Semantic Technology to Exploit Digital Content Exposed as Linked Data

TL;DR

This paper explores how semantic technology can facilitate the use and reuse of Linked Data by proposing a context-dependent semantic similarity measure for resource comparison, aiding data resource sifting.

Contribution

It introduces a novel semantic similarity assessment method for Linked Data resources, addressing scalability issues and providing recommendations for broader web data integration.

Findings

Semantic similarity effectively compares Linked Data datasets.

Recommendations for scaling similarity assessment on the Web of Data.

Semantic similarity as a key technology for resource sifting.

Abstract

The paper illustrates the research result of the application of semantic technology to ease the use and reuse of digital contents exposed as Linked Data on the web. It focuses on the specific issue of explorative research for the resource selection: a context dependent semantic similarity assessment is proposed in order to compare datasets annotated through terminologies exposed as Linked Data (e.g. habitats, species). Semantic similarity is shown as a building block technology to sift linked data resources. From semantic similarity application, we derived a set of recommendations underlying open issues in scaling the similarity assessment up to the Web of Data.