1 Public Procurement Domain
2 Public Contracts Ontology
2.1 Ontologies Reused by the PCO
-
GoodRelations Ontology5 (
gr
prefix) – to model organizations and price specifications -
VCard Ontology6 (
vcard
prefix) – to express contact information -
Payments Ontology7 (
payment
prefix) – to express subsidies -
Dublin Core8 (
dcterms
prefix) – to express descriptive metadata (e.g., title, description) -
Simple Knowledge Organization System (SKOS)9 (
skos
prefix) – to express code lists and classifications -
Friend-of-a-friend Ontology (FOAF)10 (
foaf
prefix) – to express agents, especially persons and relationships between them -
schema.org11 (
s
prefix) – to express locations and other generic kinds of entities -
Asset Description Metadata Schema (ADMS)12 (
adms
prefix) – to express identifiers.
2.2 Core Concepts of the PCO
pc:Contract
. We understand a public contract as a single object that groups pieces of information related to the contract. These pieces of information gradually arise during the public procurement process. They are published by contracting authorities on various public procurement portals in the form of different kinds of notification documents, e.g., call for tenders (sometimes also called contract notice), contract award notice, contract cancellation notice, or the like. Another important concept of the ontology are business entities, i.e., in this context, contracting authorities and suppliers. Business entities are not represented via a new class in the ontology; we rather reuse the class gr:BusinessEntity
from the GoodRelations ontology.
2.2.1 Tendering Phase Modeling
pc:Contract
. A lot is associated with its superior contract via the property pc:lot
.gr:Offering
to represent items. Basically, the items are characterized by their name, description and price, but other kinds of characteristics can be used as well, which we however do not cover in the domain model (e.g., references to various product or service classification schemes). Second, it specifies a combination of award criteria. The class pc:AwardCriteriaCombination
is the representation of this feature in the ontology. For each award criterion, expressed in the ontology using class pc:WeightedCriterion
, a textual description (or, we can say, name) and weight percentage of the criterion is specified. Usually, a specific combination is distinguished. For instance, it may specify that tenders are only compared on the basis of the price offered and that the tender offering the lowest price has to be selected.pc:Tender
. Then, according to the award criteria, the authority selects and awards the best tender. In this stage, the authority publishes the date of the award and marks the selected tender as the awarded tender.2.2.2 Pre-realization, Realization and Evaluation Phase Modeling
foaf:Document
to represent unstructured textual documents. We only consider one particular structured information published – the price agreed by both the authority and supplier. The agreed price should be the same as the price offered in the awarded tender but it can differ in some specific cases.3 Procurement Data Extraction and Pre-processing
3.1 Data Extraction from HTML
div
elements combined with additional textual information. Section 1 of the document contains combined information about the lot ID and the lot name, so it is necessary to split these properties. Section 2 only contains one property, with a textual label that has to be removed. In the Sects. 3 and 4 the fields are separated by br
tags combined with additional labels.input
elements with unique id
attributes (see the right side of Fig. 2), which allows to access the data fields without any additional transformation.# | PCO property | # | PCO property |
---|---|---|---|
1 | dc:title + adms:identifier
| 3 | pc:supplier
|
2 | pc:numberOfTenders
| 4 | pc:offeredPrice
|
3.2 Data Extraction from Structured Formats
3.2.1 TED Data
3.3 Czech Data
3.3.1 Polish Data
wykonawca_0
, wykonawca_1
, wykonawca_2
and so on. We also had to write our own extension functions for Tripliser allowing us to generate new identifiers for addresses, as data structures, from their parts: locality, postal code and street.3.3.2 U.S. Data
4 LOD-Enabled Public Contract Matchmaking
4.1 Public Contracts Filing Application
4.1.1 Buyer’s and Supplier’s View
4.1.2 Application Architecture
4.2 Matchmaking Functionality Internals
5 Aggregated Analysis of Procurement Linked Data
5.1 Analysis Scenarios
-
Journalists and NGOs: the data may help them reveal corruption and clientelism in public sector.
-
Official government bodies: both specific supervisory bodies that address the issues of transparency and fair competition and statistical offices that collect data as part of aggregated information on the national economy.
-
Bidders: analysing the previous successful and unsuccessful tenders may be helpful when preparing a new one; in long term, the companies may also actively plan their bidding strategies based on procurement market trends (revealed by automated analysis).
-
Contracting authorities: they want to understand the supply side in order to know how to formulate the contract conditions, in view of successful matchmaking. Good progress of a future contract may be derived from previous experience with certain bidders. An additional goal may be to attract an adequate number of bidders; excessively many bidders bring large overheads to the awarding process, while too low a number may reduce competition (and, under some circumstances, even lead to contract canceling by a supervisory body, due to an anti-monopoly action).
5.2 Analytical Methods
mainObject
attribute), originating from one of the core procurement dataset, and the population density attribute, originating from DBpedia. It indicates that contracts for ‘Research and Development in the Physical, Engineering, and Life Sciences’ in localities with higher population density tend to attract a high number of tenders (as higher interval values for the former mostly coincide with higher values for the latter, in the individual rules).5.3 Integration of Analytical Functionality into PCFA
-
Interactively exploring, in graphical form, the linked data about
-
the current notice
-
a (matching) historical notice/contract
-
a relevant supplier, including its contracts.
-
-
Viewing suggested values for the remaining pieces of contract notice information based on the already provided ones. The values will be provided by an inductively trained recommender.
-
Getting an estimate of the number of bidders for (as complete as possible) contract notice information. For this, a predictive ordinal classifier will be developed.