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Combining Text Embedding and Knowledge Graph Embedding Techniques for Academic Search Engines
2018
International Semantic Web Conference
The past decades have witnessed a rapid increase in the global scientific output as measured by published papers. Exploring a scientific field and searching for relevant papers and authors seems like a needle-in-a-haystack problem. Although many academic search engines have been developed to accelerate this retrieval process, most of them rely on content-based methods and feature engineering. In this work, we present an entity retrieval prototype system on top of IOS Press LD Connect which
dblp:conf/semweb/MaiJY18
fatcat:o2ehtvu7zjb4ljjraorwnxcnlq