Unstructured data sources like websites, blogs, and PDFs can be incredibly valuable to knowledge bases. Still, it can also be the most challenging to integrate accurately. Read on to understand integrating unstructured data sources into your knowledge base.
Definition of Unstructured Data
What is unstructured data? It’s information that is not formatted in a way that’s easy for software applications to read. According to Egnyte, “Usually text-heavy, unstructured data cannot be stored in cells or in a file structure, such as a CSV (comma separated value) or a tab-delimited text file.”
Types of Structured and Unstructured Data
- Structured data is information entered into a database or spreadsheet in a structured format, such as a table or list. This data can be easily queried and analyzed with existing software tools.
- Unstructured data refers to information that has not been organized, including documents, images, videos and audio files.
- Semi-structured data falls somewhere in between. It’s structured enough to be queried and analyzed but not as easily as structured data.
- Hybrid data is a combination of both structured and unstructured data.
- Semi-structured data is a hybrid data you might use when you have an existing database where some of your records have more information than others.
Imagine a world where you could quickly and easily access your organization’s unstructured data. Imagine being able to search for anything and everything – from keywords or concepts to known entities, connected documents, and beyond.
Challenges with Gaining Access to Unstructured Information
If you’re an enterprise, you’ve probably accumulated a lot of unstructured data in one form or another. The challenge is that most people don’t know how to access it, and even if they do, it can be difficult and time-consuming to make sense of it all.
Benefits of a Well-Structured Knowledge Graph
A well-structured knowledge graph allows your knowledge base to return more relevant search results, increases information discoverability and eliminates duplicate content. This is particularly important for companies that depend on high-quality content marketing campaigns to drive traffic and boost their SEO rankings.
Considerations in Structuring Your Knowledge Graph
Structuring your knowledge graph is not an easy task and requires analysis of your business goals, target audience, and types of unstructured data you’re working with. However, there are a few key elements that you should consider when structuring your knowledge graph:
1) What do you want to achieve?
2) Who is your target audience?
3) What types of unstructured data do you have?
4) What is your business goal?
5) How will you structure your knowledge graph?
6) Where will you store your knowledge graph?
The structure is just as important as the content for knowledge graphs. It’s not enough to just load up your knowledge graph with data. You have to structure it well, too. Otherwise, you won’t be able to get much value out of it.
Adding Structure Through Process Automation
While process automation may sound complex, it simply means setting up rules and triggers so that unstructured data sources are automatically added to your knowledge base regularly. You can do this through simple software tools like Zapier or IFTTT.
Semantic Graph Database Integration for Massive Scale
Semantic graph databases provide a platform for bridging enterprise data silos and enriching unstructured data sources. In short, they can help you manage billions of entities in your knowledge base and hundreds of millions of relationships between them.
To successfully integrate unstructured data sources with a knowledge base, you must first map out your goals and objectives. Then use existing documents as a reference and build from there.