Folksonomy and taxonomy are both methods that are commonly used to organize information. But there's a big difference between folksonomy and taxonomy.
Folksonomy and taxonomy are both methods that are commonly used to organize and label data and digital content, often through tags. But while they both try to solve the same issue, there are major differences between the two in how they deal with this information. Let’s discuss the difference between folksonomy and taxonomy, as well as the new intelligent folksonomy.
An introduction to taxonomy
Let’s start with taxonomy. It’s a structured, hierarchical way to sort information based on its shared characteristics. They’re added by the person who owns or generates the content. The aim is to create an organized way of categorizing material, often so that the information is easy to access. They're often used to organize websites or content repositories.
But taxonomy comes with some problems.
One issue is that can be time-consuming and costly. It also often uses language that’s familiar to a professional audience, but perhaps not so for end users of a product.
There are also instances of when content owners don’t use particularly accurate or informative tagging systems. Sometimes, the categorization method doesn’t make much sense to users. In other cases, it may just be too complicated to understand easily. For instance, a professional may think a certain article is about “artificial intelligence”, but your users might really find it by looking for “robots.”
All of this can make it tough for users to find the information they’re looking for.
This is where folksonomy becomes useful
Folksonomy is based on tags that are added by the consumers of content, rather than its creator. In other words, it’s the “folk” that add the tags. This system doesn’t use a pre-specified hierarchy to categorize content. Instead, consumers add their own labels they think are useful to sort the information, using whatever words they like.
You can see this on websites where users can apply their own tags to information, like image hosting site Flickr. These tags end up being written in natural language, rather than a forced or formal list.
Folksonomy is a powerful tool when lots of users all add tags to a single piece of information. Companies can use data about what users are adding to come up with better ways to structure content, and so help them find what they’re looking for. It’s also scalable and fast to use.
The issues with standard folksonomy
While folksonomy does help deal with some of the weaknesses in taxonomy, it comes with issues of its own.
One is that it can be a bit chaotic. Think about labeling a color. One person might say it looks like “teal”, another think it’s “turquoise”, and some may categorize it as simply “blue” or “green.” You can end up with too many different tags to label the same content.
These types of tags also have a degree of ambiguity, precisely because there aren’t any strict guidelines.
Another issue is with abbreviations or acronyms, which can lead to confusion between similar topics or words. For instance, folksonomy may have trouble separating terms like SEALs - the Sea Air Land Forces in the Navy - and seals the animal. It also doesn’t do a very good job of dealing with synonyms, or with highly technical terms.
The next step: Intelligent folksonomy
Though standard folksonomy is a useful tool, there are lots of problems and confusion caused by its lack of linguistic control. That means that you often can't get insights from your non-structured data. This is why there are new intelligent folksonomy solutions, based on machine learning and Large Language Model (LLM), to help you pinpoint the exact information you need.
A Large Language Model (LLM) software is a very useful tool in the field of generative artificial intelligence (IAG). This type of software is able to analyse large amounts of data and unstructured text to extract valuable information and create accurate predictive models. Moreover, when trained with a large amount of information, it can understand and generate natural language with high accuracy. This means it can be used in a wide variety of applications, from chatbots and virtual assistants to recommender systems and data analytics. Large Language Model software is an essential tool for any company that wants to take advantage of its data and improve its efficiency and accuracy in decision making.
These next-generation tagging systems take advantage of advances in technology to give your data intelligent tags. This means there is no longer the need for time-consuming manual labor to create and define tags and structures.
This goes a long way towards resolving many of folksonomy's problems, while still allowing you to benefit from its natural, intuitive language.
One of these is our own user-friendly intelligent folksonomy software. The software lets you merge synonyms, separate homonyms, add a technical or customized dictionary for your specific needs, and even reduce tags through a black list. Its smart algorithms also take into account errors and duplicate content.
Standard folksonomy provides lots of valuable information and data. Today's new intelligent folksonomy tools allow you to get useful insights from that data.
Are you interested in learning more about Bismart’s Folksonomy software? Contact us to find out how our solutions can help you today.
