To stay competitive within the market, organizations need to accurately understand the skills and competencies of their human resources to better utilize them and more effectively respond to internal and external demands for expertise. This is particularly important for most services organizations which provide a variety of products and services to multiple and changing clients. The ability to accurately locate experts is also important to knowledge workers. From this perspective, finding individuals with appropriate knowledge and skills is important for accomplishing knowledge intensive tasks and solving complex problems. This thesis focuses on the problem of representing and reasoning about skills and competencies over time for more effective human resources management and expert finding. Proper modeling of skills and competencies provides, among other things, a common language for assessments, a foundation for consistent interviewing, a linkage between performance management and learning, and a means for aligning business strategy and skills for driving organizational performance. It also improves knowledge management by making explicit what the organization knows and can perform. In this thesis, we present a framework for profiling human resources over time. In particular, we use first-order logic to represent and reason about expertise, skills and competencies and capture information about sources of skill and competency information. The framework provides: - a formal ontology for skill and competency management which specifies how individuals should be represented and evaluated against a skill, - a technique for inferring and validating skills and competencies over time using different sources of information, - a systematic way of evaluating human resources in order to provide a more efficient, structured, and consistent assessment process, and - techniques for identifying unreliable sources of information and revising trust in these sources. This work enhances the ability of organizations to better utilize their human assets and improves the task of expert finding required for accomplishing knowledge intensive tasks and solving complex problems.

To stay competitive within the market, organizations need to accurately understand the skills and competencies of their human resources to better utilize them and more effectively respond to internal and external demands for expertise. This is particularly important for most services organizations which provide a variety of products and services to multiple and changing clients. The ability to accurately locate experts is also important to knowledge workers. From this perspective, finding individuals with appropriate knowledge and skills is important for accomplishing knowledge intensive tasks and solving complex problems. This thesis focuses on the problem of representing and reasoning about skills and competencies over time for more effective human resources management and expert finding. Proper modeling of skills and competencies provides, among other things, a common language for assessments, a foundation for consistent interviewing, a linkage between performance management and learning, and a means for aligning business strategy and skills for driving organizational performance. It also improves knowledge management by making explicit what the organization knows and can perform. In this thesis, we present a framework for profiling human resources over time. In particular, we use first-order logic to represent and reason about expertise, skills and competencies and capture information about sources of skill and competency information. The framework provides: - a formal ontology for skill and competency management which specifies how individuals should be represented and evaluated against a skill, - a technique for inferring and validating skills and competencies over time using different sources of information, - a systematic way of evaluating human resources in order to provide a more efficient, structured, and consistent assessment process, and - techniques for identifying unreliable sources of information and revising trust in these sources. This work enhances the ability of organizations to better utilize their human assets and improves the task of expert finding required for accomplishing knowledge intensive tasks and solving complex problems.