Diachronic Linked Data: Towards Long-Term Preservation of Structured Interrelated Information

TL;DR

This paper addresses the challenge of preserving evolving linked data over time, emphasizing the importance of managing temporal aspects to ensure long-term accessibility and consistency of interconnected datasets.

Contribution

It introduces a conceptual approach for long-term preservation of diachronic linked data, highlighting the significance of temporal management in maintaining data integrity.

Findings

Identified key challenges in preserving evolving linked data.

Proposed a direction for solutions addressing temporal issues.

Motivated by real-world use cases demonstrating the need for diachronic data management.

Abstract

The Linked Data Paradigm is one of the most promising technologies for publishing, sharing, and connecting data on the Web, and offers a new way for data integration and interoperability. However, the proliferation of distributed, inter-connected sources of information and services on the Web poses significant new challenges for managing consistently a huge number of large datasets and their interdependencies. In this paper we focus on the key problem of preserving evolving structured interlinked data. We argue that a number of issues that hinder applications and users are related to the temporal aspect that is intrinsic in linked data. We present a number of real use cases to motivate our approach, we discuss the problems that occur, and propose a direction for a solution.