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Graph representation learning method based on three-way partial order structure
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DOI：10.1016/j.ijar.2023.109104.png)
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In the era of big data, handling massive datasets to extract valuable information has become increasingly critical. Knowledge representation emerges as a pivotal method to address this challenge. In the domain of knowledge representation, there exist two primary approaches: symbolic representation and vector representation. The integration of symbolic and vector representations to harness their respective strengths has become the cutting-edge approach to address challenges in the field of knowledge representation. This paper proposes a method that integrates a partial order formal structure analysis (POFSA) with graph representation learning. Specifically, we initially construct three-way partial order structure graphs, then create an attribute graph based on this structure, which can be processed by the graph representation learning methods. Finally, we utilize the graph representation learning to construct embeddings for three-way attribute partial order structure diagram (APOSD). We comprehensively assesse these embeddings across eight different datasets and present the results. The experiments indicate the feasibility of our proposed approach which is proven a novel approach of combining symbolic and vector representations for handling complex data and implicit knowledge.
Keyword:
Concept -cognitive learning
Partial order formal structure analysis
Three-way decision
Granular computing
Graph representation learning
Graph neural networks
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被引数:
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