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