Linked Data Integration

Organize, connect, and standardize data to build robust datasets.

Data integration has become a critical component of data management.

As the volume of data increases, data silos are a real challenge for many companies — they prevent them from using their data to its full potential. It is essential to have automated tools that can properly connect, fuse and analyze different sources of information.

Linked Data Integration technology helps you to combine data from multiple sources into one cohesive dataset which can be used across your organization without any technical issues.

What is Linked Data Integration?

Linked Data Integration is the process of combining data from multiple sources.

The Linked Data Principles allow data to be accessed and linked in a consistent and semantically meaningful manner. This is a set of principles for publishing and using data on the web, which include:

Using URIs as identifiers: Each entity (e.g., a person, place, or thing) in the data should be identified by a unique URI so that it can be clearly distinguished from other entities.
Using HTTP URIs: URIs should be accessible via the HTTP protocol so that they can be easily dereferenced (i.e., accessed) by other systems.
Provide useful information about the URIs: When a URI is dereferenced, the information returned should be in a human- and machine-readable format and provide enough context to understand the meaning of the data.
Link to other URIs: The data should use URIs to link to other related resources on the web so that users can easily explore and discover new information.
Using existing vocabulary and ontologies to describe the data: To enable data to be linked and understood consistently, it is helpful to use existing language and ontologies that are relevant to the data and use them to describe the entities, properties, and relationships within the data.

The goal of Linked Data Integration is to make it easier to find, access, and reuse data across different systems and to enable new types of applications.

It helps create robust datasets by semantically linking and fusing data sources. And it enhances data accuracy while keeping track of the origin of the data.

What technologies do we use for Linked Data Integration?

We don’t always select the same solution when we have to define a set of tools to build a knowledge graph. It always depends on the project’s needs and can be influenced by user needs, business needs, the types of data that need to be integrated… etc. That’s why we value the fact that we can provide our customers with a hybrid solution that perfectly fits their data use case.

Just to name a few technologies we use to link and fuse datasets as one:

RDF (Resource Description Framework) RDF is a standard for representing data on the web-based on the idea of representing data as a set of triples, consisting of a subject, predicate, and object. These triples can be used to describe the relationships between entities.
SPARQL (SPARQL Protocol and RDF Query Language) SPARQL is a query language for RDF data and is used to retrieve and manipulate data stored in RDF format. It allows you to query data stored in a triple store, such as an RDF graph.
Enterprise Knowledge Graphs Enterprise Knowledge Graph (EKG) uses a similar model to extract and store data in knowledge graphs, databases specifically designed for storing and querying RDF data. It integrates and accesses information assets within an organization using data and metadata.
Protégé Protégé is a free, open-source platform for creating, editing, and sharing ontologies. It is used to represent knowledge in a structured and formal way, making it easy to share and reuse across different systems and applications.
Hanami Hanami is a data editor solution for data quality validation and lineage traceability of RDF data. It automates the creation of data models and data editing using SHACL.
RML mapper RML Mapper is a tool for mapping data from various structured and semi-structured data sources (such as CSV, JSON, and XML) to RDF triples.
Ontologies and Vocabularies To enable data to be linked and understood in a consistent way, it is important to use existing vocabulary and ontologies, such as DBpedia, schema.org, and FOAF, to describe the data and relationships between entities.

How do we ensure that data is integrated in the best way possible?

First, we want to ensure that the integrated data will be useful and meaningful for the intended use case. This is why we always start, as with any of our projects, by clearly defining the objectives and requirements of the integration. For example, the data needed and how it will be used.

Once the objectives and requirements are clear, we make sure to check the data sources to be used, considering factors such as data quality, availability, and cost. Then we define common data models and vocabulary that will be used throughout the integration. This will ensure that all the data can be linked and understood in a consistent manner.

Before integrating the data, it is very likely that it would need to be cleaned and transformed, to ensure that all of your sources are consistent with one another. For this essential step in data maintenance, we have developed a tool which you can find out more about here on Hanami – our solution for data quality validation and lineage traceability.

Once the data is integrated, we test it and validate it to ensure it meets all the objectives and requirements of the project that we have defined.

After integration, it is essential to continuously monitor and maintain the integrated data to keep it accurate and up-to-date. At this stage, we usually document the integration process including data sources, methods, and results. That can be a helpful resource in case of debugging, reproducibility and future maintenance.

Linked Data from the Web can be very tricky to convert into a healthy, robust, and unified dataset.

We’ve seen that different data sources can use a range of different vocabulary to represent data about the same concept. And on top of that, these concepts can also be identified by using different URIs, within different data sources. It is a big challenge to optimize your data before developing efficient applications.

We are very familiar with this type of challenge and thanks to our technical knowledge with a particular focus on semantic technologies we can provide you with the solutions to semantically link and fuse your data while keeping track of its origins.

We are a specialist consultancy with long experience in designing and implementing Enterprise Knowledge Graphs in government and other data-intensive sectors.
Through our combination of technical skills, industry practitioners, and expertise in open and linked data, we can help you see data challenges from a new perspective.