TY  - JOUR
T1  - Knowledge Completion System using Neuro-Symbolic-based Rule Induction and Inference Engine
AU  - Shin, Won-Chul 
AU  - Park, Hyun-Kyu 
AU  - Park, Young-Tack 
JO  - Journal of KIISE, JOK
PY  - 2021
DA  - 2021/1/14
DO  - 10.5626/JOK.2021.48.11.1202
KW  - knowledge graphs
KW  - deep learning
KW  - logic system
KW  - inference engine
KW  - knowledge inference
AB  - Recently, there have been several studies on knowledge completion methods aimed to solve the incomplete knowledge graphs problem. Methods such as Neural Theorem Prover (NTP), which combines the advantages of deep learning methods and logic systems, have performed well over existing methods. However, NTP faces challenges in processing large-scale knowledge graphs because all the triples of the knowledge graph are involved in the computation to obtain prediction results for one input. In this paper, we propose an integrated system of deep learning and logic inference methods that can learn vector representations of symbols from improved models of computational complexity of NTP to rule induction, and perform knowledge inference from induced rules using inference engines. In this paper, for rule-induction performance verification of the rule generation model, we compared test data inference ability with NTP using induced rules on Nations, Kinship, and UMLS data set. Experiments with Kdata and WiseKB knowledge inference through inference engines resulted in a 30% increase in Kdata and a 95% increase in WiseKB compared to the knowledge graphs used in experiments.