A Meaning-oriented Approach to Semantic Data Modeling

TL;DR

This paper introduces a meaning-oriented, graph-based semantic data model that uses subjective meanings and semantic graphs to improve semantic representation and address issues like dynamic specialization and natural join.

Contribution

It proposes a novel meaning-oriented approach to semantic data modeling using semantic graphs, enhancing flexibility over traditional entity-relationship models.

Findings

Addresses semantic representation issues effectively

Supports dynamic specialization and natural join

Provides a flexible, meaning-based semantic modeling framework

Abstract

Semantic information is often represented as the entities and the relationships among them with conventional semantic models. This approach is straightforward but is not suitable for many posteriori requests in semantic data modeling. In this paper, we propose a meaning-oriented approach to modeling semantic data and establish a graph-based semantic data model. In this approach we use the meanings, i.e., the subjective views of the entities and relationships, to describe the semantic information, and use the semantic graphs containing the meaning nodes and the meta-meaning relations to specify the taxonomy and the compound construction of the semantic concepts. We demonstrate how this meaning-oriented approach can address many important semantic representation issues, including dynamic specialization and natural join.