Ontology similarity calculation and ontology mapping are important research topics in information retrieval. By analyzing the fast algorithm for learning large scale preference relations, we propose the fast algorithm for ontology similarity measure and ontology mapping. Via the ranking learning algorithm, the ontology graph is mapped into a line consists of real numbers. The similarity between two concepts then can be measured by comparing the difference between their corresponding real numbers. The new algorithm has lower compute complexity and two experimental results show that the proposed algorithm has high accuracy and efficiency both on ontology similarity calculation and ontology mapping. Some issues are discussed in the last section as further works.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Models of labor supply derived from stochastic utility representations and discretized sets of feasible hours of work have gained popularity because they are more practical than the standard approaches based on marginal calculus. In this paper we argue that practicality is not the only feature that can be addressed by means of stochastic choice theory. This theory also offers a powerful framework for developing a more realistic model for labor supply choices, founded on individuals having preferences over jobs and facing restrictions on the choice of jobs and hours of work. We discuss and clarify how this modeling framework deviates from both the conventional discrete approach (Van Soest, 1995), as well as the standard textbook approach based on marginal calculus (Hausman, 1985). It is argued that a model based on job choice opens up for a more realistic representation of the choice environment, and consequently offers the possibility of conducting a richer set of simulations of alternative policies. Terms of use: Documents inJEL-Code: C510, J220, H240.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper discusses aspects of a framework for modeling labor supply where the notion of job choice is fundamental. In this framework, workers are assumed to have preferences over latent job opportunities belonging to worker-specific choice sets from which they choose their preferred job. The observed hours of work and wage is interpreted as the job-specific hours and wage of the chosen job. The main contribution of this paper is an analysis of the identification problem of this framework under various conditions, when conventional cross-section micro-data are applied.The modeling framework is applied to analyze labor supply behavior for married/cohabiting couples using Norwegian micro data. Specifically, we estimate two model versions with in the general framework. Based on the empirical results, we discuss further qualitative properties of the model versions. Finally, we apply the preferred model version to conduct a simulation experiment of a counterfactual policy reforms.Keywords: Labor supply, non-pecuniary job attributes, latent choice sets, random utility models, identification.JEL classification: J22, C51. Acknowledgments:We thank Jørgen Aasness, Thor Olav Thoresen, Terje Skjerpen and the participants in workshops at IZA, Bonn, and Statistics Norway for helpful comments. We are particularly grateful for the extensive and constructive criticism, help and suggestions by Thierry Magnac and two anonymous referees.

The Covid-19 crisis has forced great societal changes, including forcing many to work from home (WFH) in an effort to limit the spread of the disease. The ability to work from home has long been considered a perk, but we have few estimates of how many jobs are actually possible to be performed from home. This paper proposes a method to estimate the share of these jobs. For each occupation, we obtain a WFH friendly measure by asking respondents from Amazon Mechanical Turk (MTurk) to evaluate whether the corresponding tasks can be performed from home based on the descriptions from the International Standard Classification of Occupations 2008 (ISCO-08) standard. The share of WFH friendly jobs in an economy can then be estimated by combining these measures with the labor statistics on occupational employments. Using Norway as an illustrating example, we find that approximately 38% of Norwegian jobs can be performed from home. The Norwegian results also suggest that the pandemic and the government’s attempts to mitigate this crisis may have a quite uneven impact on the working population. Those who are already disadvantaged are often less likely to have jobs that can be performed from home.