The Job Posting Management Tool is a proof-of-concept application of the Personal Knowledge Management Tool. The basic steps are:
Automatically collect information from your sources and hold it until you need it.
The JPMT aggregates job postings from multiple sites into one common inbox. Clicking the List New link displays unread job postings sorted by relevance based on user-defined criteria.
Individual documents (job postings) can be flagged as opportunities worth investigating or rejected in bulk operation. This workflow gameifies the inbox and allows the user to efficiently evaluate large numbers of documents, keeping only the most relevant postings.
Automatically tag and score documents and remember what is new and what has already been read.
The following image shows the automated tags and their relevance score based on the OpenCalais API toolkit which is used for this POC to extract semantic metadata from a job post.
Using automated semantic term extraction effectively “normalizes” all jobs against a common set of tags, which allows browsing of all related jobs based on the actual contents of the job postings.
Keep what you want to keep for later reference and forget what you don’t.
Once a relevant posting has been identified and flagged as an opportunity worth pursuing, the Opportunity Dashboard gives the job-seeker a tool to quickly compare and rank all open opportunities to prioritize their activity.
Search or browse articles based on topic, importance, date, etc.
Unread job postings from the user’s inbox can be quickly browsed based on topic. This navigation allows the user to see what is new quickly and efficiently.