Malgorzata Mochol

The Impact of Semantic Web Technologies on Job Recruitment Processes

Using Semantic Web technologies in the domain of online recruitment could substantially increase market transparency, lower the transaction costs for employers, and change the business models of the intermediaries involved. In this paper, we describe how online recruitment processes can be streamlined using Semantic Web technologies. We analyze the realization chances of such a scenario from the technological and business viewpoint. We describe a prototypical implementation of the required technological infrastructure and analyze the potential economic impacts, the interests of the involved parties, and potential changes to their business models.

Improving the accuracy of job search with semantic techniques

In this paper we introduce a prototype job portal which uses semantically annotated job offers and applicants. In our opinion, using Semantic Web technologies substantially increase market transparency, lower transaction costs and speed up the procurement process. However adding semantics is not a panacea for everything. We identify some outstanding problems in job search using the system and outline how the technique of query approximation can be the basis for a solution. Through an Industry-Research co-operation we are extending the prototype with these semantic techniques to demonstrate a more accurate job search.

Combining RDF Vocabularies for Expert Finding

This paper presents a framework for the reuse and extension of existing, established vocabularies in the Semantic Web. Driven by the primary application of expert finding, we will explore the reuse of vocabularies that have attracted a considerable user community already (FOAF, SIOC, etc.) or are derived from de facto standards used in tools or industrial practice (such as vCard, iCal and Dublin Core). This focus guarantees direct applicability and low entry barriers, unlike when devising a new ontology from scratch. The Web is already populated with several vocabularies which complement each other (but also have considerable overlap) in that they cover a wide range of necessary features to adequately describe the expert finding domain. Little effort has been made so far to identify and compare existing approaches, and to devise best practices on how to use and extend various vocabularies conjointly. It is the goal of the recently started ExpertFinder initiative to fill this gap. In this paper we present the ExpertFinder framework for reuse and extension of existing vocabularies in the Semantic Web. We provide a practical analysis of overlaps and options for combined use and extensions of several existing vocabularies, as well as a proposal for applying rules and other enabling technologies to the expert finding task.

Towards a Rule-Based Matcher Selection

The central problems w.r.t. interoperability and data integration issues in the Semantic Web are schema and ontology matching approaches. Today it takes an expert to determine the best algorithm and a decision can usually be made only after experimentation, so as both the necessary scaling and off-the-shelf use of matching algorithms are not possible. To tackle these issues, we present a rule-based evaluation method in which the best algorithms are determined semi-automatically and the selection performs prior to the execution of an algorithm.

Cost Estimation for Ontology Development: Applying the ONTOCOM Model

Techniques for reliably estimating development efforts are a fundamental requirement for a wide-scale dissemination of ontologies in real-world application settings. The parametric cost estimation model ONTOCOM is a first attempt to cope with the lack of instruments for business measurements in the field of ontology engineering. This paper is intended as a user guide to ONTOCOM. It describes how ontology engineers can customize the general-purpose model to particular process models and subsequently apply it in various development phases to calculate the estimated efforts.

Achieving Maturity: The State of Practice in Ontology Engineering in 2009

In this paper we give an account of the current state of practice in ontology engineering (OE) based on the findings of a 6 months empirical survey that analyzed 148 OE projects. The survey focused on process-related issues and looked into the impact of research achievements on real-world OE projects, the complexity of particular ontology development tasks, the level of tool support, and the usage scenarios for ontologies. The main contributions of this survey are twofold: 1) the size of the data set is larger than every other similar endeavor; 2) the findings of the survey confirm that OE is an established engineering discipline w.r.t the maturity and level of acceptance of its main components, methodologies, etc. whereas further research should target economic aspects of OE and the customization of existing technology to the specifics of vertical domains.

