@inproceedings{64d7434c94e64acc90889e28b28a617b,
title = "Similarity calculations of academic articles using topic events and domain knowledge",
abstract = "While studies investigating the semantic similarity among concepts, sentences and short text fragments have been fruitful, the problem of document-level semantic matching remains largely unexplored due to its complexity. In this paper, we explore the document-level semantic similarity issue in the academic literatures using an interpretable method. To integrally describe the semantics of an article, we construct a topic event model that utilizes multiple information facets, such as the study purposes, methodologies and domains. Furthermore, to better understand the documents and achieve a more accurate similarity comparison, we incorporate external knowledge into the topic event construction and similarity calculation. Our approach achieves significant improvements over state-of-the-art methods.",
keywords = "Document semantic similarity, Domain ontology, Scientific literature analysis, Text understanding, Topic event",
author = "Ming Liu and Bo Lang and Zepeng Gu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 ; Conference date: 23-07-2018 Through 25-07-2018",
year = "2018",
doi = "10.1007/978-3-319-96890-2\_4",
language = "英语",
isbn = "9783319968896",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "45--53",
editor = "Jianliang Xu and Yoshiharu Ishikawa and Yi Cai",
booktitle = "Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings",
address = "德国",
}