Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology
This addresses the problem of accurate semantic matching for academic documents, which is crucial for tasks like plagiarism detection and semantic search, though it appears incremental as it builds on existing concepts with a novel combination.
The paper tackles document-level semantic similarity for academic articles by representing them with topic events and using domain ontology for similarity calculation, achieving significant performance compared to state-of-the-art methods.
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.