Research Article
Optimization of Large-Scale Knowledge Forward Reasoning Based on OWL 2 DL Ontology
@INPROCEEDINGS{10.1007/978-3-031-24386-8_21, author={Lingyun Cui and Tenglong Ren and Xiaowang Zhang and Zhiyong Feng}, title={Optimization of Large-Scale Knowledge Forward Reasoning Based on OWL 2 DL Ontology}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2023}, month={1}, keywords={Forward reasoning Ontology RDF data}, doi={10.1007/978-3-031-24386-8_21} }- Lingyun Cui
Tenglong Ren
Xiaowang Zhang
Zhiyong Feng
Year: 2023
Optimization of Large-Scale Knowledge Forward Reasoning Based on OWL 2 DL Ontology
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-24386-8_21
Abstract
This paper focuses on the performance of optimized forward reason systems. The main characteristics of forward reasoning are that it is sensitive to the update of data, has a high cost of precomputation closure, and can not be closely related to the characteristics of the specific query. Therefore, it usually makes reason on irrelevant data. Processing this data reduces the performance of the reason system and consumes a lot of memory resources. Backward reasoning can make up for this defect to a certain extent, but its inherent defect of the high cost of online query rewriting cannot make it efficient in reasoning tasks. We design an efficient reason method, which can effectively combine the advantages of forward reason and backward reason to ensure the completeness of reason as much as possible. It can not only reduce the processing cost caused by data updates and desensitize semantic data to a certain extent but also avoid the high cost caused by query rewriting and greatly reduce the cost of precomputation closure. Finally, we implement the proposed method on a prototype of a forward reason system named SUMA-F and compare it with the current forward reason systems with better performance on various datasets of different sizes. Experiments show that the SUMA-F has high reasoning efficiency, is better than other systems, and has high scalability on large-scale datasets.
