An Ontology for Modelling Human Resources Management based on Standards

Employment Services (ES) are becoming more and more important for Public Administrations where their social implications on sustainability, workforce mobility and equal opportunities play a fundamental strategic importance for any central or local Government. The EU SEEMP (Single European Employment Market-Place) project aims at improving facilitate workers mobility in Europe. Ontologies are used to model descriptions of job offers and curricula; and for facilitating the process of exchanging job offer data and CV data between ES. In this paper we present the methodological approach we followed for reusing existing human resources management standards in the SEEMP project, in order to build a common “language” called Reference Ontology

DEVELOPING STEP BY STEP AN ONTOLOGY TO SUPPORT HUMAN RESOURCE MANAGEMENT

Human Resources Management is aimed at ensuring all activities, development, motivation and retention of human resources within the organization to achieve its objectives with maximum efficiency and meet the needs of employees. Because the current economy is dominating by information and technology, the managers of an organization need concise and structured information to cope with problems arising in the activity they lead. The same is happening in the department of human resources, management requires structured and concise information about the employees, activities, processes, tasks, etc. in order to properly manage the entire human resources management. This paper argues for the development of an effective ontology to manage the human resources existing in an IT company.human resource management, ontology, ontology web language

Ontology-based employer demand management

Skills shortages globally pose a real and urgent need for proper investigation and workforce development planning into the future. Analysing workforce development and employer demand needs through electronic job market allows much deeper and wider research into skill shortages. Current methods do not provide the level of depth required to address such important economic implications. In this paper, we present a system aiming to gather and analyse current employer demand information from online job advertisements. It identifies current employer demand needs analysed from electronic job market

Development of an ontology for the information management of labor competencies that serves as support to the processes of the Human Management area of ??UNAB

El enfoque de competencias dentro de la gestión humana ha cobrado gran importancia en las organizaciones de hoy. Este enfoque impacta procesos críticos para el logro de los objetivos estratégicos dentro de las empresas. Por su importancia y complejidad, la información concerniente a las competencias laborales debe estar soportada por tecnologías de la información que faciliten su administración. Dada las características de la información relativa a las competencias laborales una solución informática prometedora es el uso de ontologías. Modelando con ontologías los catálogos de competencias es posible la automatización de tareas críticas de la gestión de recursos humanos Como objetivo principal de este trabajo se desarrolló una ontología para la administración basada en competencias de los recursos humanos de la UNAB. Adicionalmente se creó un repositorio de datos expresado en la ontología y se desarrolló un prototipo de un servicio de consultas semánticas. Aunque con la ontología desarrollada se pudo modelar adecuadamente la administración por competencias de los recursos humanos de la UNAB, se encontraron dificultades tanto en la creación del repositorio de datos como en el desarrollo del servicio de consultas semánticas. Para el repositorio de información no se encontró una herramienta que permitiera modelar adecuadamente la información semántica y que brindara un nivel aceptable de integridad y seguridad. En cuanto al prototipo del servicio semántico de consultas, las tecnologías escogidas para adelantar este proyecto no permitieron desarrollar consultas de alto nivel de complejidad que se requerían para comparar el perfil de una persona con el perfil requerido por un cargo específico1. INTRODUCCIÓN 1 2. DEFINICIÓN DEL PROBLEMA 3 2.1 Planteamiento del problema 3 2.2 Pregunta de investigación 4 2.3 Hipótesis 4 2.4 Objetivos 5 2.4.1 Objetivo General 5 2.4.2 Objetivos específicos 5 2.5 Resultados esperados 5 3. MARCO REFERENCIAL 6 3.1 Gestión de Recursos Humanos 6 3.2 Administración por competencias 7 3.3 Conocimiento 8 3.4 Capital intelectual 9 3.5 Gestión del conocimiento y del capital intelectual 9 3.6 Almacenamiento y Recuperación del conocimiento 10 3.7 La información en la web 10 3.8 La Web semántica 11 3.8.1 Tecnologías involucradas en la web semántica 13 3.9 Ontologías 15 3.9.1 Clasificación de las ontologías 16 3.9.2 Componentes de una Ontología 17 3.9.3 Objetivos de las Ontologías 18 3.10 Metodologías y herramientas para el desarrollo de ontologías 18 3.10.1 Metodologías 18 3.10.2 Lenguajes 19 3.10.3 Herramientas para el desarrollo de ontologías 20 3.11 Métodos para la evaluación de consultas 21 3.12 Estado del arte 23 4. DISEÑO METODOLOGICO 25 4.1 Revisión bibliográfica 25 4.1.1 Criterios de Búsqueda 25 4.1.2 Criterios de selección 25 4.2 Metodología de desarrollo del proyecto 26 4.2.1 Actividades del proyecto 27 5. DESARROLLO DEL PROYECTO 30 5.1 FASE 1: Análisis de requerimientos de información 30 5.1.1 Estructura del área de recursos humanos 30 5.1.2 Gestión por competencias en la UNAB 31 5.1.3 Participantes 32 5.2 FASE 2. Análisis y selección de Herramientas tecnológicas 32 5.2.1 Análisis de metodologías para la construcción de ontologías 32 5.2.2 Análisis de herramientas para la construcción de Ontologías 34 5.2.3 Análisis de lenguajes para el desarrollo de ontologías 35 5.2.4 Selección del repositorio de información 36 5.3 FASE 3: Desarrollo de la ontología 37 5.3.1 Especificación 38 5.3.2 Conceptualización 41 5.3.3 Formalización 45 5.3.4 Implementación 48 5.3.5 Validación y evaluación del modelo 51 5.4 FASE 4: Construcción del repositorio de información 52 5.5 FASE 5: Construcción del prototipo del módulo de consultas 54 5.5.1 Definición de consultas 54 5.5.2 Tecnologías utilizadas 54 6. RESULTADOS 56 6.1 Descripción de la ontología 56 6.2 Repositorio de información 61 6.2.1 Generación de perfiles de cargos 61 6.2.2 Generación de personas y perfiles de personas 61 6.3 Arquitectura del prototipo del servicio semántico de consultas 61 6.3.1 Capa de datos 62 6.3.2 Capa de aplicación 62 6.3.3 Capa de interface de usuario 63 6.4 Evaluación de la ontología 64 7. CONCLUSIONES 69 8. TRABAJOS FUTUROS 70 BIBLIOGRAFíA 71 ANEXOS 74 Anexo 1. Especificación de la de la ontología. 74MaestríaThe approach of labor skills within human management has become very important in today's organizations. This approach impacts critical processes to the achievement of strategic objectives within the business. Because of its importance and the complexity, the regarding labor skills must be supported by information technologies to facilitate his administration. Given the characteristics of the information on labor skills, which are usually expressed in natural language a promising computing solution is the use of ontologies. With ontologies modeling skills catalogs is possible the automation of critical tasks of human resource management. The Main objective of this work is developed ontology for competency-based human resources management for the UNAB. Additionally, a data repository expressed in the ontology and a prototype of a semantic query service was created. Although the developed ontology could adequately model the competency management of human resources for the UNAB, difficulties were encountered in the creation of the data repository and the prototype of a semantic query service. For information repository a technology that would adequately model the semantic information that would provide an acceptable level of safety and security not was found. As the prototype of semantic query service, choice of technologies to advance this project they did not allow to develop high level of complexity queries that is needed to compare the profile of a person with the right profile for a specific positio

