Many organizations have their own methods of tagging their content; however, these are typically manual. Manually tagging entire databases or content management systems (CMS), file by file, is very time consuming and involves a lot of people.
An equally important downfall to these methods is that they are largely text-based. Simple text-based tagging is arguably a thing of the past, since it cannot keep up with the high volumes of content organizations use. In search engines, text-based tagging can only return results on exact keywords, whereas concept tagging can return results based on a much more diverse profile of attributes.
An advanced tagging method that is built off semantic concept tags allows organizations to better structure their databases and CMS as well as build intelligent search engines and robust recommender systems.
Gartner Inc, : ‘Gartner Critical Capabilities for Metadata Management Solutions’ (Mark Beyer, et al, November 2020)
The biggest challenges for organizations using manual text tags.
Quality of tags decreases over time
Why concept-based tagging is better than text-based tagging.
When an auto classification strategy is driven by simple text-based tags, the search engine can only retrieve information based on the exact terminology. Therefore, every word that the user enters in a search field should be extremely precise and relevant. On an ecommerce site, if a user wants to buy a blue cardigan, they would have to enter “blue cardigan” into the search field.
The advantage to concept tagging is that users can enter unspecific language or multiple keywords, and the search engine could retrieve the precise results that they want. For example, if the same user wants to buy a cardigan but can’t remember the name “cardigan,” entering “blue sweater” in the search field can still retrieve results for a cardigan because sweater and cardigan are bundled together in one concept.
On the left side of the screenshot, these concepts are organized into a hierarchical taxonomy which gives structure to the documents and their tags – fulfilling the final step of auto classification. The concepts can be automatically sorted into their corresponding classes and concept schemes in the taxonomy through predefined rules that have been set up in the thesaurus structure. The benefit to maintaining tags in a taxonomy is the consistency it provides through its hierarchical structure and controlled vocabularies.
Adding Knowledge Graphs to strengthen concept tagging.
In a CMS, documents can be tagged with authors, topics, authoring dates, etc. If a user is looking for a document by one particular author, all those documents tagged with the same author will be retrieved so that the user does not have to sift through the whole database. The user can also locate documents more easily based on their classification, e.g. searching for news items vs. event articles.
Even more, concept tagging serves as a fundamental step to making graph-based recommender engines. Semantic graph-based recommender systems are the powerful alternative to standard search for their ability to suggest smarter results based on the user’s interactions with a platform and understanding of context and meaning.
If a research team for a pharmaceutical company is trying to write a paper on heart-related conditions. If the user searches for “heart rate,” they will only be given results that explicitly talk about heart rate. With a graph-based recommender system, the user gets the obvious results as well as intelligent “further reading” suggestions. I.e. you type in the words “heart rate” and get documents also relating to heart diseases, abnormalities, etc. relating to heart rate; in this case, the recommender system understands that one thing affects the other. Altogether, the content creation process is much easier and helpful.
The metadata from the semantic concept tags helps the user become better oriented to their CMS so they can use it more efficiently.
Experience the major benefits of concept tagging with PoolParty PowerTagging.
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