Modelling the Semantic Web using a Type System

TL;DR

This paper proposes modeling the Semantic Web as a type system that enables symbolic representation, reasoning, and resolving semantic heterogeneity, integrating analytics like machine learning for enhanced data inference.

Contribution

It introduces a novel type system framework for the Semantic Web that supports symbolic data modeling, inductive reasoning, and dynamic semantic heterogeneity resolution.

Findings

Supports symbolic representation of linked data

Enables inductive reasoning with integrated analytics

Provides on-the-fly semantic heterogeneity resolution

Abstract

We present an approach for modeling the Semantic Web as a type system. By using a type system, we can use symbolic representation for representing linked data. Objects with only data properties and references to external resources are represented as terms in the type system. Triples are represented symbolically using type constructors as the predicates. In our type system, we allow users to add analytics that utilize machine learning or knowledge discovery to perform inductive reasoning over data. These analytics can be used by the inference engine when performing reasoning to answer a query. Furthermore, our type system defines a means to resolve semantic heterogeneity on-the-fly.