Shared posts

04 Dec 09:14

A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain

We face the complex problem of timely, accurate and mutually satisfactory mediation between job offers and suitable applicant profiles by means of semantic processing techniques. In fact, this problem has become a major challenge for all public and private recruitment agencies around the world as well as for employers and job seekers. It is widely agreed that smart algorithms for automatically matching, learning, and querying job offers and candidate profiles will provide a key technology of high importance and impact and will help to counter the lack of skilled labor and/or appropriate job positions for unemployed people. Additionally, such a framework can support global matching aiming at finding an optimal allocation of job seekers to available jobs, which is relevant for independent employment agencies, e.g. in order to reduce unemployment.
04 Dec 09:14

Maintenance of Profile Matchings in Knowledge Bases

A profile describes a set of properties, e.g. a set of skills a person may have or a set of skills required for a particular job. Profile matching aims to determine how well a given profile fits to a requested profile. Profiles can be defined by filters in a lattice of concepts derived from a knowledge base that is grounded in description logic, and matching can be realised by assigning values in [0,1] to pairs of such filters: the higher the matching value the better is the fit. In this paper the problem is investigated, whether given a set of filters together with matching values determined by some human expert a matching measure can be determined such that the computed matching values preserve the rankings given by the expert. In the paper plausibility constraints for the values given by an expert are formulated. If these plausibility constraints are satisfied, the problem of determining a ranking-preserving matching measure can be solved.
04 Dec 09:14

MaF: An Ontology Matching Framework

In this work, we present our experience when developing the Matching Framework (MaF), a framework for matching ontologies that allows users to configure their own ontology matching algorithms and it allows developers to perform research on new complex algorithms. MaF provides numerical results instead of logic results provided by other kinds of algorithms. The framework can be configured by selecting the simple algorithms which will be used from a set of 136 basic algorithms, indicating exactly how many and how these algorithms will be composed and selecting the thresholds for retrieving the most promising mappings. Output results are provided in a standard format so that they can be used in many existing tools (evaluators, mediators, viewers, and so on) which follow this standard. The main goal of our work is not to better the existing solutions for ontology matching, but to help research new ways of combining algorithms in order to meet specific needs. In fact, the system can test more than 6 * 136! possible combinations of algorithms, but the graphical interface is designed to simplify the matching process.
04 Dec 09:14

Extending Knowledge-Based Profile Matching in the Human Resources Domain

In the Human Resources domain the accurate matching between job positions and job applicants profiles is crucial for job seekers and recruiters. The use of recruitment taxonomies has proven to be of significant advantage in the area by enabling semantic matching and reasoning. Hence, the development of Knowledge Bases (KB) where curricula vitae and job offers can be uploaded and queried in order to obtain the best matches by both, applicants and recruiters is highly important. We introduce an approach to improve matching of profiles, starting by expressing jobs and applicants profiles by filters representing skills and competencies. Filters are used to calculate the similarity between concepts in the subsumption hierarchy of a KB. This is enhanced by adding weights and aggregates on filters. Moreover, we present an approach to evaluate over-qualification and introduce blow-up operators that transform certain role relations such that matching of filters can be applied.
04 Dec 09:14

An Overview of Knowledge Management Techniques for e-Recruitment

The number of potential job candidates and therefore, costs associated with their hiring, has grown significantly in the recent years. This is mainly due to both the complicated situation of the labour market and the increased geographical flexibility of employees. Some initiatives for making the e-Recruitment processes more efficient have notably improved the situation by developing automatic solutions. But there are still some challenges that remain open since traditional solutions do not consider semantic relations properly. This problem can be appropriately addressed by means of a sub discipline of knowledge management called semantic processing. Therefore, we overview the major techniques from this field that can play a key role in the design of a novel business model that is more attractive for job applicants and job providers.
04 Dec 09:14

Automated Knowledge Base Management: A Survey

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of the tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, and significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable.
04 Dec 09:14

Evaluation of two heuristic approaches to solve the ontology meta-matching problem

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.
04 Dec 09:14

An Overview of Current Ontology Meta-Matching Solutions

Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.
04 Dec 09:14

Automatic Recommendation of Prognosis Measures for Mechanical Components based on Massive Text Mining

Automatically providing suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an...

Jorge Martínez Gil, Bernhard Freudenthaler, Thomas Natschläger (2017) Proceedings of the 19th International Conference on Information Integration and Web-based Applications and Services, iiWAS 2017, Salzburg, December 4-6, 2017 https://doi.org/10.1145/3151759.3151774 : 32-39
04 Dec 09:14

An overview of current ontology meta-matching solutions

Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thres...

Jorge Martínez Gil, José Francisco Aldana Montes (2012) Knowledge Eng. Review https://doi.org/10.1017/S0269888912000288 27: 4 393-412