Implementing an Inference Engine for RDFS/OWL Constructs and User-Defined Rules in HBase
Zhengbo Liu, Wenbin Yao, Dongbin Wang · 2017
The Inference Engine is an integral part of Semantic Data Stores. In this paper, we describe our experience of implementing a scalable inference engine for HBase Semantic Data Store. This inference engine computes production rule based entailment of one or more RDFS/OWL encoded semantic data models. The inference engine capabilities include i) inferencing based on semantics of RDFS/OWL constructs and user-defined rules, and ii) validation of semantic data model based on RDFS/OWL semantics. A unique aspect of our approach is that the inference engine is implemented entirely as a database application on top of the HBase Database. The paper describes the inferencing requirements, challenges in supporting a sufficiently expressive set of RDFS/OWL constructs, and techniques adopted to build a scalable inference engine. A performance study conducted using LUBM semantic datasets demonstrates the effectiveness of our approach.