The Dilated Triple

TL;DR

The paper introduces a method to add context to RDF triples in the Semantic Web, enabling more human-like, context-sensitive understanding by associating triples with related statements and using graph similarity.

Contribution

It proposes a simple approach to contextualize RDF triples by linking them with related statements and applying graph similarity, enhancing relevance in semantic data retrieval.

Findings

Contextualization improves relevance of RDF triples.

Graph similarity effectively identifies related statements.

Enhanced context-awareness aligns RDF with human thought processes.

Abstract

The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.