Comparing competences on academia and occupational contexts based on similarity measures

This paper is a first contribution of a schema to match professional and work competences through similarity measures. In this contribution we focus on the determination of connections between university profiles based in standards (body of knowledge and thesauri) similarity measures and Natural Language Processing (NLP) techniques. Our first experiments proved that this hybrid schema got a promise results in the recognition of competency patterns in order to apply in the laboral contex

A review and comparison of ontology-based approaches to robot autonomy

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.Peer ReviewedPostprint (author's final draft

Representing and Reasoning about Skills and Competencies over Time

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.Ph