An employer demand intelligence framework

Employer demand intelligence is crucial to ensure accurate and reliable education, workforce and immigration related decisions are made. To date, current methods have been manually intensive and expensive — providing insufficient scope of information required to address such important economic implications. This research developed an Employer Demand Intelligence Framework (EDIF) to address detailed employer demand intelligence requirements. To further the EDIF’s functionality, a semi-automated Employer Demand Identification Tool (EDIT) was developed that continuously provide such intelligence

Method for Reusing and Re-engineering Non-ontological Resources for Building Ontologies

This thesis is focused on the reuse and possible subsequent re-engineering of knowledge resources, as opposed to custom-building new ontologies from scratch. The deep analysis of the state of the art has revealed that there are some methods and tools in the literature for transforming non-ontological resources into ontologies, but with some limitations: _ Most of the methods presented are based on ad-hoc transformations for the resource type, and the resource implementation. _ Only a few take advantage of the resource data model, an important artifact for the re-engineering process [GGPSFVT08]. _ There is no any integrated framework, method or corresponding tool, that considers the resources types, data models and implementations identified in an unified way. _ With regard to the transformation approach, the majority of the methods perform a TBox transformation, many others perform an ABox transformation and some perform a population. However, no method includes the possibility to perform the three transformation approaches. _ Regarding to the degree of automation, almost all the methods perform a semi-automatic transformation of the resource. _ According to the explicitation of the hidden semantics in the relations of the resource components, we can state that the methods that perform a TBox transformation make explicit the semantics in the relations of the resource components. Most of those methods identify subClassOf relations, others identify ad-hoc relations, and some identify partOf relations. However, only a few methods make explicit the three types of relations. _ With respect to how the methods make explicit the hidden semantics in the relations of the resource terms, we can say that three methods rely on the domain expert for making explicit the semantics, and two rely on an external resource, e.g., DOLCE ontology. Moreover, there are two methods that rely on external resources but not for making explicit the hidden semantics, but for finding out a proper ontology for populating it. _ According to the provision of the methodological guidelines, almost all the methods provide methodological guidelines for the transformation. However these guidelines are not finely detailed; for instance, they do not provide information about who is in charge of performing a particular activity/task, nor when that activity/task has to be carried out. _ With regard to the techniques employed, most of the methods do not mention them at all. Only a few methods specify techniques as transformation rules, lexico-syntactic patterns, mapping rules and natural language techniques. In this thesis we have provided a method and its technological support that rely on re-engineering patterns in order to speed up the ontology development process by reusing and re-engineering as much as possible available non-ontological resources. To achieve this overall goal, we have decomposed it in the following objectives: (1) the definition of methodological aspects related with the reuse of non-ontolo-gical resource for building ontologies; (2) the definition of methodological aspects related with the re-engineering of non-ontological resources for building ontologies; (3) the creation of a library of patterns for re-engineering nonontological resources into ontologies; and (4) the development of a software library that implements the suggestions given by the re-engineering patterns. Having in mind these goals, in this chapter we present how the open research problems identified in Chapter 2 are solved by the main thesis contributions. Then, we discuss the verification of our hypotheses, and finally we provide an outlook for the future work in those topics