and domains to integrally describe the
research work, and calculate the similarity between topic events based on the
domain ontology to acquire the semantic similarity between articles.
Experiments show that our approach achieves significant performance compared to
state-of-the-art methods.
