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Adaptable Content Retrieval for Cognitive Filtering of Sites and Content According to Job Role Skill Set Taxonomy

IP.com Disclosure Number: IPCOM000219548D
Publication Date: 2012-Jul-05
Document File: 3 page(s) / 31K

Publishing Venue

The IP.com Prior Art Database

Abstract

This publication describes a method to reduce cognitive load by filtering search queries and results according to job role skill sets and expertise taxonomy. Individuals with the same or similar job role skill set descriptions are analyzed to identify their common preferences for content repositories and sources of content for searching, so that repositories can be queried according to rank in the overall usefulness to a given Job-Role-Skill. Content is analyzed for expertise level so that the system is better able to predict the content that is of the greatest value and least cognitive load for the person accessing the content.

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Adaptable Content Retrieval for Cognitive Filtering of Sites and Content According to Job Role Skill Set Taxonomy

Individuals who are using search in order to support their work activities are often bombarded with content that is from thousands or millions of sources that are not necessarily relevant to what they are trying to accomplish. While two people may both be querying for "Android* Accessibility" for example, someone in sales might be looking for examples to bolster a sales lead while someone in development is more likely looking for details of the Android Accessibility API. Clearly more carefully stating the search criteria could help to return different results. Additionally, the sales person might choose to search specific accessibility market insight resources and the developer might choose to search a site targeting developers.

Current options require more careful crafting the search and thought regarding the search string and which site will be used to conduct the search. In a large-scale enterprise, the time taken by employees to figure out the best sites to reduce search query results adds up. Consider that an enterprise with 440,000 employees each taking just 10 seconds more per query on 300 queries a year adds up to 63 person-years in lost productivity.

This article describes a method to reduce cognitive load by filtering search queries and results according to job role skill sets and expertise taxonomy. Individuals with the same or similar job role skill set descriptions are analyzed to identify their common preferences for content repositories and sources of content for searching, so that repositories can be queried according to rank in the overall usefulness to a given a combination of job, role and skill. Content is analyzed for expertise level so that the system is better able to predict the content that is of the greatest value and least cognitive load for the person accessing the content.

Existence of a Job Role Skill Set Database

This method requires that a database with job roles, skill sets and individual mastery (Job Role Skill Set database) exists. The purpose of the central database is to enable an integrated search engine to leverage the information without requiring that the user enter this information for each search or provide personal configuration options for the search engine with this information. The enterprises already have such databases as part of their Human Resources organization and that database can be leveraged for the search. Having information about the skill level of individual enables the search engine to return search results based on position in a skills hierarchy. More advanced topics are displayed to those who have more advanced backgrounds and needs.

Existing metadata standards, such as Dublin Core** and the IEEE*** Learning Object Metadata standards, provide standard ways of describing roles, skills and skill levels. While those standards enable the ability to find data of...