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The trends of open science have enabled several open scholarly datasets whichinclude millions of papers and authors. Managing, exploring, and utilizing suchlarge and complicated datasets effectively are challenging. In recent years,the knowledge graph has emerged as a universal data format for representingknowledge about heterogeneous entities and their relationships. The knowledgegraph can be modeled by knowledge graph embedding methods, which represententities and relations as embedding vectors in semantic space, then model theinteractions between these embedding vectors. However, the semantic structuresin the knowledge graph embedding space are not well-studied, thus knowledgegraph embedding methods are usually only used for knowledge graph completionbut not data representation and analysis. In this paper, we propose to analyzethese semantic structures based on the well-studied word embedding space anduse them to support data exploration. We also define the semantic queries,which are algebraic operations between the embedding vectors in the knowledgegraph embedding space, to solve queries such as similarity and analogy betweenthe entities on the original datasets. We then design a general framework fordata exploration by semantic queries and discuss the solution to sometraditional scholarly data exploration tasks. We also propose some newinteresting tasks that can be solved based on the uncanny semantic structuresof the embedding space.

Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space