José Emilio Labra

TOWARDS A PAN-EUROPEAN E-PROCUREMENT PLATFORM TO AGGREGATE, PUBLISH AND SEARCH PUBLIC PROCUREMENT NOTICES POWERED BY LINKED OPEN DATA: THE MOLDEAS APPROACH

This paper aims to describe a public procurement information platform which provides a unified pan-European system that exploits the aggregation of tender notices using linking open data and semantic web technologies. This platform requires a step-based method to deal with the requirements of the public procurement sector and the open government data initiative: (1) modeling the unstructured information included in public procurement notices (contracting authorities, organizations, contracts awarded, etc.); (2) enriching that information with the existing product classification systems and the linked data vocabularies; (3) publishing relevant information extracted out of the notices following the linking open data approach; (4) implementing enhanced services based on advanced algorithms and techniques like query expansion methods to exploit the information in a semantic way. Taking into account that public procurement notices contain different kinds of data like types of contract, region, duration, total amount, target enterprise, etc., various 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. Moreover, this platform is supposed to be especially relevant for SMEs that want to tender in the European Union (EU), easing their access to the information of the notices and fostering their participation in cross-border public procurement processes across Europe. Finally an example of use is provided to evaluate and compare the goodness and the improvement of the proposed platform with regard to the existing ones.

ADONIS: Automated diagnosis system based on sound and precise logical descriptions

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of Semantic Descriptions. However, soundness and efficiency of the underlying logics in these descriptions are critical to harness the potential of these systems. In this paper, we provide a well-structured ontology for automated diagnosis and a three-fold formalization based on Predicate Logic, Description Logic and Rules.

Application of the spreading activation technique for recommending concepts of well-known ontologies in medical systems

The aim of this paper is to present the application of the Spreading Activation Technique in the scope of medical systems. This technique is implemented through the ONTOSPREAD framework for the development, configuration, customization and execution of the Spreading Activation technique over graph-based structures, more specifically over RDF graphs and ontologies arising from the Semantic Web area. It has been used to the efficient exploration and querying of large and heterogeneous knowledge bases based on semantic networks in the Information and Document Retrieval domains. ONTOSPREAD implements the double process of activation and spreading of concepts in ontologies applying different restrictions of the original model like weight degradation according to the distance or others coming from the extension of this technique like the converging paths reward. It is considered to be relevant to support the recommendation of concepts for tagging clinical records and to provide a tool for decision-support in clinical diagnosis. Finally an evaluation methodology and two examples using the well-known ontologies Galen and SNOMED CT are presented to validate the goodness, the improvement and the capabilities of this technique applied to medical systems.

Query expansion methods and performance evaluation for reusing linking open data of the European public procurement notices

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).

Towards a Stepwise Method for Unifying and Reconciling Corporate Names in Public Contracts Metadata: The CORFU Technique

The present paper introduces a technique to deal with coporate names heterogeneities in the context of public procurement metadata. Public bodies are currently facing a big challenge trying to improve both the performance and the transparency of administrative processes. The e-Government and Open Linked Data initiatives have emerged as efforts to tackle existing interoperability and integration issues among ICT-based systems but the creation of a real transparent environment requires much more than the simple publication of data and information in specific open formats; data and information quality is the next major step in the pubic sector. More specifically in the e-Procurement domain there is a vast amount of valuable metadata that is already available via the Internet protocols and formats and can be used for the creation of new added-value services. Nevertheless the simple extraction of statistics or creation of reports can imply extra tasks with regards to clean, prepare and reconcile data. On the other hand, transparency has become a major objective in public administractions and, in the case of public procurement, one of the most interesting services lies in tracking rewarded contracts (mainly type, location, and supplier). Although it seems a basic kind of reporting service the truth is that its generation can turn into a complex task due to a lack of standardization in supplier names or the use of different descriptors for the type of contract. In this paper, a stepwise method based on natural language processing and semantics to address the unfication of corporate names is defined and implemented. Moreover a research study to evaluate the precision and recall of the proposed technique, using as use case the public dataset of rewarded public contracts in Australia during the period 2004-2012, is also presented. Finally some discussion, conclusions and future work are also outlined.

Towards the evaluation of usability in educative websites

This article presents a methodological approach of evaluation of the usability in educational environments ME-UsitE, an analysis of the necessity to implement a tool of evaluation and measurement of the usability to support the methodology, that contributes to the detection of problems and failures of usability from the perspective of the user of educative websites in a real work environment. Taking into account that the websites are designed and implemented for a domain and audience in particular, it is important to consider factors like these for the implementation of a tool that allows not only to carry out the evaluation process but also to support the analysis of the gathered data. Thus, the obtained results may allow us to establish policies for the improvement of the usability of the site, when providing a score that determines whether the level of usability offered covers the requirements for the proposed audience and the profile of the participant user in the evaluation.

The effects of ERP implementations on the profitability of big firms: the case of Spain

This paper aims to analyse the incidence of the implementation of ERP systems on the profitability of the biggest Spanish firms. To reach this objective, we sent a questionnaire to a sample of companies. We also gathered the financial statements of these companies. The main results indicate a significant decrement in the profitability of the companies that implemented an ERP system. The reason for this decrement was a reduction in the profit margin, which was caused by an increase in the operational expenses. The results also suggest that adopting firms manipulated earnings to hide the deterioration of their performance. The profitability of non-ERP firms remained unchanged, so the productivity paradox assumption is not supported.

Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.