Creating article tags in Pega Knowledge
|Description||Pega Knowledge article tags|
|Version as of||8.4|
|Application||Pega Customer Service|
|Capability/Industry Area||Knowledge Management|
Pega Knowledge provides an article tagging capability that helps increase the searchability of articles. Tags reflect terms (single or multi-word) that authors anticipate end users may use during searches for knowledge articles. Tags can be entered manually by authors when creating or editing articles, or by using auto-tagging. Auto-tagging uses AI to detect topics in the article title, abstract, and content body. This configurable option supports suggesting from 1-5 tags only when an article is first created and submitted. Authors can delete unwanted tags and manually add tags, providing control and flexibility when creating articles.
Pega uses the Elasticsearch engine for case and article searching. During article searches, the engine parses through an index consisting of article attributes: article title, article abstract, article body, tags, and file attachments (when enabled). Most search 'hits' will be found in the title or article body and the search results will include articles with the search terms. All end users do not think alike, nor search alike. Some end users may search for articles using a term or string that may not be in the article at all. Pega Knowledge provides out-of-the-box reports that list the most common search terms with no results (Top search strings in Interaction Portal with no articles and Top search strings with no articles for a selected help site). These reports can be used to help identify tags that may be required to increase the searchability on articles.
For example, a healthcare provider may have a number of articles describing the details around the Affordable Care Act or ACA. But if end users, including external customers, are not familiar with the official name of the act, they may search for 'obamacare'. Adding this term as a tag would increase the searchability of the related ACA articles.
- Use terms that you expect end users (CSRs and external customers) to use when searching for an article
- Make sure the tags are relevant to the article and are not common words that could be found in other articles
- Use an underscore character between words in a multi-word tag, for example, credit_card_fraud
Auto-tagging is enabled in the Knowledge Management Authoring portal (Configurations > Auto tagging). Pega recommends setting the number of tag suggestions to 3 (options range from 1-5).
Boost search allows organizations to modify or fine-tune the display order of search results. For example, if you prefer to give more prevalence in searching to article tags, you could assign a Boost Score of 5 to Article tags, and perhaps a 3 to Article title. When displaying search results, articles with a tag that matches the search term will be placed at the top of the search results, followed by articles with the search term found in the title, and so on.