🤖 AI Summary
To address the challenges of semantic understanding and interactive analysis in open academic big data exploration, this paper pioneers the modeling of knowledge graph (KG) embedding spaces as interpretable semantic vector spaces—extending beyond their conventional use solely for link prediction. We propose a novel vector-algebraic paradigm for semantic querying—including analogical reasoning and similarity-based retrieval—that systematically uncovers structured semantic regularities among entities and relations in the embedding space. Our method integrates state-of-the-art KG embedding models (e.g., TransE, ComplEx) with word-embedding-style analogy analysis techniques to construct an explainable query framework tailored for scholarly KGs. Extensive evaluation on multiple public academic KGs demonstrates substantial improvements in deep relational pattern mining for entities such as papers and authors, and effectively supports cross-domain analogical inference and interactive exploratory tasks.
📝 Abstract
The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.