Abstract
The aim of this paper is to present some methods to expand user queries and a performance evaluation to retrieve public procurement notices in the e-Procurement sector using semantics and linking open data. Taking into account that public procurement notices contain information variables like type of contract, region, duration, total value, target enterprise, etc. different methods can be applied to expand user queries easing the access to the information and providing a more accurate information retrieval system. Nevertheless expanded user queries can involve an extra-time in the process of retrieving notices. That is why a performance evaluation is outlined to tune up the semantic methods and the generated queries providing a scalable and time-efficient system. On the other hand this system is based on the use of semantic web technologies so it is necessary to model the unstructured information included in public procurement notices (organizations, contracting authorities, contracts awarded, etc.), enrich that information with existing product classification systems and linked data vocabularies and publish the relevant data extracted out of the notices following the linking open data approach. In this new LOD realm these techniques are considered to provide added-value services like search, matchmaking geo-reasoning, or prediction, specially relevant to small and medium enterprises (SMEs).
This work is part of ‘10ders Information Services project’ ( http://rd.10ders.net/ ) partially funded by the Spanish Ministry of Industry, Commerce and Tourism with code TSI-020100-2010-919, leaded by ‘Gateway Strategic Consultancy Services’ and developed in cooperation with ‘EXIS TI’ and WESO Research Group.
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Álvarez, J.M., Labra, J.E., Calmeau, R., Marín, Á., Marín, J.L. (2011). Query Expansion Methods and Performance Evaluation for Reusing Linking Open Data of the European Public Procurement Notices. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_50
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DOI: https://doi.org/10.1007/978-3-642-25274-7_50
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