Building Real-Time Ontology Based on Adaptive Filter for Multi-Domain Knowledge Organization

Abstract

Multi-domain knowledge organization is an effective way of correlating cross-domain knowledge or intercommunicating between cross-domain knowledge systems. As a knowledge organization model, ontology is widely used in information and management systems. To organize multi-domain knowledge, ontologies in different domains correlate to each other directly or indirectly. Generally, matching and integrating ontologies of different domain into a large scale ontology is the common way of directly correlating, while building a top level ontology is the main method for indirectly correlating. As the scale of domain ontologies get larger and larger, both direct and indirect methods become more difficult and time-consuming. In order to improve the organization of multi-domain knowledge, this paper presents a novel ontology organization method to build real-time ontology by adaptive filter while user presenting requirements. Only the entities related to user requirements are integrated, while building a real-time ontology. Firstly, the method searches domain ontologies that are related to user requirements. Then subontologies are extracted from search results by filter, and they are integrated into a new ontology under direction of filter, i.e. real-time ontology. Finally, four criteria are introduced to evaluate real-time ontology. The experiment results illuminate that real-time ontology perform excellently in accuracy, recall, correctness and especially time-consuming.INDEX TERMS Real-time ontology, multi-domain knowledge organization, ontology matching, ontology integration, knowledge engineering

New directions of digitally driven S&T evaluation

“…Ontology is a cross-domain knowledge organization model. Zhou et al (2021) proposed a real-time ontology RTO model, which keeps only the entities related to user requirements in the domain ontology. RTO is lightweight, avoiding the problems of large scale and low matching efficiency of super large ontology (SLO) and the difficulty of maintaining and updating top-level ontology (TLO).…”

Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability

“…It provides multi-domain correlation and multi-domain intercommunication. Instead of machine learning techniques, the adaptive filter algorithm was introduced to integrate the domain ontology [10]. The multiple ontology semantic reasoning is not possible instead of single ontology semantic reasoning.…”