Semantic-Web-Technologien in der Arbeitsplatzvermittlung

ZusammenfassungThe importance of the Internet for job procurement is increasing for the reason that three quarters of the people in the employment age are online. On the other hand because ever more companies are publishing their job offers on the Web. However, due to the large number of openings published online it is almost impossible for job seekers and job portals to gain an overview of the entire employment market. Since job offers lack semantically meaningful annotations, the search and integration into databases are made highly difficult. Applying Semantic Web technologies to the e-recruitment process provides advantages for all participants in the market. In this paper we describe a method for analysing the domain-specific language of an application domain. We use this method to describe the e-recruitment process and the necessary ontologies for annotating job offers and job applications. In conclusion, we present the prototypical implementation of the scenario based on Semantic Web, especially semantic matching.

Trend Ontology for Knowledge-Based Trend Mining in Textual Information

Providing ontologies for the automatic trend detection enhance the quality of trend predictions. However, in the case of dynamic and fuzzy expert knowledge like the knowledge used in trend detection, it is difficult to formalize knowledge unambiguously and in a static way. In this paper we report on our experiences in modeling and formalizing trend ontology for automatic knowledge-based trend detection by the example of market research, i.e. we describe the knowledge-based trend mining approach and requirements for trend ontology, discuss obstacles in modeling trend knowledge and outline three lightweight trend ontologies modeled.

A High-Level Architecture of a Metadata-based Ontology Matching Framework

One of the pre-requisites for the realization of the semantic Web vision are matching techniques which are capable of handling the open, dynamic and heterogeneous nature of the semantic data in a feasible way. Currently this issue is not being optimally resolved; the majority of existing approaches to ontology matching are (implicitly) restricted to processing particular classes of ontologies and thus unable to guarantee a predictable result quality on arbitrary inputs. Accounting for the empirical findings of two case studies in ontology engineering, we argue that a possible solution to cope with this situation is to design a matching strategy which strives for an optimization of the matching process whilst being aware of the inherent dependencies between algorithms and the types of ontologies these are able to process successfully. We introduce a matching framework that, given a set of ontologies to be matched described by ontology metadata, takes into account the capabilities of existing matching algorithms (matcher metadata) and suggests, by using a set of rules, appropriate ones

Expertise bewerben und finden im Social Semantic Web

Im vorliegenden Beitrag diskutieren wir Rahmenbedingungen zur Kombination, Wiederverwendung und Erweiterung bestehender RDFVokabulare im Social Semantic Web. Hierbei konzentrieren wir uns auf das Anwendungsszenario des Auffindens und Bewerbens von Experten im Web oder Intranet. Wir prasentieren, wie RDF-Vokabulare einerseits und de facto Standardformate andererseits, die von taglich verwendeten Applikationen benutzt werden (z. B. vCard, iCal oder Dublin Core), kombiniert werden konnen, um konkrete Anwendungsfalle der Expertensuche und zum Management von Expertise zu losen. Unser Fokus liegt darauf aufzuzeigen, dass fur praktische Anwendungsszenarien nicht notwendigerweise neue Ontologien entwickelt werden mussen, sondern der Schlussel vielmehr in der Integration von bestehenden, weit verbreiteten und sich erganzenden Formaten zu einem koharenten Netzwerk von Ontologien liegt. Dieser Ansatz garantiert sowohl direkte Anwendbarkeit von als auch niedrige Einstiegsbarrieren in Semantic Web-Technologien sowie einfache Integrierbarkeit in bestehende Applikationen. Die im Web verfugbaren und verwendeten RDFFormate decken zwar einen grosen Bereich der Aspekte zur Beschreibung von Personen und Expertisen ab, zeigen aber auch signifikante Uberlappungen. Bisher gibt es wenig systematische Ansatze, um diese Vokabulare zu verbinden, sei es in Form von allgemeingultigen Praktiken, die definieren, wann welches Format zu benutzen ist, oder in Form von Regeln, die Uberlappungen zwischen einzelnen Formaten formalisieren. Der vorliegende Artikel analysiert, wie bestehende Formate zur Beschreibung von Personen, Organisationen und deren Expertise kombiniert und, wo notig, erweitert werden konnen. Daruber hinaus diskutieren wir Regelsprachen zur Beschreibung von Formatuberlappungen sowie deren praktische Verwendbarkeit zur Erstellung eines Ontologie-Netzwerks zur Beschreibung von Experten.