PPROC, an ontology for transparency in'public procurement

Public procurement or tendering refers to the process followed by public authorities for the procurement of goods and services. In most developed countries, the law requires public authorities to provide online information to ensure competitive tendering as far as possible, for which the adequate announcement of tenders is an essential requirement. In addition, transparency laws being proposed in such countries are making the monitoring of public contracts by citizens a fundamental right. This paper describes the PPROC ontology, which has been developed to give support to both processes, publication and accountability, by semantically describing public procurement processes and contracts. The PPROC ontology is extensive, since it covers not only the usual data about the tender, its objectives, deadlines and awardees, but also details of the whole process, from the initial contract publication to its termination. This makes it possible to use the ontology for both open data publication purposes and for the overall management of the public contract procurement process

Data Quality Barriers for Transparency in Public Procurement

Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics.publishedVersio

Identificación de elementos en curación de datos para la gestión de patentes colombianas en química de alimentos

Las patentes son uno de los documentos que indican y fomentan el desarrollo tecnológico de un país y trabajo investigativo. Estos documentos son especiales porque manejan información primaria, tienen diferentes autores, tienen un ciclo de vida complejo y derecho de explotación por un tiempo determinado. Estos documentos se pueden consultar en bases de datos que poseen controles y estándares propios o exigidos por la OMPI. En Colombia la SIC tramita y gestiona dichos documentos. La curación de datos es un proceso transversal de la curación digital, que se entiende como todos los procesos que controlan o crean los datos para que tengan un ciclo de vida satisfactorio y cumplan las funciones para las cuales fueron creados. Por consiguiente al tener una curación de datos satisfactoria se intuye que los documentos pueden tener una mayor facilidad de ser gestionados en bases de datos. El objetivo de ésta investigación es identificar cuáles son los elementos en curación de datos necesarios para mejorar la gestión de patentes colombianas en química de alimentos. Las patentes seleccionadas fueron patentes colombianas en el periodo 2002-2012. El trabajo utilizó la base de datos Espacenet por su organización estandarizada de las patentes y su facilidad a la hora de utilizar controles en la muestra. Como resultado de la investigación se encontró que las patentes colombianas no tienen un control sobre la cantidad y tipo de descriptores temáticos por patente y la normalización de los datos que relacionan unas patentes con otras.Patents are used often to both indicate and support the investigative work and technology development of a Country. These are special documents for not only do they contain primary information, they also have different authors, legal claims, complex life cycle and rights of exploitation. Patents are consulted in internet databases, which are often subjected to OMPI standards. The SIC manages said documents in Colombia. Data Curation is a process in Digital Curation. This process controls and creates data so that it can accomplish the process they were created for. Thusly it is inferred that with a competent data curation patents could be managed easily in databases. The objective of this investigation is to identify the elements in Data Curation that allows a better management of Colombian patents. This investigation used the Espacenet database for its simplicity in search engine and high standards and control. It also used the Colombian patents in Food Chemistry during 2002 to 2012 for analysis. As a result, it was found that Colombian patents did not ha a control about the quantity and type of subject descriptors for a patent and a normalization of the data that relates one patent to another.Profesional en Ciencia de la Información - Bibliotecólogo (a)Pregrad

Semantic and Syntactic Matching of Heterogeneous e-Catalogues

In e-procurement, companies use e-catalogues to exchange product infor-mation with business partners. Matching e-catalogues with product requests helps the suppliers to identify the best business opportunities in B2B e-Marketplaces. But various ways to specify products and the large variety of e-catalogue formats used by different business actors makes it difficult. This Ph.D. thesis aims to discover potential syntactic and semantic rela-tionships among product data in procurement documents and exploit it to find similar e-catalogues. Using a Concept-based Vector Space Model, product data and its semantic interpretation is used to find the correlation of product data. In order to identify important terms in procurement documents, standard e-catalogues and e-tenders are used as a resource to train a Product Named Entity Recognizer to find B2B product mentions in e-catalogues. The proposed approach makes it possible to use the benefits of all availa-ble semantic resources and schemas but not to be dependent on any specific as-sumption. The solution can serve as a B2B product search system in e-Procurement platforms and e-Marketplaces