MIM1: Interlinking Data

Introduction

This MIM helps cities and communities overcome the challenges of disconnected data sources that reside in different contexts of diverse systems. It provides recommendations on how diverse data sources can be contextualised to a city and community's operational environment, consistently combined, interrelated, and interpreted so meaningful insights can be derived.

Use Cases

1. Flanders: How to make linked data (or graph databases in general) more accessible to lay people. Investments in setting up a linked data environment often are nullified by people that do not now how to handle it, and thus reduce it to tabular data and spreadsheets

Objectives

This MIM supports interlinking datapoints across domains, departments or use cases. It aims to realise semantic interoperability across potentially different data models and ontologies, by providing mappings, translations, linking databases, etc.

It also includes a consistent set of identifiers of individual instances of each entity, so that data about any entity can be combined with other data referring to that entity, and every instance of that entity, in the confidence that they refer to the same thing.

Capabilities

TBD

Requirements

TBD

Mechanisms

TBD

Specifications

TBD

Interoperability Guidance

Graph Databases and Interoperability

Graph databases increase interoperability by providing a flexible data model that naturally represents relationships and connections. They store data in nodes, edges, and properties, offering an intuitive structure that mirrors real-world networks. This model allows for easy integration of diverse data sources without predefined schemas, accommodating changes and extensions effortlessly. The use of open standards and query languages like Cypher and SPARQL further ensures that graph databases can interact with other systems and data formats, enhancing overall interoperability.

Linked Data and Interoperability

Linked Data enhances interoperability by using standard web technologies to connect disparate data sources across different domains. It employs URIs to unambiguously identify resources and RDF (Resource Description Framework) to structure and link data sets. By adhering to open standards such as SPARQL for querying and OWL (Web Ontology Language) for defining relationships, Linked Data enables seamless integration and interaction between diverse data systems. This approach not only facilitates the sharing and reuse of data but also ensures that information remains accessible and meaningful across various applications and platforms.

Conformance and compliance testing

TBD