Expertise finding is the use of tools for finding and assessing individual expertise, with particular focus on scientific expertise. In recruitment industry, e.g., LinkedIn, expertise finding is the problem of searching for hiring candidates given certain required skills as well as other types of information need, such as, location and companies.
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Importance of expertise
It can be argued that human expertise is more valuable than capital, means of production or intellectual property. Contrary to expertise, all other aspects of capitalism are now relatively generic: access to capital is global, as is access to means of production for many areas of manufacturing. Intellectual property can be similarly licensed. Furthermore, expertise finding is also a key aspect of institutional memory, as without its experts an institution is effectively decapitated. However, finding and “licensing” expertise, the key to the effective use of these resources, remain much harder, starting with the very first step: finding expertise that you can trust.
Until very recently, finding expertise required a mix of individual, social and collaborative practices, a haphazard process at best. Mostly, it involved contacting individuals one trusts and asking them for referrals, while hoping that one’s judgment about those individuals is justified and that their answers are thoughtful.
In the last fifteen years, a class of knowledge management software has emerged to facilitate and improve the quality of expertise finding, termed “expertise locating systems”. These software range from social networking systems to knowledge bases. Some software, like those in the social networking realm, rely on users to connect each other, thus using social filtering to act as “recommender systems”.
At the other end of the spectrum are specialized knowledge bases that rely on experts to populate a specialized type of database with their self-determined areas of expertise and contributions, and do not rely on user recommendations. Hybrids that feature expert-populated content in conjunction with user recommendations also exist, and are arguably more valuable for doing so.
Still other expertise knowledge bases rely strictly on external manifestations of expertise, herein termed “gated objects”, e.g., citation impacts for scientific papers or data mining approaches wherein many of the work products of an expert are collated. Such systems are more likely to be free of user-introduced biases (e.g., ResearchScorecard ), though the use of computational methods can introduce other biases.
More recently, LinkedIn Expertise Search introduces a hybrid approach based on user-generated data (e.g., member profiles), community-based signals (e.g., recommendations and skill endorsements) and personalized signals (e.g., social connection between searcher and results). Given required skills and other types of information need like location and industries, the system allows recruiters to search for hiring candidates amongst more than 450 million LinkedIn members. A recent survey shows that 94% of recruiters use LinkedIn to search for candidates
Examples of the systems outlined above are listed in Table 1.
Table 1: A classification of expertise location systems
Technical problems
A number of interesting problems follow from the use of expertise finding systems:
Expertise ranking
Means of classifying and ranking expertise (and therefore experts) become essential if the number of experts returned by a query is greater than a handful. This raises the following social problems associated with such systems:
Approaches for creating expertise content
Interesting expertise systems over the years
In no particular order...
Conferences
- The ExpertFinder Initiative